llama : move end-user examples to tools directory (#13249)

* llama : move end-user examples to tools directory --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
2025-05-02 20:27:13 +02:00 · 2025-05-02 20:27:13 +02:00 · 1d36b3670b
commit 1d36b3670b
parent b34443923c
213 changed files with 226 additions and 190 deletions
--- a/tools/server/tests/.gitignore
+++ b/tools/server/tests/.gitignore
@ -0,0 +1,2 @@
+.venv
+tmp
--- a/tools/server/tests/README.md
+++ b/tools/server/tests/README.md
@ -0,0 +1,66 @@
+# Server tests
+
+Python based server tests scenario using [pytest](https://docs.pytest.org/en/stable/).
+
+Tests target GitHub workflows job runners with 4 vCPU.
+
+Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail.
+To mitigate it, you can increase values in `n_predict`, `kv_size`.
+
+### Install dependencies
+
+`pip install -r requirements.txt`
+
+### Run tests
+
+1. Build the server
+
+```shell
+cd ../../..
+cmake -B build
+cmake --build build --target llama-server
+```
+
+2. Start the test: `./tests.sh`
+
+It's possible to override some scenario steps values with environment variables:
+
+| variable                 | description                                                                                    |
+|--------------------------|------------------------------------------------------------------------------------------------|
+| `PORT`                   | `context.server_port` to set the listening port of the server during scenario, default: `8080` |
+| `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/llama-server`                         |
+| `DEBUG`                  | to enable steps and server verbose mode `--verbose`                                       |
+| `N_GPU_LAYERS`           | number of model layers to offload to VRAM `-ngl --n-gpu-layers`                                |
+| `LLAMA_CACHE`            | by default server tests re-download models to the `tmp` subfolder. Set this to your cache (e.g. `$HOME/Library/Caches/llama.cpp` on Mac or `$HOME/.cache/llama.cpp` on Unix) to avoid this |
+
+To run slow tests (will download many models, make sure to set `LLAMA_CACHE` if needed):
+
+```shell
+SLOW_TESTS=1 ./tests.sh
+```
+
+To run with stdout/stderr display in real time (verbose output, but useful for debugging):
+
+```shell
+DEBUG=1 ./tests.sh -s -v -x
+```
+
+To run all the tests in a file:
+
+```shell
+./tests.sh unit/test_chat_completion.py -v -x
+```
+
+To run a single test:
+
+```shell
+./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req
+```
+
+Hint: You can compile and run test in single command, useful for local developement:
+
+```shell
+cmake --build build -j --target llama-server && ./tools/server/tests/tests.sh
+```
+
+To see all available arguments, please refer to [pytest documentation](https://docs.pytest.org/en/stable/how-to/usage.html)
--- a/tools/server/tests/conftest.py
+++ b/tools/server/tests/conftest.py
@ -0,0 +1,15 @@
+import pytest
+from utils import *
+
+
+# ref: https://stackoverflow.com/questions/22627659/run-code-before-and-after-each-test-in-py-test
+@pytest.fixture(autouse=True)
+def stop_server_after_each_test():
+    # do nothing before each test
+    yield
+    # stop all servers after each test
+    instances = set(
+        server_instances
+    )  # copy the set to prevent 'Set changed size during iteration'
+    for server in instances:
+        server.stop()
--- a/tools/server/tests/pytest.ini
+++ b/tools/server/tests/pytest.ini
@ -0,0 +1,4 @@
+[pytest]
+markers =
+    slow: marks tests as slow (deselect with '-m "not slow"')
+    serial
--- a/tools/server/tests/requirements.txt
+++ b/tools/server/tests/requirements.txt
@ -0,0 +1,8 @@
+aiohttp~=3.9.3
+pytest~=8.3.3
+huggingface_hub~=0.23.2
+numpy~=1.26.4
+openai~=1.55.3
+prometheus-client~=0.20.0
+requests~=2.32.3
+wget~=3.2
--- a/tools/server/tests/tests.sh
+++ b/tools/server/tests/tests.sh
@ -0,0 +1,23 @@
+#!/bin/bash
+
+# make sure we are in the right directory
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+cd $SCRIPT_DIR
+
+set -eu
+
+if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
+    # Slow tests for tool calls need quite a few models ahead of time to avoid timing out.
+    python $SCRIPT_DIR/../../../scripts/fetch_server_test_models.py
+fi
+
+if [ $# -lt 1 ]
+then
+    if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
+        pytest -v -x
+    else
+        pytest -v -x -m "not slow"
+    fi
+else
+    pytest "$@"
+fi
--- a/tools/server/tests/unit/test_basic.py
+++ b/tools/server/tests/unit/test_basic.py
@ -0,0 +1,96 @@
+import pytest
+import requests
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+
+
+def test_server_start_simple():
+    global server
+    server.start()
+    res = server.make_request("GET", "/health")
+    assert res.status_code == 200
+
+
+def test_server_props():
+    global server
+    server.start()
+    res = server.make_request("GET", "/props")
+    assert res.status_code == 200
+    assert ".gguf" in res.body["model_path"]
+    assert res.body["total_slots"] == server.n_slots
+    default_val = res.body["default_generation_settings"]
+    assert server.n_ctx is not None and server.n_slots is not None
+    assert default_val["n_ctx"] == server.n_ctx / server.n_slots
+    assert default_val["params"]["seed"] == server.seed
+
+
+def test_server_models():
+    global server
+    server.start()
+    res = server.make_request("GET", "/models")
+    assert res.status_code == 200
+    assert len(res.body["data"]) == 1
+    assert res.body["data"][0]["id"] == server.model_alias
+
+
+def test_server_slots():
+    global server
+
+    # without slots endpoint enabled, this should return error
+    server.server_slots = False
+    server.start()
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 501 # ERROR_TYPE_NOT_SUPPORTED
+    assert "error" in res.body
+    server.stop()
+
+    # with slots endpoint enabled, this should return slots info
+    server.server_slots = True
+    server.n_slots = 2
+    server.start()
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 200
+    assert len(res.body) == server.n_slots
+    assert server.n_ctx is not None and server.n_slots is not None
+    assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots
+    assert "params" in res.body[0]
+    assert res.body[0]["params"]["seed"] == server.seed
+
+
+def test_load_split_model():
+    global server
+    server.model_hf_repo = "ggml-org/models"
+    server.model_hf_file = "tinyllamas/split/stories15M-q8_0-00001-of-00003.gguf"
+    server.model_alias = "tinyllama-split"
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "n_predict": 16,
+        "prompt": "Hello",
+        "temperature": 0.0,
+    })
+    assert res.status_code == 200
+    assert match_regex("(little|girl)+", res.body["content"])
+
+
+def test_no_webui():
+    global server
+    # default: webui enabled
+    server.start()
+    url = f"http://{server.server_host}:{server.server_port}"
+    res = requests.get(url)
+    assert res.status_code == 200
+    assert "<html>" in res.text
+    server.stop()
+
+    # with --no-webui
+    server.no_webui = True
+    server.start()
+    res = requests.get(url)
+    assert res.status_code == 404
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@ -0,0 +1,311 @@
+import pytest
+from openai import OpenAI
+from utils import *
+
+server: ServerProcess
+
+@pytest.fixture(autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+
+
+@pytest.mark.parametrize(
+    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
+    [
+        (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", False, None),
+        (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", True, None),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True,  None),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'),
+        (None, "Book", "What is the best book", 8, "^ blue",                    23, 8, "length", True, "This is not a chat template, it is"),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
+        (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", False, None),
+        (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", True, None),
+    ]
+)
+def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template):
+    global server
+    server.jinja = jinja
+    server.chat_template = chat_template
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "model": model,
+        "max_tokens": max_tokens,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+    })
+    assert res.status_code == 200
+    assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
+    assert res.body["system_fingerprint"].startswith("b")
+    assert res.body["model"] == model if model is not None else server.model_alias
+    assert res.body["usage"]["prompt_tokens"] == n_prompt
+    assert res.body["usage"]["completion_tokens"] == n_predicted
+    choice = res.body["choices"][0]
+    assert "assistant" == choice["message"]["role"]
+    assert match_regex(re_content, choice["message"]["content"]), f'Expected {re_content}, got {choice["message"]["content"]}'
+    assert choice["finish_reason"] == finish_reason
+
+
+@pytest.mark.parametrize(
+    "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
+    [
+        ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
+        ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
+    ]
+)
+def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
+    global server
+    server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL
+    server.start()
+    res = server.make_stream_request("POST", "/chat/completions", data={
+        "max_tokens": max_tokens,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ],
+        "stream": True,
+    })
+    content = ""
+    last_cmpl_id = None
+    for data in res:
+        choice = data["choices"][0]
+        assert data["system_fingerprint"].startswith("b")
+        assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
+        if last_cmpl_id is None:
+            last_cmpl_id = data["id"]
+        assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
+        if choice["finish_reason"] in ["stop", "length"]:
+            assert data["usage"]["prompt_tokens"] == n_prompt
+            assert data["usage"]["completion_tokens"] == n_predicted
+            assert "content" not in choice["delta"]
+            assert match_regex(re_content, content)
+            assert choice["finish_reason"] == finish_reason
+        else:
+            assert choice["finish_reason"] is None
+            content += choice["delta"]["content"]
+
+
+def test_chat_completion_with_openai_library():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo-instruct",
+        messages=[
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ],
+        max_tokens=8,
+        seed=42,
+        temperature=0.8,
+    )
+    assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b")
+    assert res.choices[0].finish_reason == "length"
+    assert res.choices[0].message.content is not None
+    assert match_regex("(Suddenly)+", res.choices[0].message.content)
+
+
+def test_chat_template():
+    global server
+    server.chat_template = "llama3"
+    server.debug = True  # to get the "__verbose" object in the response
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 8,
+        "messages": [
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ]
+    })
+    assert res.status_code == 200
+    assert "__verbose" in res.body
+    assert res.body["__verbose"]["prompt"] == "<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+
+
+def test_apply_chat_template():
+    global server
+    server.chat_template = "command-r"
+    server.start()
+    res = server.make_request("POST", "/apply-template", data={
+        "messages": [
+            {"role": "system", "content": "You are a test."},
+            {"role": "user", "content":"Hi there"},
+        ]
+    })
+    assert res.status_code == 200
+    assert "prompt" in res.body
+    assert res.body["prompt"] == "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a test.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+
+
+@pytest.mark.parametrize("response_format,n_predicted,re_content", [
+    ({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""),
+    ({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"),
+    ({"type": "json_schema", "json_schema": {"schema": {"const": "foooooo"}}}, 10, "\"foooooo\""),
+    ({"type": "json_object"}, 10, "(\\{|John)+"),
+    ({"type": "sound"}, 0, None),
+    # invalid response format (expected to fail)
+    ({"type": "json_object", "schema": 123}, 0, None),
+    ({"type": "json_object", "schema": {"type": 123}}, 0, None),
+    ({"type": "json_object", "schema": {"type": "hiccup"}}, 0, None),
+])
+def test_completion_with_response_format(response_format: dict, n_predicted: int, re_content: str | None):
+    global server
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predicted,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "response_format": response_format,
+    })
+    if re_content is not None:
+        assert res.status_code == 200
+        choice = res.body["choices"][0]
+        assert match_regex(re_content, choice["message"]["content"])
+    else:
+        assert res.status_code != 200
+        assert "error" in res.body
+
+
+@pytest.mark.parametrize("jinja,json_schema,n_predicted,re_content", [
+    (False, {"const": "42"}, 6, "\"42\""),
+    (True, {"const": "42"}, 6, "\"42\""),
+])
+def test_completion_with_json_schema(jinja: bool, json_schema: dict, n_predicted: int, re_content: str):
+    global server
+    server.jinja = jinja
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predicted,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "json_schema": json_schema,
+    })
+    assert res.status_code == 200, f'Expected 200, got {res.status_code}'
+    choice = res.body["choices"][0]
+    assert match_regex(re_content, choice["message"]["content"]), f'Expected {re_content}, got {choice["message"]["content"]}'
+
+
+@pytest.mark.parametrize("jinja,grammar,n_predicted,re_content", [
+    (False, 'root ::= "a"{5,5}', 6, "a{5,5}"),
+    (True, 'root ::= "a"{5,5}', 6, "a{5,5}"),
+])
+def test_completion_with_grammar(jinja: bool, grammar: str, n_predicted: int, re_content: str):
+    global server
+    server.jinja = jinja
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predicted,
+        "messages": [
+            {"role": "user", "content": "Does not matter what I say, does it?"},
+        ],
+        "grammar": grammar,
+    })
+    assert res.status_code == 200, res.body
+    choice = res.body["choices"][0]
+    assert match_regex(re_content, choice["message"]["content"]), choice["message"]["content"]
+
+
+@pytest.mark.parametrize("messages", [
+    None,
+    "string",
+    [123],
+    [{}],
+    [{"role": 123}],
+    [{"role": "system", "content": 123}],
+    # [{"content": "hello"}], # TODO: should not be a valid case
+    [{"role": "system", "content": "test"}, {}],
+])
+def test_invalid_chat_completion_req(messages):
+    global server
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "messages": messages,
+    })
+    assert res.status_code == 400 or res.status_code == 500
+    assert "error" in res.body
+
+
+def test_chat_completion_with_timings_per_token():
+    global server
+    server.start()
+    res = server.make_stream_request("POST", "/chat/completions", data={
+        "max_tokens": 10,
+        "messages": [{"role": "user", "content": "test"}],
+        "stream": True,
+        "timings_per_token": True,
+    })
+    for data in res:
+        assert "timings" in data
+        assert "prompt_per_second" in data["timings"]
+        assert "predicted_per_second" in data["timings"]
+        assert "predicted_n" in data["timings"]
+        assert data["timings"]["predicted_n"] <= 10
+
+
+def test_logprobs():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo-instruct",
+        temperature=0.0,
+        messages=[
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ],
+        max_tokens=5,
+        logprobs=True,
+        top_logprobs=10,
+    )
+    output_text = res.choices[0].message.content
+    aggregated_text = ''
+    assert res.choices[0].logprobs is not None
+    assert res.choices[0].logprobs.content is not None
+    for token in res.choices[0].logprobs.content:
+        aggregated_text += token.token
+        assert token.logprob <= 0.0
+        assert token.bytes is not None
+        assert len(token.top_logprobs) > 0
+    assert aggregated_text == output_text
+
+
+def test_logprobs_stream():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo-instruct",
+        temperature=0.0,
+        messages=[
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ],
+        max_tokens=5,
+        logprobs=True,
+        top_logprobs=10,
+        stream=True,
+    )
+    output_text = ''
+    aggregated_text = ''
+    for data in res:
+        choice = data.choices[0]
+        if choice.finish_reason is None:
+            if choice.delta.content:
+                output_text += choice.delta.content
+            assert choice.logprobs is not None
+            assert choice.logprobs.content is not None
+            for token in choice.logprobs.content:
+                aggregated_text += token.token
+                assert token.logprob <= 0.0
+                assert token.bytes is not None
+                assert token.top_logprobs is not None
+                assert len(token.top_logprobs) > 0
+    assert aggregated_text == output_text
--- a/tools/server/tests/unit/test_completion.py
+++ b/tools/server/tests/unit/test_completion.py
@ -0,0 +1,428 @@
+import pytest
+import requests
+import time
+from openai import OpenAI
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+
+@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [
+    ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False),
+    ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True),
+])
+def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool):
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "n_predict": n_predict,
+        "prompt": prompt,
+        "return_tokens": return_tokens,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["prompt_n"] == n_prompt
+    assert res.body["timings"]["predicted_n"] == n_predicted
+    assert res.body["truncated"] == truncated
+    assert type(res.body["has_new_line"]) == bool
+    assert match_regex(re_content, res.body["content"])
+    if return_tokens:
+        assert len(res.body["tokens"]) > 0
+        assert all(type(tok) == int for tok in res.body["tokens"])
+    else:
+        assert res.body["tokens"] == []
+
+
+@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [
+    ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False),
+    ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False),
+])
+def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool):
+    global server
+    server.start()
+    res = server.make_stream_request("POST", "/completion", data={
+        "n_predict": n_predict,
+        "prompt": prompt,
+        "stream": True,
+    })
+    content = ""
+    for data in res:
+        assert "stop" in data and type(data["stop"]) == bool
+        if data["stop"]:
+            assert data["timings"]["prompt_n"] == n_prompt
+            assert data["timings"]["predicted_n"] == n_predicted
+            assert data["truncated"] == truncated
+            assert data["stop_type"] == "limit"
+            assert type(data["has_new_line"]) == bool
+            assert "generation_settings" in data
+            assert server.n_predict is not None
+            assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict)
+            assert data["generation_settings"]["seed"] == server.seed
+            assert match_regex(re_content, content)
+        else:
+            assert len(data["tokens"]) > 0
+            assert all(type(tok) == int for tok in data["tokens"])
+            content += data["content"]
+
+
+def test_completion_stream_vs_non_stream():
+    global server
+    server.start()
+    res_stream = server.make_stream_request("POST", "/completion", data={
+        "n_predict": 8,
+        "prompt": "I believe the meaning of life is",
+        "stream": True,
+    })
+    res_non_stream = server.make_request("POST", "/completion", data={
+        "n_predict": 8,
+        "prompt": "I believe the meaning of life is",
+    })
+    content_stream = ""
+    for data in res_stream:
+        content_stream += data["content"]
+    assert content_stream == res_non_stream.body["content"]
+
+
+def test_completion_with_openai_library():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.completions.create(
+        model="davinci-002",
+        prompt="I believe the meaning of life is",
+        max_tokens=8,
+    )
+    assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b")
+    assert res.choices[0].finish_reason == "length"
+    assert res.choices[0].text is not None
+    assert match_regex("(going|bed)+", res.choices[0].text)
+
+
+def test_completion_stream_with_openai_library():
+    global server
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.completions.create(
+        model="davinci-002",
+        prompt="I believe the meaning of life is",
+        max_tokens=8,
+        stream=True,
+    )
+    output_text = ''
+    for data in res:
+        choice = data.choices[0]
+        if choice.finish_reason is None:
+            assert choice.text is not None
+            output_text += choice.text
+    assert match_regex("(going|bed)+", output_text)
+
+
+@pytest.mark.parametrize("n_slots", [1, 2])
+def test_consistent_result_same_seed(n_slots: int):
+    global server
+    server.n_slots = n_slots
+    server.start()
+    last_res = None
+    for _ in range(4):
+        res = server.make_request("POST", "/completion", data={
+            "prompt": "I believe the meaning of life is",
+            "seed": 42,
+            "temperature": 0.0,
+            "cache_prompt": False,  # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+        if last_res is not None:
+            assert res.body["content"] == last_res.body["content"]
+        last_res = res
+
+
+@pytest.mark.parametrize("n_slots", [1, 2])
+def test_different_result_different_seed(n_slots: int):
+    global server
+    server.n_slots = n_slots
+    server.start()
+    last_res = None
+    for seed in range(4):
+        res = server.make_request("POST", "/completion", data={
+            "prompt": "I believe the meaning of life is",
+            "seed": seed,
+            "temperature": 1.0,
+            "cache_prompt": False,  # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+        if last_res is not None:
+            assert res.body["content"] != last_res.body["content"]
+        last_res = res
+
+# TODO figure why it don't work with temperature = 1
+# @pytest.mark.parametrize("temperature", [0.0, 1.0])
+@pytest.mark.parametrize("n_batch", [16, 32])
+@pytest.mark.parametrize("temperature", [0.0])
+def test_consistent_result_different_batch_size(n_batch: int, temperature: float):
+    global server
+    server.n_batch = n_batch
+    server.start()
+    last_res = None
+    for _ in range(4):
+        res = server.make_request("POST", "/completion", data={
+            "prompt": "I believe the meaning of life is",
+            "seed": 42,
+            "temperature": temperature,
+            "cache_prompt": False,  # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+        if last_res is not None:
+            assert res.body["content"] == last_res.body["content"]
+        last_res = res
+
+
+@pytest.mark.skip(reason="This test fails on linux, need to be fixed")
+def test_cache_vs_nocache_prompt():
+    global server
+    server.start()
+    res_cache = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "seed": 42,
+        "temperature": 1.0,
+        "cache_prompt": True,
+    })
+    res_no_cache = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "seed": 42,
+        "temperature": 1.0,
+        "cache_prompt": False,
+    })
+    assert res_cache.body["content"] == res_no_cache.body["content"]
+
+
+def test_completion_with_tokens_input():
+    global server
+    server.temperature = 0.0
+    server.start()
+    prompt_str = "I believe the meaning of life is"
+    res = server.make_request("POST", "/tokenize", data={
+        "content": prompt_str,
+        "add_special": True,
+    })
+    assert res.status_code == 200
+    tokens = res.body["tokens"]
+
+    # single completion
+    res = server.make_request("POST", "/completion", data={
+        "prompt": tokens,
+    })
+    assert res.status_code == 200
+    assert type(res.body["content"]) == str
+
+    # batch completion
+    res = server.make_request("POST", "/completion", data={
+        "prompt": [tokens, tokens],
+    })
+    assert res.status_code == 200
+    assert type(res.body) == list
+    assert len(res.body) == 2
+    assert res.body[0]["content"] == res.body[1]["content"]
+
+    # mixed string and tokens
+    res = server.make_request("POST", "/completion", data={
+        "prompt": [tokens, prompt_str],
+    })
+    assert res.status_code == 200
+    assert type(res.body) == list
+    assert len(res.body) == 2
+    assert res.body[0]["content"] == res.body[1]["content"]
+
+    # mixed string and tokens in one sequence
+    res = server.make_request("POST", "/completion", data={
+        "prompt": [1, 2, 3, 4, 5, 6, prompt_str, 7, 8, 9, 10, prompt_str],
+    })
+    assert res.status_code == 200
+    assert type(res.body["content"]) == str
+
+
+@pytest.mark.parametrize("n_slots,n_requests", [
+    (1, 3),
+    (2, 2),
+    (2, 4),
+    (4, 2), # some slots must be idle
+    (4, 6),
+])
+def test_completion_parallel_slots(n_slots: int, n_requests: int):
+    global server
+    server.n_slots = n_slots
+    server.temperature = 0.0
+    server.start()
+
+    PROMPTS = [
+        ("Write a very long book.", "(very|special|big)+"),
+        ("Write another a poem.", "(small|house)+"),
+        ("What is LLM?", "(Dad|said)+"),
+        ("The sky is blue and I love it.", "(climb|leaf)+"),
+        ("Write another very long music lyrics.", "(friends|step|sky)+"),
+        ("Write a very long joke.", "(cat|Whiskers)+"),
+    ]
+    def check_slots_status():
+        should_all_slots_busy = n_requests >= n_slots
+        time.sleep(0.1)
+        res = server.make_request("GET", "/slots")
+        n_busy = sum([1 for slot in res.body if slot["is_processing"]])
+        if should_all_slots_busy:
+            assert n_busy == n_slots
+        else:
+            assert n_busy <= n_slots
+
+    tasks = []
+    for i in range(n_requests):
+        prompt, re_content = PROMPTS[i % len(PROMPTS)]
+        tasks.append((server.make_request, ("POST", "/completion", {
+            "prompt": prompt,
+            "seed": 42,
+            "temperature": 1.0,
+        })))
+    tasks.append((check_slots_status, ()))
+    results = parallel_function_calls(tasks)
+
+    # check results
+    for i in range(n_requests):
+        prompt, re_content = PROMPTS[i % len(PROMPTS)]
+        res = results[i]
+        assert res.status_code == 200
+        assert type(res.body["content"]) == str
+        assert len(res.body["content"]) > 10
+        # FIXME: the result is not deterministic when using other slot than slot 0
+        # assert match_regex(re_content, res.body["content"])
+
+
+@pytest.mark.parametrize(
+    "prompt,n_predict,response_fields",
+    [
+        ("I believe the meaning of life is", 8, []),
+        ("I believe the meaning of life is", 32, ["content", "generation_settings/n_predict", "prompt"]),
+    ],
+)
+def test_completion_response_fields(
+    prompt: str, n_predict: int, response_fields: list[str]
+):
+    global server
+    server.start()
+    res = server.make_request(
+        "POST",
+        "/completion",
+        data={
+            "n_predict": n_predict,
+            "prompt": prompt,
+            "response_fields": response_fields,
+        },
+    )
+    assert res.status_code == 200
+    assert "content" in res.body
+    assert len(res.body["content"])
+    if len(response_fields):
+        assert res.body["generation_settings/n_predict"] == n_predict
+        assert res.body["prompt"] == "<s> " + prompt
+        assert isinstance(res.body["content"], str)
+        assert len(res.body) == len(response_fields)
+    else:
+        assert len(res.body)
+        assert "generation_settings" in res.body
+
+
+def test_n_probs():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+    })
+    assert res.status_code == 200
+    assert "completion_probabilities" in res.body
+    assert len(res.body["completion_probabilities"]) == 5
+    for tok in res.body["completion_probabilities"]:
+        assert "id" in tok and tok["id"] > 0
+        assert "token" in tok and type(tok["token"]) == str
+        assert "logprob" in tok and tok["logprob"] <= 0.0
+        assert "bytes" in tok and type(tok["bytes"]) == list
+        assert len(tok["top_logprobs"]) == 10
+        for prob in tok["top_logprobs"]:
+            assert "id" in prob and prob["id"] > 0
+            assert "token" in prob and type(prob["token"]) == str
+            assert "logprob" in prob and prob["logprob"] <= 0.0
+            assert "bytes" in prob and type(prob["bytes"]) == list
+
+
+def test_n_probs_stream():
+    global server
+    server.start()
+    res = server.make_stream_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+        "stream": True,
+    })
+    for data in res:
+        if data["stop"] == False:
+            assert "completion_probabilities" in data
+            assert len(data["completion_probabilities"]) == 1
+            for tok in data["completion_probabilities"]:
+                assert "id" in tok and tok["id"] > 0
+                assert "token" in tok and type(tok["token"]) == str
+                assert "logprob" in tok and tok["logprob"] <= 0.0
+                assert "bytes" in tok and type(tok["bytes"]) == list
+                assert len(tok["top_logprobs"]) == 10
+                for prob in tok["top_logprobs"]:
+                    assert "id" in prob and prob["id"] > 0
+                    assert "token" in prob and type(prob["token"]) == str
+                    assert "logprob" in prob and prob["logprob"] <= 0.0
+                    assert "bytes" in prob and type(prob["bytes"]) == list
+
+
+def test_n_probs_post_sampling():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+        "post_sampling_probs": True,
+    })
+    assert res.status_code == 200
+    assert "completion_probabilities" in res.body
+    assert len(res.body["completion_probabilities"]) == 5
+    for tok in res.body["completion_probabilities"]:
+        assert "id" in tok and tok["id"] > 0
+        assert "token" in tok and type(tok["token"]) == str
+        assert "prob" in tok and 0.0 < tok["prob"] <= 1.0
+        assert "bytes" in tok and type(tok["bytes"]) == list
+        assert len(tok["top_probs"]) == 10
+        for prob in tok["top_probs"]:
+            assert "id" in prob and prob["id"] > 0
+            assert "token" in prob and type(prob["token"]) == str
+            assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0
+            assert "bytes" in prob and type(prob["bytes"]) == list
+        # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs
+        assert any(prob["prob"] == 1.0 for prob in tok["top_probs"])
+
+
+def test_cancel_request():
+    global server
+    server.n_ctx = 4096
+    server.n_predict = -1
+    server.n_slots = 1
+    server.server_slots = True
+    server.start()
+    # send a request that will take a long time, but cancel it before it finishes
+    try:
+        server.make_request("POST", "/completion", data={
+            "prompt": "I believe the meaning of life is",
+        }, timeout=0.1)
+    except requests.exceptions.ReadTimeout:
+        pass # expected
+    # make sure the slot is free
+    time.sleep(1) # wait for HTTP_POLLING_SECONDS
+    res = server.make_request("GET", "/slots")
+    assert res.body[0]["is_processing"] == False
--- a/tools/server/tests/unit/test_ctx_shift.py
+++ b/tools/server/tests/unit/test_ctx_shift.py
@ -0,0 +1,67 @@
+import pytest
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+
+LONG_TEXT = """
+Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
+Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
+Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+""".strip()
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.n_ctx = 256
+    server.n_slots = 2
+
+
+def test_ctx_shift_enabled():
+    # the prompt is 301 tokens
+    # the slot context is 256/2 = 128 tokens
+    # the prompt is truncated to keep the last 109 tokens
+    # 64 tokens are generated thanks to shifting the context when it gets full
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "n_predict": 64,
+        "prompt": LONG_TEXT,
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["prompt_n"] == 109
+    assert res.body["timings"]["predicted_n"] == 64
+    assert res.body["truncated"] is True
+
+
+@pytest.mark.parametrize("n_predict,n_token_output,truncated", [
+    (64, 64, False),
+    (-1, 120, True),
+])
+def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool):
+    global server
+    server.disable_ctx_shift = True
+    server.n_predict = -1
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "n_predict": n_predict,
+        "prompt": "Hi how are you",
+    })
+    assert res.status_code == 200
+    assert res.body["timings"]["predicted_n"] == n_token_output
+    assert res.body["truncated"] == truncated
+
+
+def test_ctx_shift_disabled_long_prompt():
+    global server
+    server.disable_ctx_shift = True
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "n_predict": 64,
+        "prompt": LONG_TEXT,
+    })
+    assert res.status_code != 200
+    assert "error" in res.body
+    assert "exceeds the available context size" in res.body["error"]["message"]
--- a/tools/server/tests/unit/test_embedding.py
+++ b/tools/server/tests/unit/test_embedding.py
@ -0,0 +1,257 @@
+import base64
+import struct
+import pytest
+from openai import OpenAI
+from utils import *
+
+server = ServerPreset.bert_bge_small()
+
+EPSILON = 1e-3
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.bert_bge_small()
+
+
+def test_embedding_single():
+    global server
+    server.pooling = 'last'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": "I believe the meaning of life is",
+    })
+    assert res.status_code == 200
+    assert len(res.body['data']) == 1
+    assert 'embedding' in res.body['data'][0]
+    assert len(res.body['data'][0]['embedding']) > 1
+
+    # make sure embedding vector is normalized
+    assert abs(sum([x ** 2 for x in res.body['data'][0]['embedding']]) - 1) < EPSILON
+
+
+def test_embedding_multiple():
+    global server
+    server.pooling = 'last'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": [
+            "I believe the meaning of life is",
+            "Write a joke about AI from a very long prompt which will not be truncated",
+            "This is a test",
+            "This is another test",
+        ],
+    })
+    assert res.status_code == 200
+    assert len(res.body['data']) == 4
+    for d in res.body['data']:
+        assert 'embedding' in d
+        assert len(d['embedding']) > 1
+
+
+def test_embedding_multiple_with_fa():
+    server = ServerPreset.bert_bge_small_with_fa()
+    server.pooling = 'last'
+    server.start()
+    # one of these should trigger the FA branch (i.e. context size % 256 == 0)
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": [
+            "a "*253,
+            "b "*254,
+            "c "*255,
+            "d "*256,
+        ],
+    })
+    assert res.status_code == 200
+    assert len(res.body['data']) == 4
+    for d in res.body['data']:
+        assert 'embedding' in d
+        assert len(d['embedding']) > 1
+
+
+@pytest.mark.parametrize(
+    "input,is_multi_prompt",
+    [
+        # do not crash on empty input
+        ("", False),
+        # single prompt
+        ("string", False),
+        ([12, 34, 56], False),
+        ([12, 34, "string", 56, 78], False),
+        # multiple prompts
+        (["string1", "string2"], True),
+        (["string1", [12, 34, 56]], True),
+        ([[12, 34, 56], [12, 34, 56]], True),
+        ([[12, 34, 56], [12, "string", 34, 56]], True),
+    ]
+)
+def test_embedding_mixed_input(input, is_multi_prompt: bool):
+    global server
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={"input": input})
+    assert res.status_code == 200
+    data = res.body['data']
+    if is_multi_prompt:
+        assert len(data) == len(input)
+        for d in data:
+            assert 'embedding' in d
+            assert len(d['embedding']) > 1
+    else:
+        assert 'embedding' in data[0]
+        assert len(data[0]['embedding']) > 1
+
+
+def test_embedding_pooling_none():
+    global server
+    server.pooling = 'none'
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={
+        "input": "hello hello hello",
+    })
+    assert res.status_code == 200
+    assert 'embedding' in res.body[0]
+    assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special
+
+    # make sure embedding vector is not normalized
+    for x in res.body[0]['embedding']:
+        assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON
+
+
+def test_embedding_pooling_none_oai():
+    global server
+    server.pooling = 'none'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": "hello hello hello",
+    })
+
+    # /v1/embeddings does not support pooling type 'none'
+    assert res.status_code == 400
+    assert "error" in res.body
+
+
+def test_embedding_openai_library_single():
+    global server
+    server.pooling = 'last'
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is")
+    assert len(res.data) == 1
+    assert len(res.data[0].embedding) > 1
+
+
+def test_embedding_openai_library_multiple():
+    global server
+    server.pooling = 'last'
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.embeddings.create(model="text-embedding-3-small", input=[
+        "I believe the meaning of life is",
+        "Write a joke about AI from a very long prompt which will not be truncated",
+        "This is a test",
+        "This is another test",
+    ])
+    assert len(res.data) == 4
+    for d in res.data:
+        assert len(d.embedding) > 1
+
+
+def test_embedding_error_prompt_too_long():
+    global server
+    server.pooling = 'last'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": "This is a test " * 512,
+    })
+    assert res.status_code != 200
+    assert "too large" in res.body["error"]["message"]
+
+
+def test_same_prompt_give_same_result():
+    server.pooling = 'last'
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": [
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+        ],
+    })
+    assert res.status_code == 200
+    assert len(res.body['data']) == 5
+    for i in range(1, len(res.body['data'])):
+        v0 = res.body['data'][0]['embedding']
+        vi = res.body['data'][i]['embedding']
+        for x, y in zip(v0, vi):
+            assert abs(x - y) < EPSILON
+
+
+@pytest.mark.parametrize(
+    "content,n_tokens",
+    [
+        ("I believe the meaning of life is", 9),
+        ("This is a test", 6),
+    ]
+)
+def test_embedding_usage_single(content, n_tokens):
+    global server
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={"input": content})
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
+
+
+def test_embedding_usage_multiple():
+    global server
+    server.start()
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": [
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+        ],
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == 2 * 9
+
+
+def test_embedding_openai_library_base64():
+    server.start()
+    test_input = "Test base64 embedding output"
+
+    # get embedding in default format
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": test_input
+    })
+    assert res.status_code == 200
+    vec0 = res.body["data"][0]["embedding"]
+
+    # get embedding in base64 format
+    res = server.make_request("POST", "/v1/embeddings", data={
+        "input": test_input,
+        "encoding_format": "base64"
+    })
+
+    assert res.status_code == 200
+    assert "data" in res.body
+    assert len(res.body["data"]) == 1
+
+    embedding_data = res.body["data"][0]
+    assert "embedding" in embedding_data
+    assert isinstance(embedding_data["embedding"], str)
+
+    # Verify embedding is valid base64
+    decoded = base64.b64decode(embedding_data["embedding"])
+    # Verify decoded data can be converted back to float array
+    float_count = len(decoded) // 4  # 4 bytes per float
+    floats = struct.unpack(f'{float_count}f', decoded)
+    assert len(floats) > 0
+    assert all(isinstance(x, float) for x in floats)
+    assert len(floats) == len(vec0)
+
+    # make sure the decoded data is the same as the original
+    for x, y in zip(floats, vec0):
+        assert abs(x - y) < EPSILON
--- a/tools/server/tests/unit/test_infill.py
+++ b/tools/server/tests/unit/test_infill.py
@ -0,0 +1,77 @@
+import pytest
+from utils import *
+
+server = ServerPreset.tinyllama_infill()
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama_infill()
+
+
+def test_infill_without_input_extra():
+    global server
+    server.start()
+    res = server.make_request("POST", "/infill", data={
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
+        "input_suffix": "}\n",
+    })
+    assert res.status_code == 200
+    assert match_regex("(Ann|small|shiny|Daddy)+", res.body["content"])
+
+
+def test_infill_with_input_extra():
+    global server
+    server.start()
+    res = server.make_request("POST", "/infill", data={
+        "input_extra": [{
+            "filename": "llama.h",
+            "text": "LLAMA_API int32_t llama_n_threads();\n"
+        }],
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
+        "input_suffix": "}\n",
+    })
+    assert res.status_code == 200
+    assert match_regex("(Dad|excited|park)+", res.body["content"])
+
+
+@pytest.mark.parametrize("input_extra", [
+    {},
+    {"filename": "ok"},
+    {"filename": 123},
+    {"filename": 123, "text": "abc"},
+    {"filename": 123, "text": 456},
+])
+def test_invalid_input_extra_req(input_extra):
+    global server
+    server.start()
+    res = server.make_request("POST", "/infill", data={
+        "input_extra": [input_extra],
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
+        "input_suffix": "}\n",
+    })
+    assert res.status_code == 400
+    assert "error" in res.body
+
+
+@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
+def test_with_qwen_model():
+    global server
+    server.model_file = None
+    server.model_hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-IQ3_XXS-GGUF"
+    server.model_hf_file = "qwen2.5-coder-1.5b-iq3_xxs-imat.gguf"
+    server.start(timeout_seconds=600)
+    res = server.make_request("POST", "/infill", data={
+        "input_extra": [{
+            "filename": "llama.h",
+            "text": "LLAMA_API int32_t llama_n_threads();\n"
+        }],
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
+        "input_suffix": "}\n",
+    })
+    assert res.status_code == 200
+    assert res.body["content"] == "n_threads();\n    printf(\"Number of threads: %d\\n\", n_threads);\n    return 0;\n"
--- a/tools/server/tests/unit/test_lora.py
+++ b/tools/server/tests/unit/test_lora.py
@ -0,0 +1,115 @@
+import pytest
+from utils import *
+
+server = ServerPreset.stories15m_moe()
+
+LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf"
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.stories15m_moe()
+    server.lora_files = [download_file(LORA_FILE_URL)]
+
+
+@pytest.mark.parametrize("scale,re_content", [
+    # without applying lora, the model should behave like a bedtime story generator
+    (0.0, "(little|girl|three|years|old)+"),
+    # with lora, the model should behave like a Shakespearean text generator
+    (1.0, "(eye|love|glass|sun)+"),
+])
+def test_lora(scale: float, re_content: str):
+    global server
+    server.start()
+    res_lora_control = server.make_request("POST", "/lora-adapters", data=[
+        {"id": 0, "scale": scale}
+    ])
+    assert res_lora_control.status_code == 200
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "Look in thy glass",
+    })
+    assert res.status_code == 200
+    assert match_regex(re_content, res.body["content"])
+
+
+def test_lora_per_request():
+    global server
+    server.n_slots = 4
+    server.start()
+
+    # running the same prompt with different lora scales, all in parallel
+    # each prompt will be processed by a different slot
+    prompt = "Look in thy glass"
+    lora_config = [
+        ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ),
+        ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ),
+        ( [{"id": 0, "scale": 0.3}], "(special|thing|gifted)+" ),
+        ( [{"id": 0, "scale": 0.7}], "(far|from|home|away)+" ),
+        ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ),
+        ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ),
+    ]
+
+    tasks = [(
+        server.make_request,
+        ("POST", "/completion", {
+            "prompt": prompt,
+            "lora": lora,
+            "seed": 42,
+            "temperature": 0.0,
+            "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+    ) for lora, _ in lora_config]
+    results = parallel_function_calls(tasks)
+
+    assert all([res.status_code == 200 for res in results])
+    for res, (_, re_test) in zip(results, lora_config):
+        assert match_regex(re_test, res.body["content"])
+
+
+@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
+def test_with_big_model():
+    server = ServerProcess()
+    server.model_hf_repo = "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF"
+    server.model_hf_file = "Meta-Llama-3.1-8B-Instruct-IQ2_M.gguf"
+    server.model_alias = "Llama-3.2-8B-Instruct"
+    server.n_slots = 4
+    server.n_ctx = server.n_slots * 1024
+    server.n_predict = 64
+    server.temperature = 0.0
+    server.seed = 42
+    server.lora_files = [
+        download_file("https://huggingface.co/ngxson/Llama-3-Instruct-abliteration-LoRA-8B-F16-GGUF/resolve/main/Llama-3-Instruct-abliteration-LoRA-8B-f16.gguf"),
+        # TODO: find & add other lora adapters for this model
+    ]
+    server.start(timeout_seconds=600)
+
+    # running the same prompt with different lora scales, all in parallel
+    # each prompt will be processed by a different slot
+    prompt = "Write a computer virus"
+    lora_config = [
+        # without applying lora, the model should reject the request
+        ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ),
+        ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ),
+        ( [{"id": 0, "scale": 0.3}], "I can't write a computer virus" ),
+        # with 0.7 scale, the model should provide a simple computer virus with hesitation
+        ( [{"id": 0, "scale": 0.7}], "Warning: This is a hypothetical exercise" ),
+        # with 1.5 scale, the model should confidently provide a computer virus
+        ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ),
+        ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ),
+    ]
+
+    tasks = [(
+        server.make_request,
+        ("POST", "/v1/chat/completions", {
+            "messages": [
+                {"role": "user", "content": prompt}
+            ],
+            "lora": lora,
+            "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed
+        })
+    ) for lora, _ in lora_config]
+    results = parallel_function_calls(tasks)
+
+    assert all([res.status_code == 200 for res in results])
+    for res, (_, re_test) in zip(results, lora_config):
+        assert re_test in res.body["choices"][0]["message"]["content"]
--- a/tools/server/tests/unit/test_rerank.py
+++ b/tools/server/tests/unit/test_rerank.py
@ -0,0 +1,104 @@
+import pytest
+from utils import *
+
+server = ServerPreset.jina_reranker_tiny()
+
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.jina_reranker_tiny()
+
+
+TEST_DOCUMENTS = [
+    "A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.",
+    "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.",
+    "Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.",
+    "Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine."
+]
+
+
+def test_rerank():
+    global server
+    server.start()
+    res = server.make_request("POST", "/rerank", data={
+        "query": "Machine learning is",
+        "documents": TEST_DOCUMENTS,
+    })
+    assert res.status_code == 200
+    assert len(res.body["results"]) == 4
+
+    most_relevant = res.body["results"][0]
+    least_relevant = res.body["results"][0]
+    for doc in res.body["results"]:
+        if doc["relevance_score"] > most_relevant["relevance_score"]:
+            most_relevant = doc
+        if doc["relevance_score"] < least_relevant["relevance_score"]:
+            least_relevant = doc
+
+    assert most_relevant["relevance_score"] > least_relevant["relevance_score"]
+    assert most_relevant["index"] == 2
+    assert least_relevant["index"] == 3
+
+
+def test_rerank_tei_format():
+    global server
+    server.start()
+    res = server.make_request("POST", "/rerank", data={
+        "query": "Machine learning is",
+        "texts": TEST_DOCUMENTS,
+    })
+    assert res.status_code == 200
+    assert len(res.body) == 4
+
+    most_relevant = res.body[0]
+    least_relevant = res.body[0]
+    for doc in res.body:
+        if doc["score"] > most_relevant["score"]:
+            most_relevant = doc
+        if doc["score"] < least_relevant["score"]:
+            least_relevant = doc
+
+    assert most_relevant["score"] > least_relevant["score"]
+    assert most_relevant["index"] == 2
+    assert least_relevant["index"] == 3
+
+
+@pytest.mark.parametrize("documents", [
+    [],
+    None,
+    123,
+    [1, 2, 3],
+])
+def test_invalid_rerank_req(documents):
+    global server
+    server.start()
+    res = server.make_request("POST", "/rerank", data={
+        "query": "Machine learning is",
+        "documents": documents,
+    })
+    assert res.status_code == 400
+    assert "error" in res.body
+
+
+@pytest.mark.parametrize(
+    "query,doc1,doc2,n_tokens",
+    [
+        ("Machine learning is", "A machine", "Learning is", 19),
+        ("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
+    ]
+)
+def test_rerank_usage(query, doc1, doc2, n_tokens):
+    global server
+    server.start()
+
+    res = server.make_request("POST", "/rerank", data={
+        "query": query,
+        "documents": [
+            doc1,
+            doc2,
+        ]
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
--- a/tools/server/tests/unit/test_security.py
+++ b/tools/server/tests/unit/test_security.py
@ -0,0 +1,83 @@
+import pytest
+from openai import OpenAI
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+TEST_API_KEY = "sk-this-is-the-secret-key"
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.api_key = TEST_API_KEY
+
+
+@pytest.mark.parametrize("endpoint", ["/health", "/models"])
+def test_access_public_endpoint(endpoint: str):
+    global server
+    server.start()
+    res = server.make_request("GET", endpoint)
+    assert res.status_code == 200
+    assert "error" not in res.body
+
+
+@pytest.mark.parametrize("api_key", [None, "invalid-key"])
+def test_incorrect_api_key(api_key: str):
+    global server
+    server.start()
+    res = server.make_request("POST", "/completions", data={
+        "prompt": "I believe the meaning of life is",
+    }, headers={
+        "Authorization": f"Bearer {api_key}" if api_key else None,
+    })
+    assert res.status_code == 401
+    assert "error" in res.body
+    assert res.body["error"]["type"] == "authentication_error"
+
+
+def test_correct_api_key():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completions", data={
+        "prompt": "I believe the meaning of life is",
+    }, headers={
+        "Authorization": f"Bearer {TEST_API_KEY}",
+    })
+    assert res.status_code == 200
+    assert "error" not in res.body
+    assert "content" in res.body
+
+
+def test_openai_library_correct_api_key():
+    global server
+    server.start()
+    client = OpenAI(api_key=TEST_API_KEY, base_url=f"http://{server.server_host}:{server.server_port}")
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a chatbot."},
+            {"role": "user", "content": "What is the meaning of life?"},
+        ],
+    )
+    assert len(res.choices) == 1
+
+
+@pytest.mark.parametrize("origin,cors_header,cors_header_value", [
+    ("localhost", "Access-Control-Allow-Origin", "localhost"),
+    ("web.mydomain.fr", "Access-Control-Allow-Origin", "web.mydomain.fr"),
+    ("origin", "Access-Control-Allow-Credentials", "true"),
+    ("web.mydomain.fr", "Access-Control-Allow-Methods", "GET, POST"),
+    ("web.mydomain.fr", "Access-Control-Allow-Headers", "*"),
+])
+def test_cors_options(origin: str, cors_header: str, cors_header_value: str):
+    global server
+    server.start()
+    res = server.make_request("OPTIONS", "/completions", headers={
+        "Origin": origin,
+        "Access-Control-Request-Method": "POST",
+        "Access-Control-Request-Headers": "Authorization",
+    })
+    assert res.status_code == 200
+    assert cors_header in res.headers
+    assert res.headers[cors_header] == cors_header_value
--- a/tools/server/tests/unit/test_slot_save.py
+++ b/tools/server/tests/unit/test_slot_save.py
@ -0,0 +1,98 @@
+import pytest
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.slot_save_path = "./tmp"
+    server.temperature = 0.0
+
+
+def test_slot_save_restore():
+    global server
+    server.start()
+
+    # First prompt in slot 1 should be fully processed
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of France?",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert match_regex("(Whiskers|Flana)+", res.body["content"])
+    assert res.body["timings"]["prompt_n"] == 21  # all tokens are processed
+
+    # Save state of slot 1
+    res = server.make_request("POST", "/slots/1?action=save", data={
+        "filename": "slot1.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_saved"] == 84
+
+    # Since we have cache, this should only process the last tokens
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of Germany?",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert match_regex("(Jack|said)+", res.body["content"])
+    assert res.body["timings"]["prompt_n"] == 6  # only different part is processed
+
+    # Loading the saved cache into slot 0
+    res = server.make_request("POST", "/slots/0?action=restore", data={
+        "filename": "slot1.bin",
+    })
+    assert res.status_code == 200
+    assert res.body["n_restored"] == 84
+
+    # Since we have cache, slot 0 should only process the last tokens
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of Germany?",
+        "id_slot": 0,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert match_regex("(Jack|said)+", res.body["content"])
+    assert res.body["timings"]["prompt_n"] == 6  # only different part is processed
+
+    # For verification that slot 1 was not corrupted during slot 0 load, same thing should work
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of Germany?",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert match_regex("(Jack|said)+", res.body["content"])
+    assert res.body["timings"]["prompt_n"] == 1
+
+
+def test_slot_erase():
+    global server
+    server.start()
+
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of France?",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert match_regex("(Whiskers|Flana)+", res.body["content"])
+    assert res.body["timings"]["prompt_n"] == 21  # all tokens are processed
+
+    # erase slot 1
+    res = server.make_request("POST", "/slots/1?action=erase")
+    assert res.status_code == 200
+
+    # re-run the same prompt, it should process all tokens again
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "What is the capital of France?",
+        "id_slot": 1,
+        "cache_prompt": True,
+    })
+    assert res.status_code == 200
+    assert match_regex("(Whiskers|Flana)+", res.body["content"])
+    assert res.body["timings"]["prompt_n"] == 21  # all tokens are processed
--- a/tools/server/tests/unit/test_speculative.py
+++ b/tools/server/tests/unit/test_speculative.py
@ -0,0 +1,126 @@
+import pytest
+from utils import *
+
+# We use a F16 MOE gguf as main model, and q4_0 as draft model
+
+server = ServerPreset.stories15m_moe()
+
+MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories15M-q4_0.gguf"
+
+def create_server():
+    global server
+    server = ServerPreset.stories15m_moe()
+    # set default values
+    server.model_draft = download_file(MODEL_DRAFT_FILE_URL)
+    server.draft_min = 4
+    server.draft_max = 8
+
+
+@pytest.fixture(scope="module", autouse=True)
+def fixture_create_server():
+    return create_server()
+
+
+def test_with_and_without_draft():
+    global server
+    server.model_draft = None  # disable draft model
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "temperature": 0.0,
+        "top_k": 1,
+    })
+    assert res.status_code == 200
+    content_no_draft = res.body["content"]
+    server.stop()
+
+    # create new server with draft model
+    create_server()
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "temperature": 0.0,
+        "top_k": 1,
+    })
+    assert res.status_code == 200
+    content_draft = res.body["content"]
+
+    assert content_no_draft == content_draft
+
+
+def test_different_draft_min_draft_max():
+    global server
+    test_values = [
+        (1, 2),
+        (1, 4),
+        (4, 8),
+        (4, 12),
+        (8, 16),
+    ]
+    last_content = None
+    for draft_min, draft_max in test_values:
+        server.stop()
+        server.draft_min = draft_min
+        server.draft_max = draft_max
+        server.start()
+        res = server.make_request("POST", "/completion", data={
+            "prompt": "I believe the meaning of life is",
+            "temperature": 0.0,
+            "top_k": 1,
+        })
+        assert res.status_code == 200
+        if last_content is not None:
+            assert last_content == res.body["content"]
+        last_content = res.body["content"]
+
+
+def test_slot_ctx_not_exceeded():
+    global server
+    server.n_ctx = 64
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "Hello " * 56,
+        "temperature": 0.0,
+        "top_k": 1,
+        "speculative.p_min": 0.0,
+    })
+    assert res.status_code == 200
+    assert len(res.body["content"]) > 0
+
+
+def test_with_ctx_shift():
+    global server
+    server.n_ctx = 64
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "Hello " * 56,
+        "temperature": 0.0,
+        "top_k": 1,
+        "n_predict": 64,
+        "speculative.p_min": 0.0,
+    })
+    assert res.status_code == 200
+    assert len(res.body["content"]) > 0
+    assert res.body["tokens_predicted"] == 64
+    assert res.body["truncated"] == True
+
+
+@pytest.mark.parametrize("n_slots,n_requests", [
+    (1, 2),
+    (2, 2),
+])
+def test_multi_requests_parallel(n_slots: int, n_requests: int):
+    global server
+    server.n_slots = n_slots
+    server.start()
+    tasks = []
+    for _ in range(n_requests):
+        tasks.append((server.make_request, ("POST", "/completion", {
+            "prompt": "I believe the meaning of life is",
+            "temperature": 0.0,
+            "top_k": 1,
+        })))
+    results = parallel_function_calls(tasks)
+    for res in results:
+        assert res.status_code == 200
+        assert match_regex("(wise|kind|owl|answer)+", res.body["content"])
--- a/tools/server/tests/unit/test_tokenize.py
+++ b/tools/server/tests/unit/test_tokenize.py
@ -0,0 +1,59 @@
+import pytest
+from utils import *
+
+server = ServerPreset.tinyllama2()
+
+
+@pytest.fixture(scope="module", autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+
+
+def test_tokenize_detokenize():
+    global server
+    server.start()
+    # tokenize
+    content = "What is the capital of France ?"
+    res_tok = server.make_request("POST", "/tokenize", data={
+        "content": content
+    })
+    assert res_tok.status_code == 200
+    assert len(res_tok.body["tokens"]) > 5
+    # detokenize
+    res_detok = server.make_request("POST", "/detokenize", data={
+        "tokens": res_tok.body["tokens"],
+    })
+    assert res_detok.status_code == 200
+    assert res_detok.body["content"].strip() == content
+
+
+def test_tokenize_with_bos():
+    global server
+    server.start()
+    # tokenize
+    content = "What is the capital of France ?"
+    bosId = 1
+    res_tok = server.make_request("POST", "/tokenize", data={
+        "content": content,
+        "add_special": True,
+    })
+    assert res_tok.status_code == 200
+    assert res_tok.body["tokens"][0] == bosId
+
+
+def test_tokenize_with_pieces():
+    global server
+    server.start()
+    # tokenize
+    content = "This is a test string with unicode 媽 and emoji 🤗"
+    res_tok = server.make_request("POST", "/tokenize", data={
+        "content": content,
+        "with_pieces": True,
+    })
+    assert res_tok.status_code == 200
+    for token in res_tok.body["tokens"]:
+        assert "id" in token
+        assert token["id"] > 0
+        assert "piece" in token
+        assert len(token["piece"]) > 0
--- a/tools/server/tests/unit/test_tool_call.py
+++ b/tools/server/tests/unit/test_tool_call.py
@ -0,0 +1,606 @@
+#!/usr/bin/env python
+import pytest
+
+# ensure grandparent path is in sys.path
+from pathlib import Path
+import sys
+path = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(path))
+
+from utils import *
+
+server: ServerProcess
+
+TIMEOUT_SERVER_START = 15*60
+TIMEOUT_HTTP_REQUEST = 60
+
+@pytest.fixture(autouse=True)
+def create_server():
+    global server
+    server = ServerPreset.tinyllama2()
+    server.model_alias = "tinyllama-2-tool-call"
+    server.server_port = 8081
+
+
+TEST_TOOL = {
+    "type":"function",
+    "function": {
+        "name": "test",
+        "description": "",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "success": {"type": "boolean", "const": True},
+            },
+            "required": ["success"]
+        }
+    }
+}
+
+PYTHON_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "python",
+        "description": "Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "The code to run in the ipython interpreter."
+                }
+            },
+            "required": ["code"]
+        }
+    }
+}
+
+WEATHER_TOOL = {
+  "type":"function",
+  "function":{
+    "name":"get_current_weather",
+    "description":"Get the current weather in a given location",
+    "parameters":{
+      "type":"object",
+      "properties":{
+        "location":{
+          "type":"string",
+          "description":"The city and country/state, e.g. 'San Francisco, CA', or 'Paris, France'"
+        }
+      },
+      "required":["location"]
+    }
+  }
+}
+
+
+def do_test_completion_with_required_tool_tiny(server: ServerProcess, tool: dict, argument_key: str | None, n_predict, **kwargs):
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "tool_choice": "required",
+        "tools": [tool],
+        "parallel_tool_calls": False,
+        **kwargs,
+    })
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
+    assert len(tool_call.get("id", "")) > 0, f'Expected non empty tool call id in {tool_call}'
+    expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
+    assert expected_function_name == tool_call["function"]["name"]
+    actual_arguments = tool_call["function"]["arguments"]
+    assert isinstance(actual_arguments, str)
+    if argument_key is not None:
+        actual_arguments = json.loads(actual_arguments)
+        assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}"
+
+
+@pytest.mark.parametrize("template_name,tool,argument_key", [
+    ("google-gemma-2-2b-it",                          TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.3-70B-Instruct",             TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.3-70B-Instruct",             PYTHON_TOOL,          "code"),
+])
+def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None):
+    global server
+    n_predict = 512
+    # server = ServerPreset.stories15m_moe()
+    server.jinja = True
+    server.n_predict = n_predict
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    do_test_completion_with_required_tool_tiny(server, tool, argument_key, n_predict, temperature=0.0, top_k=1, top_p=1.0)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("template_name,tool,argument_key", [
+    ("meta-llama-Llama-3.1-8B-Instruct",              TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.1-8B-Instruct",              PYTHON_TOOL,          "code"),
+    ("meetkai-functionary-medium-v3.1",               TEST_TOOL,            "success"),
+    ("meetkai-functionary-medium-v3.1",               PYTHON_TOOL,          "code"),
+    ("meetkai-functionary-medium-v3.2",               TEST_TOOL,            "success"),
+    ("meetkai-functionary-medium-v3.2",               PYTHON_TOOL,          "code"),
+    ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL,            "success"),
+    ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL,          "code"),
+    ("meta-llama-Llama-3.2-3B-Instruct",              TEST_TOOL,            "success"),
+    ("meta-llama-Llama-3.2-3B-Instruct",              PYTHON_TOOL,          "code"),
+    ("mistralai-Mistral-Nemo-Instruct-2407",          TEST_TOOL,            "success"),
+    ("mistralai-Mistral-Nemo-Instruct-2407",          PYTHON_TOOL,          "code"),
+    ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   TEST_TOOL,            "success"),
+    ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use",   PYTHON_TOOL,          "code"),
+    ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      TEST_TOOL,            "success"),
+    ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B",      PYTHON_TOOL,          "code"),
+    ("fireworks-ai-llama-3-firefunction-v2",          TEST_TOOL,            "success"),
+    # ("fireworks-ai-llama-3-firefunction-v2",          PYTHON_TOOL,          "code"),
+])
+def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None):
+    global server
+    n_predict = 512
+    # server = ServerPreset.stories15m_moe()
+    server.jinja = True
+    server.n_predict = n_predict
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    do_test_completion_with_required_tool_tiny(server, tool, argument_key, n_predict)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("tool,argument_key,hf_repo,template_override", [
+    (TEST_TOOL,    "success",  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    (PYTHON_TOOL,  "code",     "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    (PYTHON_TOOL,  "code",     "bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Qwen2.5-1.5B-Instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-1.5B-Instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-1.5B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M",      None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    (PYTHON_TOOL,  "code",     "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    (PYTHON_TOOL,  "code",     "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",   "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/functionary-small-v3.2-GGUF:Q4_K_M",       ("meetkai/functionary-medium-v3.2", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/functionary-small-v3.2-GGUF:Q4_K_M",       ("meetkai/functionary-medium-v3.2", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/functionary-small-v3.2-GGUF:Q4_K_M",       "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    (PYTHON_TOOL,  "code",     "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    (TEST_TOOL,    "success",  "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+    (PYTHON_TOOL,  "code",     "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+])
+def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+    global server
+    n_predict = 512
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192
+    server.n_predict = n_predict
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if isinstance(template_override, tuple):
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "tool_choice": "required",
+        "tools": [tool],
+        "parallel_tool_calls": False,
+        "temperature": 0.0,
+        "top_k": 1,
+        "top_p": 1.0,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    # assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
+    expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
+    assert expected_function_name == tool_call["function"]["name"]
+    actual_arguments = tool_call["function"]["arguments"]
+    assert isinstance(actual_arguments, str)
+    if argument_key is not None:
+        actual_arguments = json.loads(actual_arguments)
+        assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}"
+
+
+def do_test_completion_without_tool_call(server: ServerProcess, n_predict: int, tools: list[dict], tool_choice: str | None, **kwargs):
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "say hello world with python"},
+        ],
+        "tools": tools if tools else None,
+        "tool_choice": tool_choice,
+        **kwargs,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}'
+
+
+@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [
+    ("meta-llama-Llama-3.3-70B-Instruct",         128, [],            None),
+    ("meta-llama-Llama-3.3-70B-Instruct",         128, [TEST_TOOL],   None),
+    ("meta-llama-Llama-3.3-70B-Instruct",         128, [PYTHON_TOOL], 'none'),
+])
+def test_completion_without_tool_call_fast(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
+    global server
+    server.jinja = True
+    server.n_predict = n_predict
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    do_test_completion_without_tool_call(server, n_predict, tools, tool_choice)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [
+    ("meetkai-functionary-medium-v3.2",               256, [],            None),
+    ("meetkai-functionary-medium-v3.2",               256, [TEST_TOOL],   None),
+    ("meetkai-functionary-medium-v3.2",               256, [PYTHON_TOOL], 'none'),
+    ("meetkai-functionary-medium-v3.1",               256, [],            None),
+    ("meetkai-functionary-medium-v3.1",               256, [TEST_TOOL],   None),
+    ("meetkai-functionary-medium-v3.1",               256, [PYTHON_TOOL], 'none'),
+    ("meta-llama-Llama-3.2-3B-Instruct",              256, [],            None),
+    ("meta-llama-Llama-3.2-3B-Instruct",              256, [TEST_TOOL],   None),
+    ("meta-llama-Llama-3.2-3B-Instruct",              256, [PYTHON_TOOL], 'none'),
+])
+def test_completion_without_tool_call_slow(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
+    global server
+    server.jinja = True
+    server.n_predict = n_predict
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    do_test_completion_without_tool_call(server, n_predict, tools, tool_choice)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("hf_repo,template_override", [
+    ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
+
+    ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/Qwen2.5-1.5B-Instruct-GGUF:Q4_K_M",      None),
+    ("bartowski/Qwen2.5-1.5B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M",      None),
+    ("bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        "chatml"),
+
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    "chatml"),
+
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
+
+    ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai/functionary-medium-v3.2", None)),
+    ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       "chatml"),
+
+    ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama/Llama-3.2-3B-Instruct", None)),
+    ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L",   ("CohereForAI/c4ai-command-r7b-12-2024", "tool_use")),
+
+    ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+
+    # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
+    ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+
+    # ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
+])
+def test_weather(hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+    global server
+    n_predict = 512
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192
+    server.n_predict = n_predict
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if isinstance(template_override, tuple):
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    do_test_weather(server, max_tokens=n_predict)
+
+
+def do_test_weather(server: ServerProcess, **kwargs):
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "messages": [
+            {"role": "system", "content": "You are a chatbot that uses tools/functions. Dont overthink things."},
+            {"role": "user", "content": "What is the weather in Istanbul?"},
+        ],
+        "tools": [WEATHER_TOOL],
+        **kwargs,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    # assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
+    assert tool_call["function"]["name"] == WEATHER_TOOL["function"]["name"], f'Expected weather tool call, got {tool_call["function"]["name"]}'
+    assert len(tool_call.get("id", "")) > 0, f'Expected non empty tool call id in {tool_call}'
+    actual_arguments = json.loads(tool_call["function"]["arguments"])
+    assert 'location' in actual_arguments, f"location not found in {json.dumps(actual_arguments)}"
+    location = actual_arguments["location"]
+    assert isinstance(location, str), f"Expected location to be a string, got {type(location)}: {json.dumps(location)}"
+    assert re.match('^Istanbul(( |, ?)(TR|Turkey|Türkiye))?$', location), f'Expected Istanbul for location, got {location}'
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("result_override,n_predict,hf_repo,template_override", [
+    (None,                                           128,  "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",       "chatml"),
+    (None,                                           128,  "bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M", None),
+    (None,                                           128,  "bartowski/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M", "chatml"),
+    (None,                                           128,  "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",         "chatml"),
+    (None,                                           128,  "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",     ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    (None,                                           128,  "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",       ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
+    (None,                                           128,  "bartowski/functionary-small-v3.2-GGUF:Q8_0",        ("meetkai/functionary-medium-v3.2", None)),
+    (None,                                           128,  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M",  None),
+    (None,                                           128,  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M",  "chatml"),
+    (None,                                           128,  "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",       None),
+    ("[\\s\\S]*?\\*\\*\\s*0.5($|\\*\\*)",            8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)),
+
+    # TODO: fix these (wrong results, either didn't respect decimal instruction or got wrong value)
+    # (None,                                           128,  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M",  None),
+    # ("[\\s\\S]*?\\*\\*\\s*0.5($|\\*\\*)",            8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+])
+def test_calc_result(result_override: str | None, n_predict: int, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+    global server
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192 * 2
+    server.n_predict = n_predict
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if isinstance(template_override, tuple):
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    do_test_calc_result(server, result_override, n_predict)
+
+
+def do_test_calc_result(server: ServerProcess, result_override: str | None, n_predict: int, **kwargs):
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "system", "content": "You are a tools-calling assistant. You express numerical values with at most two decimals."},
+            {"role": "user", "content": "What's the y coordinate of a point on the unit sphere at angle 30 degrees?"},
+            {
+                "role": "assistant",
+                "content": None,
+                "tool_calls": [
+                    {
+                        "id": "call_6789",
+                        "type": "function",
+                        "function": {
+                            "name": "calculate",
+                            "arguments": "{\"expression\":\"sin(30 * pi / 180)\"}"
+                        }
+                    }
+                ]
+            },
+            {
+                "role": "tool",
+                "name": "calculate",
+                "content": "0.55644242476",
+                "tool_call_id": "call_6789"
+            }
+        ],
+        "tools": [
+            {
+                "type":"function",
+                "function":{
+                    "name":"calculate",
+                    "description":"A calculator function that computes values of arithmetic expressions in the Python syntax",
+                    "parameters":{
+                        "type":"object",
+                        "properties":{
+                            "expression":{
+                            "type":"string",
+                            "description":"An arithmetic expression to compute the value of (Python syntad, assuming all floats)"
+                            }
+                        },
+                        "required":["expression"]
+                    }
+                }
+            }
+        ],
+        **kwargs,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls is None, f'Expected no tool call in {choice["message"]}'
+    content = choice["message"].get("content")
+    assert content is not None, f'Expected content in {choice["message"]}'
+    if result_override is not None:
+        assert re.match(result_override, content), f'Expected {result_override}, got {content}'
+    else:
+        assert re.match('^[\\s\\S]*?((That\'s|\\bis) (approximately )?)?\\b0\\.(5\\b|56\\b|556)', content), \
+            f'Expected something like "The y coordinate is 0.56.", got {content}'
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("n_predict,reasoning_format,expect_content,expect_reasoning_content,hf_repo,template_override", [
+    (128, 'deepseek',  "^The sum of 102 and 7 is 109[\\s\\S]*",                        None,                                          "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",       None),
+    (128,  None,        "^The sum of 102 and 7 is 109[\\s\\S]*",                       None,                                          "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",       None),
+
+    (1024, 'deepseek',  "To find the sum of[\\s\\S]*",                                 "I need to calculate the sum of 102 and 7[\\s\\S]*",  "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+    (1024, 'none',      "^(<think>\\s*)?I need[\\s\\S]*?</think>\\s*To find[\\s\\S]*",                None,                                          "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+
+    (1024, 'deepseek',  "To find the sum of[\\s\\S]*",                                 "First, I [\\s\\S]*",                          "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)),
+])
+def test_thoughts(n_predict: int, reasoning_format: Literal['deepseek', 'none'] | None, expect_content: str | None, expect_reasoning_content: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+    global server
+    server.n_slots = 1
+    server.reasoning_format = reasoning_format
+    server.jinja = True
+    server.n_ctx = 8192 * 2
+    server.n_predict = n_predict
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if isinstance(template_override, tuple):
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "max_tokens": n_predict,
+        "messages": [
+            {"role": "user", "content": "What's the sum of 102 and 7?"},
+        ]
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}'
+
+    content = choice["message"].get("content")
+    if expect_content is None:
+        assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
+    else:
+        assert re.match(expect_content, content), f'Expected {expect_content}, got {content}'
+
+    reasoning_content = choice["message"].get("reasoning_content")
+    if expect_reasoning_content is None:
+        assert reasoning_content is None, f'Expected no reasoning content in {choice["message"]}'
+    else:
+        assert re.match(expect_reasoning_content, reasoning_content), f'Expected {expect_reasoning_content}, got {reasoning_content}'
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("hf_repo,template_override", [
+    ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+
+    ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      None),
+    ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       ("meetkai-functionary-medium-v3.2", None)),
+    ("bartowski/functionary-small-v3.2-GGUF:Q8_0",       "chatml"),
+
+    # ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
+    ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
+
+    ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M",      None),
+
+    ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      ("meta-llama-Llama-3.2-3B-Instruct", None)),
+    ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M",      None),
+
+    ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        None),
+    ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M",        "chatml"),
+
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
+    ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M",    "chatml"),
+
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")),
+    ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M",      "chatml"),
+
+    ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
+    ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
+
+    ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              None),
+    ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M",              "chatml"),
+])
+def test_hello_world(hf_repo: str, template_override: str | Tuple[str, str | None] | None):
+    global server
+    n_predict = 512 # High because of DeepSeek R1
+    server.n_slots = 1
+    server.jinja = True
+    server.n_ctx = 8192
+    server.n_predict = n_predict
+    server.model_hf_repo = hf_repo
+    server.model_hf_file = None
+    if isinstance(template_override, tuple):
+        (template_hf_repo, template_variant) = template_override
+        server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
+        assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
+    elif isinstance(template_override, str):
+        server.chat_template = template_override
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+
+    do_test_hello_world(server, max_tokens=n_predict)
+
+
+def do_test_hello_world(server: ServerProcess, **kwargs):
+    res = server.make_request("POST", "/v1/chat/completions", data={
+        "messages": [
+            {"role": "system", "content": "You are a tool-calling agent."},
+            {"role": "user", "content": "say hello world with python"},
+        ],
+        "tools": [PYTHON_TOOL],
+        **kwargs,
+    }, timeout=TIMEOUT_HTTP_REQUEST)
+    assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
+    choice = res.body["choices"][0]
+    tool_calls = choice["message"].get("tool_calls")
+    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
+    tool_call = tool_calls[0]
+    # assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
+    assert tool_call["function"]["name"] == PYTHON_TOOL["function"]["name"]
+    assert len(tool_call.get("id", "")) > 0, f'Expected non empty tool call id in {tool_call}'
+    actual_arguments = json.loads(tool_call["function"]["arguments"])
+    assert 'code' in actual_arguments, f"code not found in {json.dumps(actual_arguments)}"
+    code = actual_arguments["code"]
+    assert isinstance(code, str), f"Expected code to be a string, got {type(code)}: {json.dumps(code)}"
+    assert re.match(r'''print\(("[Hh]ello,? [Ww]orld!?"|'[Hh]ello,? [Ww]orld!?')\)''', code), f'Expected hello world, got {code}'
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@ -0,0 +1,452 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# type: ignore[reportUnusedImport]
+
+import subprocess
+import os
+import re
+import json
+import sys
+import requests
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import (
+    Any,
+    Callable,
+    ContextManager,
+    Iterable,
+    Iterator,
+    List,
+    Literal,
+    Tuple,
+    Set,
+)
+from re import RegexFlag
+import wget
+
+
+DEFAULT_HTTP_TIMEOUT = 12
+
+if "LLAMA_SANITIZE" in os.environ or "GITHUB_ACTION" in os.environ:
+    DEFAULT_HTTP_TIMEOUT = 30
+
+
+class ServerResponse:
+    headers: dict
+    status_code: int
+    body: dict | Any
+
+
+class ServerProcess:
+    # default options
+    debug: bool = False
+    server_port: int = 8080
+    server_host: str = "127.0.0.1"
+    model_hf_repo: str = "ggml-org/models"
+    model_hf_file: str | None = "tinyllamas/stories260K.gguf"
+    model_alias: str = "tinyllama-2"
+    temperature: float = 0.8
+    seed: int = 42
+
+    # custom options
+    model_alias: str | None = None
+    model_url: str | None = None
+    model_file: str | None = None
+    model_draft: str | None = None
+    n_threads: int | None = None
+    n_gpu_layer: int | None = None
+    n_batch: int | None = None
+    n_ubatch: int | None = None
+    n_ctx: int | None = None
+    n_ga: int | None = None
+    n_ga_w: int | None = None
+    n_predict: int | None = None
+    n_prompts: int | None = 0
+    slot_save_path: str | None = None
+    id_slot: int | None = None
+    cache_prompt: bool | None = None
+    n_slots: int | None = None
+    ctk: str | None = None
+    ctv: str | None = None
+    fa: bool | None = None
+    server_continuous_batching: bool | None = False
+    server_embeddings: bool | None = False
+    server_reranking: bool | None = False
+    server_metrics: bool | None = False
+    server_slots: bool | None = False
+    pooling: str | None = None
+    draft: int | None = None
+    api_key: str | None = None
+    lora_files: List[str] | None = None
+    disable_ctx_shift: int | None = False
+    draft_min: int | None = None
+    draft_max: int | None = None
+    no_webui: bool | None = None
+    jinja: bool | None = None
+    reasoning_format: Literal['deepseek', 'none'] | None = None
+    chat_template: str | None = None
+    chat_template_file: str | None = None
+    server_path: str | None = None
+
+    # session variables
+    process: subprocess.Popen | None = None
+
+    def __init__(self):
+        if "N_GPU_LAYERS" in os.environ:
+            self.n_gpu_layer = int(os.environ["N_GPU_LAYERS"])
+        if "DEBUG" in os.environ:
+            self.debug = True
+        if "PORT" in os.environ:
+            self.server_port = int(os.environ["PORT"])
+
+    def start(self, timeout_seconds: int | None = DEFAULT_HTTP_TIMEOUT) -> None:
+        if self.server_path is not None:
+            server_path = self.server_path
+        elif "LLAMA_SERVER_BIN_PATH" in os.environ:
+            server_path = os.environ["LLAMA_SERVER_BIN_PATH"]
+        elif os.name == "nt":
+            server_path = "../../../build/bin/Release/llama-server.exe"
+        else:
+            server_path = "../../../build/bin/llama-server"
+        server_args = [
+            "--host",
+            self.server_host,
+            "--port",
+            self.server_port,
+            "--temp",
+            self.temperature,
+            "--seed",
+            self.seed,
+        ]
+        if self.model_file:
+            server_args.extend(["--model", self.model_file])
+        if self.model_url:
+            server_args.extend(["--model-url", self.model_url])
+        if self.model_draft:
+            server_args.extend(["--model-draft", self.model_draft])
+        if self.model_hf_repo:
+            server_args.extend(["--hf-repo", self.model_hf_repo])
+        if self.model_hf_file:
+            server_args.extend(["--hf-file", self.model_hf_file])
+        if self.n_batch:
+            server_args.extend(["--batch-size", self.n_batch])
+        if self.n_ubatch:
+            server_args.extend(["--ubatch-size", self.n_ubatch])
+        if self.n_threads:
+            server_args.extend(["--threads", self.n_threads])
+        if self.n_gpu_layer:
+            server_args.extend(["--n-gpu-layers", self.n_gpu_layer])
+        if self.draft is not None:
+            server_args.extend(["--draft", self.draft])
+        if self.server_continuous_batching:
+            server_args.append("--cont-batching")
+        if self.server_embeddings:
+            server_args.append("--embedding")
+        if self.server_reranking:
+            server_args.append("--reranking")
+        if self.server_metrics:
+            server_args.append("--metrics")
+        if self.server_slots:
+            server_args.append("--slots")
+        if self.pooling:
+            server_args.extend(["--pooling", self.pooling])
+        if self.model_alias:
+            server_args.extend(["--alias", self.model_alias])
+        if self.n_ctx:
+            server_args.extend(["--ctx-size", self.n_ctx])
+        if self.n_slots:
+            server_args.extend(["--parallel", self.n_slots])
+        if self.ctk:
+            server_args.extend(["-ctk", self.ctk])
+        if self.ctv:
+            server_args.extend(["-ctv", self.ctv])
+        if self.fa is not None:
+            server_args.append("-fa")
+        if self.n_predict:
+            server_args.extend(["--n-predict", self.n_predict])
+        if self.slot_save_path:
+            server_args.extend(["--slot-save-path", self.slot_save_path])
+        if self.n_ga:
+            server_args.extend(["--grp-attn-n", self.n_ga])
+        if self.n_ga_w:
+            server_args.extend(["--grp-attn-w", self.n_ga_w])
+        if self.debug:
+            server_args.append("--verbose")
+        if self.lora_files:
+            for lora_file in self.lora_files:
+                server_args.extend(["--lora", lora_file])
+        if self.disable_ctx_shift:
+            server_args.extend(["--no-context-shift"])
+        if self.api_key:
+            server_args.extend(["--api-key", self.api_key])
+        if self.draft_max:
+            server_args.extend(["--draft-max", self.draft_max])
+        if self.draft_min:
+            server_args.extend(["--draft-min", self.draft_min])
+        if self.no_webui:
+            server_args.append("--no-webui")
+        if self.jinja:
+            server_args.append("--jinja")
+        if self.reasoning_format is not None:
+            server_args.extend(("--reasoning-format", self.reasoning_format))
+        if self.chat_template:
+            server_args.extend(["--chat-template", self.chat_template])
+        if self.chat_template_file:
+            server_args.extend(["--chat-template-file", self.chat_template_file])
+
+        args = [str(arg) for arg in [server_path, *server_args]]
+        print(f"tests: starting server with: {' '.join(args)}")
+
+        flags = 0
+        if "nt" == os.name:
+            flags |= subprocess.DETACHED_PROCESS
+            flags |= subprocess.CREATE_NEW_PROCESS_GROUP
+            flags |= subprocess.CREATE_NO_WINDOW
+
+        self.process = subprocess.Popen(
+            [str(arg) for arg in [server_path, *server_args]],
+            creationflags=flags,
+            stdout=sys.stdout,
+            stderr=sys.stdout,
+            env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None,
+        )
+        server_instances.add(self)
+
+        print(f"server pid={self.process.pid}, pytest pid={os.getpid()}")
+
+        # wait for server to start
+        start_time = time.time()
+        while time.time() - start_time < timeout_seconds:
+            try:
+                response = self.make_request("GET", "/health", headers={
+                    "Authorization": f"Bearer {self.api_key}" if self.api_key else None
+                })
+                if response.status_code == 200:
+                    self.ready = True
+                    return  # server is ready
+            except Exception as e:
+                pass
+            # Check if process died
+            if self.process.poll() is not None:
+                raise RuntimeError(f"Server process died with return code {self.process.returncode}")
+
+            print(f"Waiting for server to start...")
+            time.sleep(0.5)
+        raise TimeoutError(f"Server did not start within {timeout_seconds} seconds")
+
+    def stop(self) -> None:
+        if self in server_instances:
+            server_instances.remove(self)
+        if self.process:
+            print(f"Stopping server with pid={self.process.pid}")
+            self.process.kill()
+            self.process = None
+
+    def make_request(
+        self,
+        method: str,
+        path: str,
+        data: dict | Any | None = None,
+        headers: dict | None = None,
+        timeout: float | None = None,
+    ) -> ServerResponse:
+        url = f"http://{self.server_host}:{self.server_port}{path}"
+        parse_body = False
+        if method == "GET":
+            response = requests.get(url, headers=headers, timeout=timeout)
+            parse_body = True
+        elif method == "POST":
+            response = requests.post(url, headers=headers, json=data, timeout=timeout)
+            parse_body = True
+        elif method == "OPTIONS":
+            response = requests.options(url, headers=headers, timeout=timeout)
+        else:
+            raise ValueError(f"Unimplemented method: {method}")
+        result = ServerResponse()
+        result.headers = dict(response.headers)
+        result.status_code = response.status_code
+        result.body = response.json() if parse_body else None
+        print("Response from server", json.dumps(result.body, indent=2))
+        return result
+
+    def make_stream_request(
+        self,
+        method: str,
+        path: str,
+        data: dict | None = None,
+        headers: dict | None = None,
+    ) -> Iterator[dict]:
+        url = f"http://{self.server_host}:{self.server_port}{path}"
+        if method == "POST":
+            response = requests.post(url, headers=headers, json=data, stream=True)
+        else:
+            raise ValueError(f"Unimplemented method: {method}")
+        for line_bytes in response.iter_lines():
+            line = line_bytes.decode("utf-8")
+            if '[DONE]' in line:
+                break
+            elif line.startswith('data: '):
+                data = json.loads(line[6:])
+                print("Partial response from server", json.dumps(data, indent=2))
+                yield data
+
+
+server_instances: Set[ServerProcess] = set()
+
+
+class ServerPreset:
+    @staticmethod
+    def tinyllama2() -> ServerProcess:
+        server = ServerProcess()
+        server.model_hf_repo = "ggml-org/models"
+        server.model_hf_file = "tinyllamas/stories260K.gguf"
+        server.model_alias = "tinyllama-2"
+        server.n_ctx = 512
+        server.n_batch = 32
+        server.n_slots = 2
+        server.n_predict = 64
+        server.seed = 42
+        return server
+
+    @staticmethod
+    def bert_bge_small() -> ServerProcess:
+        server = ServerProcess()
+        server.model_hf_repo = "ggml-org/models"
+        server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf"
+        server.model_alias = "bert-bge-small"
+        server.n_ctx = 512
+        server.n_batch = 128
+        server.n_ubatch = 128
+        server.n_slots = 2
+        server.seed = 42
+        server.server_embeddings = True
+        return server
+
+    @staticmethod
+    def bert_bge_small_with_fa() -> ServerProcess:
+        server = ServerProcess()
+        server.model_hf_repo = "ggml-org/models"
+        server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf"
+        server.model_alias = "bert-bge-small"
+        server.n_ctx = 1024
+        server.n_batch = 300
+        server.n_ubatch = 300
+        server.n_slots = 2
+        server.fa = True
+        server.seed = 42
+        server.server_embeddings = True
+        return server
+
+    @staticmethod
+    def tinyllama_infill() -> ServerProcess:
+        server = ServerProcess()
+        server.model_hf_repo = "ggml-org/models"
+        server.model_hf_file = "tinyllamas/stories260K-infill.gguf"
+        server.model_alias = "tinyllama-infill"
+        server.n_ctx = 2048
+        server.n_batch = 1024
+        server.n_slots = 1
+        server.n_predict = 64
+        server.temperature = 0.0
+        server.seed = 42
+        return server
+
+    @staticmethod
+    def stories15m_moe() -> ServerProcess:
+        server = ServerProcess()
+        server.model_hf_repo = "ggml-org/stories15M_MOE"
+        server.model_hf_file = "stories15M_MOE-F16.gguf"
+        server.model_alias = "stories15m-moe"
+        server.n_ctx = 2048
+        server.n_batch = 1024
+        server.n_slots = 1
+        server.n_predict = 64
+        server.temperature = 0.0
+        server.seed = 42
+        return server
+
+    @staticmethod
+    def jina_reranker_tiny() -> ServerProcess:
+        server = ServerProcess()
+        server.model_hf_repo = "ggml-org/models"
+        server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf"
+        server.model_alias = "jina-reranker"
+        server.n_ctx = 512
+        server.n_batch = 512
+        server.n_slots = 1
+        server.seed = 42
+        server.server_reranking = True
+        return server
+
+
+def parallel_function_calls(function_list: List[Tuple[Callable[..., Any], Tuple[Any, ...]]]) -> List[Any]:
+    """
+    Run multiple functions in parallel and return results in the same order as calls. Equivalent to Promise.all in JS.
+
+    Example usage:
+
+    results = parallel_function_calls([
+        (func1, (arg1, arg2)),
+        (func2, (arg3, arg4)),
+    ])
+    """
+    results = [None] * len(function_list)
+    exceptions = []
+
+    def worker(index, func, args):
+        try:
+            result = func(*args)
+            results[index] = result
+        except Exception as e:
+            exceptions.append((index, str(e)))
+
+    with ThreadPoolExecutor() as executor:
+        futures = []
+        for i, (func, args) in enumerate(function_list):
+            future = executor.submit(worker, i, func, args)
+            futures.append(future)
+
+        # Wait for all futures to complete
+        for future in as_completed(futures):
+            pass
+
+    # Check if there were any exceptions
+    if exceptions:
+        print("Exceptions occurred:")
+        for index, error in exceptions:
+            print(f"Function at index {index}: {error}")
+
+    return results
+
+
+def match_regex(regex: str, text: str) -> bool:
+    return (
+        re.compile(
+            regex, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL
+        ).search(text)
+        is not None
+    )
+
+
+def download_file(url: str, output_file_path: str | None = None) -> str:
+    """
+    Download a file from a URL to a local path. If the file already exists, it will not be downloaded again.
+
+    output_file_path is the local path to save the downloaded file. If not provided, the file will be saved in the root directory.
+
+    Returns the local path of the downloaded file.
+    """
+    file_name = url.split('/').pop()
+    output_file = f'./tmp/{file_name}' if output_file_path is None else output_file_path
+    if not os.path.exists(output_file):
+        print(f"Downloading {url} to {output_file}")
+        wget.download(url, out=output_file)
+        print(f"Done downloading to {output_file}")
+    else:
+        print(f"File already exists at {output_file}")
+    return output_file
+
+
+def is_slow_test_allowed():
+    return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON"