Instructions to use noystl/mistral-e2e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noystl/mistral-e2e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="noystl/mistral-e2e")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("noystl/mistral-e2e", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use noystl/mistral-e2e with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "noystl/mistral-e2e" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "noystl/mistral-e2e", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/noystl/mistral-e2e
- SGLang
How to use noystl/mistral-e2e 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 "noystl/mistral-e2e" \ --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": "noystl/mistral-e2e", "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 "noystl/mistral-e2e" \ --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": "noystl/mistral-e2e", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use noystl/mistral-e2e with Docker Model Runner:
docker model run hf.co/noystl/mistral-e2e
Add library_name and pipeline_tag metadata
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datasets:
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- noystl/Recombination-Extraction
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language:
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- en
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This Hugging Face repository contains a fine-tuned Mistral model trained for the task of extracting recombination examples from scientific abstracts, as described in the paper [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779). The model utilizes a LoRA adapter on top of a Mistral base model.
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The model can be used for the information extraction task of identifying recombination examples within scientific text. For detailed usage instructions and reproduction of results, please refer to the Github repository linked above.
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**Bibtex**
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```bibtex
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@misc{sternlicht2025chimeraknowledgebaseidea,
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base_model:
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- mistralai/Mistral-7B-Instruct-v0.3
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datasets:
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- noystl/Recombination-Extraction
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language:
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- en
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license: cc
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library_name: transformers
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pipeline_tag: feature-extraction
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This Hugging Face repository contains a fine-tuned Mistral model trained for the task of extracting recombination examples from scientific abstracts, as described in the paper [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779). The model utilizes a LoRA adapter on top of a Mistral base model.
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The model can be used for the information extraction task of identifying recombination examples within scientific text. For detailed usage instructions and reproduction of results, please refer to the Github repository linked above.
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**Bibtex**
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```bibtex
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@misc{sternlicht2025chimeraknowledgebaseidea,
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