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chore: make the Deepseek example compatible with Yi models. (#205)

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* Update convert.py

* Update convert.py

* Update deepseek_coder.py
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Anchen 2023-12-31 01:11:33 +11:00 committed by GitHub
parent 581a5733a1
commit ee3c44d231
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2 changed files with 4 additions and 3 deletions
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5
llms/deepseek-coder/convert.py
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@ -44,7 +44,7 @@ def convert(args):
config = model.config.to_dict()
state_dict = model.state_dict()
tokenizer = AutoTokenizer.from_pretrained(str(hf_path), trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(str(hf_path), trust_remote_code=True, use_fast=False)
# things to change
# 1. there's no "model." in the weight names
@ -84,7 +84,7 @@ def convert(args):
weights = {k: v.numpy() for k, v in state_dict.items()}
config["rope_scaling_factor"] = config["rope_scaling"]["factor"]
config["rope_scaling_factor"] = config["rope_scaling"]["factor"] if config["rope_scaling"] is not None else 1.0
keep_keys = set(
[
"vocab_size",
@ -96,6 +96,7 @@ def convert(args):
"rms_norm_eps",
"intermediate_size",
"rope_scaling_factor",
"rope_theta"
]
)
for k in list(config.keys()):

2
llms/deepseek-coder/deepseek_coder.py
View File

@ -248,7 +248,7 @@ def load_model(model_path: str):
nn.QuantizedLinear.quantize_module(model, **quantization)
model.update(tree_unflatten(list(weights.items())))
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
return model, tokenizer