初始化项目，由ModelHub XC社区提供模型
Model: AIR-hl/Qwen2.5-1.5B-ultrachat200k Source: Original Platform
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.ipynb_checkpoints/README-checkpoint.md
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---
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license: apache-2.0
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---
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.ipynb_checkpoints/merges-checkpoint.txt
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README.md
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---
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license: apache-2.0
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datasets:
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- HuggingFaceH4/ultrachat_200k
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base_model:
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- Qwen/Qwen2.5-1.5B
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pipeline_tag: text-generation
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tags:
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- trl
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- qwen
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- sft
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- alignment
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- transformers
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- custome
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- chat
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---
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# Qwen2.5-1.5B-ultrachat200k
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## Model Details
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- **Model type:** sft model
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- **License:** Apache license 2.0
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- **Finetuned from model:** [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B)
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- **Training data:** [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
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- **Training framework:** [trl](https://github.com/huggingface/trl)
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## Training Details
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### Training Hyperparameters
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`attn_implementation`: flash_attention_2 \
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`bf16`: True \
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`learning_rate`: 5e-5 \
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`lr_scheduler_type`: cosine \
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`per_device_train_batch_size`: 2 \
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`gradient_accumulation_steps`: 16 \
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`torch_dtype`: bfloat16 \
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`num_train_epochs`: 1 \
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`max_seq_length`: 2048 \
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`warmup_ratio`: 0.1
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### Results
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`init_train_loss`: 1.421 \
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`final_train_loss`: 1.192 \
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`eval_loss`: 1.2003
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### Training script
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```python
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import multiprocessing
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from datasets import load_dataset
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from tqdm.rich import tqdm
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from trl import (
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ModelConfig,
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SFTTrainer,
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get_peft_config,
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get_quantization_config,
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get_kbit_device_map,
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SFTConfig,
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ScriptArguments
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)
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from trl.commands.cli_utils import TrlParser
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tqdm.pandas()
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if __name__ == "__main__":
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parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
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args, training_args, model_config = parser.parse_args_and_config()
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quantization_config = get_quantization_config(model_config)
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model_kwargs = dict(
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revision=model_config.model_revision,
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trust_remote_code=model_config.trust_remote_code,
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attn_implementation=model_config.attn_implementation,
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torch_dtype=model_config.torch_dtype,
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use_cache=False if training_args.gradient_checkpointing else True,
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device_map=get_kbit_device_map() if quantization_config is not None else None,
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quantization_config=quantization_config,
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)
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model = AutoModelForCausalLM.from_pretrained(model_config.model_name_or_path,
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**model_kwargs)
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tokenizer = AutoTokenizer.from_pretrained(
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model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, use_fast=True
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)
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tokenizer.pad_token = tokenizer.eos_token
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train_dataset = load_dataset(args.dataset_name,
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split=args.dataset_train_split,
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num_proc=multiprocessing.cpu_count())
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trainer = SFTTrainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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processing_class=tokenizer,
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peft_config=get_peft_config(model_config),
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)
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trainer.train()
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trainer.save_model(training_args.output_dir)
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```
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### Test Script
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```python
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from vllm import LLM
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from datasets import load_dataset
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from vllm.sampling_params import SamplingParams
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from transformers import AutoTokenizer
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MODEL_PATH = "autodl-tmp/saves/Qwen2.5-1.5B-ultrachat200k"
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model = LLM(MODEL_PATH,
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tensor_parallel_size=1,
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dtype='bfloat16')
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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input = tokenizer.apply_chat_template([{"role": "user", "content": "Which province is Shenyang in?"}],
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tokenize=False,
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add_generation_prompt=True)
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sampling_params = SamplingParams(max_tokens=1024,
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temperature=0.7,
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logprobs=1,
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stop_token_ids=[tokenizer.eos_token_id])
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vllm_generations = model.generate(input,
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sampling_params)
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print(vllm_generations[0].outputs[0].text)
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# print result: Shenyang is in Liaoning province, China.
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```
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config.json
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
4584
trainer_state.json
Normal file
4584
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:00cc9c46e847b9127d87bf6defbe023076c4a6963e7f86ab55873819165fbbb2
|
||||
size 5624
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user