ver4a/llama.cpp
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llama : add Phi-4-mini support (supersede #12099) (#12108)

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* Added Phi-4-mini-instruct support

* Update regex per ngxson

* Change the vocab base to Xenova/gpt-4o

* fix conversion update script

* no need to check longrope

* minor style fix

* fix python style

---------

Co-authored-by: Nicholas Sparks <nisparks@microsoft.com>
This commit is contained in:
Xuan-Son Nguyen 2025-02-28 12:44:11 +01:00 • committed by GitHub
parent 84d5f4bc19
commit c43a3e7996
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7 changed files with 191 additions and 8 deletions
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11
convert_hf_to_gguf.py
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@ -699,6 +699,9 @@ class Model:
if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5":
# ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
res = "deepseek-r1-qwen"
if chkhsh == "ccc2ef013c104be7bae2965776d611e1d7a8a2a9c547dd93a682c9a9fc80352e":
# ref: https://huggingface.co/Xenova/gpt-4o
res = "gpt-4o"
if res is None:
logger.warning("\n")
@ -2512,7 +2515,8 @@ class Phi3MiniModel(Model):
rms_eps = self.find_hparam(["rms_norm_eps"])
max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
rope_dims = n_embd // n_head
rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
rope_dims = int(rot_pct * n_embd) // n_head
self.gguf_writer.add_context_length(max_pos_embds)
self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
@ -2536,7 +2540,8 @@ class Phi3MiniModel(Model):
n_head = self.find_hparam(["num_attention_heads", "n_head"])
max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
rope_dims = n_embd // n_head
rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
rope_dims = int(rot_pct * n_embd) // n_head
# write rope scaling for long context (128k) model
rope_scaling = self.find_hparam(['rope_scaling'], True)
@ -2565,7 +2570,7 @@ class Phi3MiniModel(Model):
raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}. long_factors = {len(long_factors)}, short_factors = {len(short_factors)}.')
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))