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README.md
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---
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license: llama3.2
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---
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license: llama3.2
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datasets:
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- HuggingFaceH4/ultrachat_200k
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base_model:
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- meta-llama/Llama-3.2-1B
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pipeline_tag: text-generation
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tags:
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- trl
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- llama
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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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# Llama-3.2-1B-ultrachat200k
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## Model Details
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- **Model type:** sft model
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- **License:** llama3.2
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- **Finetuned from model:** [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
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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`: 2e-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`: 1.726 \
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`final_train_loss`: 1.22 \
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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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TrlParser
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)
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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 = '<|end_of_text|>'
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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/Llama-3.2-1B-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": "Where is Harbin?"}],
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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: Harbin is located in northeastern China in the Heilongjiang province. It is the capital of Heilongjiang province in the Northeast Asia.
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```
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