Alpaca-2
Collection
LLaMa 2 instruction tuned on cleaned Alpaca dataset using QLoRA. For languages other than English, Alpaca data was first translated using NLLB-1.3B
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32 items
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Updated
This represents the PEFT weights only. The base model is LLaMA 2 chat. Instruction finetuning was done using 4 bit QLoRA on a single A100 GPU with the PEFT config as given below. The dataset used for this instruction finetuning process is the cleaned alpaca dataset.
Do note that this model might have inferior performance on some specific tasks compared to full finetuning or a different base model trained with more specific data.
Training procedure
The following bitsandbytes quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.4.0
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