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
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
Framework versions
- PEFT 0.5.0
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Model tree for juliensimon/llama2-7b-qlora-openassistant-guanaco
Base model
meta-llama/Llama-2-7b-hf