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--- |
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pipeline_tag: text-generation |
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library_name: peft |
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base_model: meta-llama/Llama-2-7b-hf |
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tags: |
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- pytorch |
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- llama-2 |
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datasets: |
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- timdettmers/openassistant-guanaco |
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--- |
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This model was fine-tuned using 4-bit QLoRa, following the instructions in https://huggingface.co/blog/llama2#fine-tuning-with-peft. |
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The dataset includes 10k prompts. |
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I used a Amazon EC2 g5.xlarge instance (1xA10G GPU), with the Deep Learning AMI for PyTorch. |
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Training time was about 10 hours. On-demand price is about $10, which can easily be reduced to about $3 with EC2 Spot Instances. |
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The full log is included, as well as a simple inference script. |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: fp4 |
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- bnb_4bit_use_double_quant: False |
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- bnb_4bit_compute_dtype: float32 |
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### Framework versions |
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- PEFT 0.5.0 |
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