84 lines
2.0 KiB
Markdown
84 lines
2.0 KiB
Markdown
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- text-generation
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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---
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## Llama-3-8B-Agent
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This Adapter is fine-tune from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
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### Environment
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LLaMA-Factory Commit Version: **db7f3b9784d21ef5c18a11679ad995bb97d61f7c**
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GPU RTX-4090 24G 单卡
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Python 310
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### Training hyperparameters
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**Please ensure [FA2](https://github.com/Dao-AILab/flash-attention) installed**
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage sft \
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--do_train \
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--model_name_or_path /data/models/Meta-Llama-3-8B-Instruct \
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--dataset alpaca_gpt4_zh,glaive_toolcall \
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--dataset_dir data \
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--template llama3 \
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--finetuning_type lora \
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--lora_target all \
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--output_dir saves/LLaMA3-8B/lora/sft \
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--overwrite_cache \
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--overwrite_output_dir \
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--cutoff_len 8192 \
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--preprocessing_num_workers 16 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 2 \
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--gradient_accumulation_steps 8 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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--warmup_steps 20 \
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--save_steps 1000 \
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--eval_steps 1000 \
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--max_samples 6000 \
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--evaluation_strategy steps \
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--load_best_model_at_end \
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--learning_rate 5e-6 \
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--num_train_epochs 3.0 \
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--val_size 0.1 \
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--plot_loss \
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--fp16 \
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--flash_attn
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```
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### training loss
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### example
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