Instructions to use jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/home/shadeform/CoPaw-Flash-9B") model = PeftModel.from_pretrained(base_model, "jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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```bash
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export HF_TOKEN=your_huggingface_token
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CUDA_VISIBLE_DEVICES=0,1 vllm serve agentscope-ai/
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--enable-lora \
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--lora-modules agent-lora=jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA \
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--max-lora-rank 64 \
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## Acknowledgments
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- [CoPaw-Flash-9B](https://huggingface.co/agentscope-ai/
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- [Brev.dev](https://brev.nvidia.com/) — GPU cloud infrastructure by NVIDIA
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- [LocoreMind](https://locoremind.com/) — Research and development
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```bash
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export HF_TOKEN=your_huggingface_token
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CUDA_VISIBLE_DEVICES=0,1 vllm serve agentscope-ai/QwenPaw-Flash-9B \
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--enable-lora \
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--lora-modules agent-lora=jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA \
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--max-lora-rank 64 \
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## Acknowledgments
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- [CoPaw-Flash-9B](https://huggingface.co/agentscope-ai/QwenPaw-Flash-9B) — Base model by AgentScope AI
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- [Brev.dev](https://brev.nvidia.com/) — GPU cloud infrastructure by NVIDIA
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- [LocoreMind](https://locoremind.com/) — Research and development
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