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Information
Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.Quantized using --true-sequential and --act-order optimizations.
This was made using Chansung's 30B Alpaca Lora: https://huggingface.co/chansung/alpaca-lora-30bUpdate 04.06.2023
This is a more recent merge of Chansung's Alpaca Lora which was updated using the clean alpaca dataset as of 04/06/2023 with refined training parameters
Training Parameters
- num_epochs=10
- cutoff_len=512
- group_by_length
- lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'
- lora_r=16
- micro_batch_size=8
Benchmarks
Wikitext2: 4.608365058898926
Ptb-New: 8.69663143157959
C4-New: 6.624773979187012
Note: This version does not use --groupsize 128, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
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Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "MetaIX/Alpaca-30B-Int4"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MetaIX/Alpaca-30B-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'