Instructions to use Pasta009/IF-CL-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pasta009/IF-CL-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pasta009/IF-CL-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pasta009/IF-CL-13B") model = AutoModelForCausalLM.from_pretrained("Pasta009/IF-CL-13B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Pasta009/IF-CL-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pasta009/IF-CL-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pasta009/IF-CL-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Pasta009/IF-CL-13B
- SGLang
How to use Pasta009/IF-CL-13B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Pasta009/IF-CL-13B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pasta009/IF-CL-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Pasta009/IF-CL-13B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pasta009/IF-CL-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Pasta009/IF-CL-13B with Docker Model Runner:
docker model run hf.co/Pasta009/IF-CL-13B
Create README.md
Browse files
README.md
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---
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license: llama2
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datasets:
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- theblackcat102/evol-codealpaca-v1
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- Pasta009/Instruction-Fusion-Code-v1
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language:
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- en
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metrics:
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- accuracy
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pipeline_tag: text-generation
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tags:
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- code
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---
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Trained with data generated by the Instruction Fusion method.
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- **Base Model:** CodeLlama-13b
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- **Seed Instructions:** theblackcat102/evol-codealpaca-v1
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- **Repository:** https://github.com/XpastaX/Instruction-Fusion
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- **Paper:** https://arxiv.org/abs/2312.15692
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**BibTeX:**
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```
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@misc{guo2024instruction,
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title={Instruction Fusion: Advancing Prompt Evolution through Hybridization},
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author={Weidong Guo and Jiuding Yang and Kaitong Yang and Xiangyang Li and Zhuwei Rao and Yu Xu and Di Niu},
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year={2024},
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eprint={2312.15692},
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archivePrefix={arXiv},
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primaryClass={cs.AI}
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}
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```
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