Text Generation
Transformers
Safetensors
English
mistral
code
chemistry
medical
text-generation-inference
Instructions to use Locutusque/Hyperion-2.0-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Locutusque/Hyperion-2.0-Mistral-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Locutusque/Hyperion-2.0-Mistral-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Locutusque/Hyperion-2.0-Mistral-7B") model = AutoModelForCausalLM.from_pretrained("Locutusque/Hyperion-2.0-Mistral-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Locutusque/Hyperion-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/Hyperion-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/Hyperion-2.0-Mistral-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Locutusque/Hyperion-2.0-Mistral-7B
- SGLang
How to use Locutusque/Hyperion-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/Hyperion-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/Hyperion-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/Hyperion-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/Hyperion-2.0-Mistral-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Locutusque/Hyperion-2.0-Mistral-7B with Docker Model Runner:
docker model run hf.co/Locutusque/Hyperion-2.0-Mistral-7B
Commit History
-
Adding AWQ Quant (#3)
Verified signature- Adding AWQ Quant (3cf097b068114e9c920f3b3596d559800d1e8242) Co-authored-by: ParmyJack <ParmyJack@users.noreply.huggingface.co>90e310e
-
Update README.md
Verified signature9e02bc8 -
Update README.md (#2)
Verified signature- Update README.md (ef1f47f6f01cf56685f9c23d2076fe2fe99e08a3) Co-authored-by: Bartowski <bartowski@users.noreply.huggingface.co>f2f9b34
-
Update README.md
Verified signature2947447 -
Update README.md
Verified signaturec12978b -
Update README.md
Verified signature4069b74 -
Update README.md
Verified signature76f585c -
Update README.md
Verified signature3a64f6e -
Update README.md
Verified signature966bad6 -
Upload MistralForCausalLM (#1)
Verified signature- Upload MistralForCausalLM (eaa6f74e59843302fa4babcbb8e64e866f77d353)3e9f665 -
Upload tokenizer
Verified signature97a6dbb -
initial commit
Verified signatureb2e3943