Text Generation
Transformers
Safetensors
English
mistral
code
chemistry
medical
text-generation-inference
Instructions to use Locutusque/NeuralHyperion-2.0-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Locutusque/NeuralHyperion-2.0-Mistral-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Locutusque/NeuralHyperion-2.0-Mistral-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Locutusque/NeuralHyperion-2.0-Mistral-7B") model = AutoModelForCausalLM.from_pretrained("Locutusque/NeuralHyperion-2.0-Mistral-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Locutusque/NeuralHyperion-2.0-Mistral-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Locutusque/NeuralHyperion-2.0-Mistral-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Locutusque/NeuralHyperion-2.0-Mistral-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Locutusque/NeuralHyperion-2.0-Mistral-7B
- SGLang
How to use Locutusque/NeuralHyperion-2.0-Mistral-7B 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 "Locutusque/NeuralHyperion-2.0-Mistral-7B" \ --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": "Locutusque/NeuralHyperion-2.0-Mistral-7B", "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 "Locutusque/NeuralHyperion-2.0-Mistral-7B" \ --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": "Locutusque/NeuralHyperion-2.0-Mistral-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Locutusque/NeuralHyperion-2.0-Mistral-7B with Docker Model Runner:
docker model run hf.co/Locutusque/NeuralHyperion-2.0-Mistral-7B
Commit History
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Add AWQ quant links (#3)
Verified signature- Add AWQ quant links (1a3d9f5cd6bb6861ee67ba6164817cbd2ad6b1f0) Co-authored-by: Joseph David Memphries <Kronikus@users.noreply.huggingface.co>d9c137c
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Add Quants (#2)
Verified signature- Add Quants (33a4e0f5e5a43d25d587c8426a2f8fb35dc95321) Co-authored-by: Bartowski <bartowski@users.noreply.huggingface.co>dce6074 -
Update README.md
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Upload MistralForCausalLM (#1)
Verified signature- Upload MistralForCausalLM (6900ad7fd93079a22c3c268bf656cfadf510552b)641abb2 -
Upload tokenizer
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initial commit
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