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LLaMA-Factory-Mirror
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fix #1659

This commit is contained in:
hiyouga 2023-11-28 20:52:28 +08:00
parent c2d4300ac4
commit 475a3fa0f4
4 changed files with 12 additions and 6 deletions
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9
src/llmtuner/extras/constants.py
View File

@ -217,7 +217,11 @@ register_model_group(
"Qwen-7B": "Qwen/Qwen-7B",
"Qwen-14B": "Qwen/Qwen-14B",
"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat"
"Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
"Qwen-7B-int8-Chat": "Qwen/Qwen-7B-Chat-Int8",
"Qwen-7B-int4-Chat": "Qwen/Qwen-7B-Chat-Int4",
"Qwen-14B-int8-Chat": "Qwen/Qwen-14B-Chat-Int8",
"Qwen-14B-int4-Chat": "Qwen/Qwen-14B-Chat-Int4"
},
module="c_attn",
template="qwen"
@ -266,8 +270,7 @@ register_model_group(
"Yi-6B": "01-ai/Yi-6B",
"Yi-34B": "01-ai/Yi-34B",
"Yi-34B-Chat": "01-ai/Yi-34B-Chat",
"Yi-34B-int8-Chat": "01-ai/Yi-34B-Chat-8bits",
"Yi-34B-int4-Chat": "01-ai/Yi-34B-Chat-4bits"
"Yi-34B-int8-Chat": "01-ai/Yi-34B-Chat-8bits"
},
template="yi"
)

2
src/llmtuner/hparams/finetuning_args.py
View File

@ -179,7 +179,7 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments):
raise ValueError("Reward model is necessary for PPO training.")
if self.stage == "ppo" and self.reward_model_type == "lora" and self.finetuning_type != "lora":
raise ValueError("Lora reward model only supports lora training.")
raise ValueError("Freeze/Full PPO training needs `reward_model_type=full`.")
def save_to_json(self, json_path: str):
r"""Saves the content of this instance in JSON format inside `json_path`."""

3
src/llmtuner/model/loader.py
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@ -146,6 +146,9 @@ def load_model_and_tokenizer(
# Quantization configurations (using bitsandbytes library)
if model_args.quantization_bit is not None:
if getattr(config, "quantization_config", None):
raise ValueError("Remove `quantization_bit` if you are using a quantized model.")
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")

4
src/llmtuner/train/tuner.py
View File

@ -38,8 +38,8 @@ def export_model(args: Optional[Dict[str, Any]] = None):
model_args, _, finetuning_args, _ = get_infer_args(args)
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
if getattr(model, "quantization_method", None) == "gptq":
raise ValueError("Cannot export a GPTQ quantized model.")
if getattr(model, "quantization_method", None) in ["gptq", "awq"]:
raise ValueError("Cannot export a GPTQ or AWQ quantized model.")
model.config.use_cache = True
model.save_pretrained(finetuning_args.export_dir, max_shard_size="{}GB".format(finetuning_args.export_size))