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release v0.5.3

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hiyouga
2024-02-29 00:34:19 +08:00
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README.md
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@@ -42,9 +42,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
- **Scalable resources**: 32-bit full-tuning, 16-bit freeze tuning, 16-bit LoRA tuning, 2/4/8-bit QLoRA with AQLM/AWQ/GPTQ/LLM.int8.
- **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA, 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
- **Advanced algorithms**: DoRA, LongLoRA, LLaMA Pro, LoftQ, agent tuning.
- **Intriguing tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune, rsLoRA.
- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune, rsLoRA.
- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
## Benchmark
@@ -140,7 +140,7 @@ Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list
## Supported Training Approaches
| Approach | Full-parameter | Partial-parameter | LoRA | QLoRA |
| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA |
| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |