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	update examples
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				@ -94,10 +94,10 @@ FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.
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### QLoRA Fine-Tuning
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#### Supervised Fine-Tuning with 4/8-bit Bitsandbytes Quantization (Recommended)
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#### Supervised Fine-Tuning with 4/8-bit Bitsandbytes/HQQ/EETQ Quantization (Recommended)
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```bash
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_bitsandbytes.yaml
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
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```
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#### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization
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@ -94,10 +94,10 @@ FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.
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### QLoRA 微调
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#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调(推荐)
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#### 基于 4/8 比特 Bitsandbytes/HQQ/EETQ 量化进行指令监督微调(推荐)
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```bash
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_bitsandbytes.yaml
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llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
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```
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#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
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@ -7,7 +7,7 @@ do_train: true
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finetuning_type: lora
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lora_target: all
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pref_beta: 0.1
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pref_loss: sigmoid  # [sigmoid (dpo), orpo, simpo]
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pref_loss: sigmoid  # choices: [sigmoid (dpo), orpo, simpo]
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### dataset
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dataset: dpo_en_demo
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@ -1,6 +1,7 @@
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### model
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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quantization_bit: 4
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quantization_method: bitsandbytes  # choices: [bitsandbytes (4/8), hqq (2/3/4/5/6/8), eetq (8)]
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### method
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stage: sft
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