juliensimon/llama2-7b-qlora-openassistant-guanaco

This model was fine-tuned using 4-bit QLoRa, following the instructions in https://huggingface.co/blog/llama2#fine-tuning-with-peft.

The dataset includes 10k prompts.

I used a Amazon EC2 g5.xlarge instance (1xA10G GPU), with the Deep Learning AMI for PyTorch. Training time was about 10 hours. On-demand price is about $10, which can easily be reduced to about $3 with EC2 Spot Instances.

The full log is included, as well as a simple inference script.

Training procedure

The following bitsandbytes quantization config was used during training:

• —quant_method: bitsandbytes
• —loadin8bit: False
• —loadin4bit: True
• —llmint8threshold: 6.0
• —llmint8skip_modules: None
• —llmint8enablefp32cpu_offload: False
• —llmint8hasfp16weight: False
• —bnb4bitquant_type: fp4
• —bnb4bitusedoublequant: False
• —bnb4bitcompute_dtype: float32

Framework versions

• —PEFT 0.5.0