Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
teknium/OpenHermes-2.5-Mistral-7B
mistralai/Mistral-7B-v0.1
text-generation-inference
Instructions to use codegood/MistHermes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codegood/MistHermes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codegood/MistHermes")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codegood/MistHermes") model = AutoModelForCausalLM.from_pretrained("codegood/MistHermes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codegood/MistHermes with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codegood/MistHermes" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codegood/MistHermes", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codegood/MistHermes
- SGLang
How to use codegood/MistHermes 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 "codegood/MistHermes" \ --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": "codegood/MistHermes", "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 "codegood/MistHermes" \ --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": "codegood/MistHermes", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codegood/MistHermes with Docker Model Runner:
docker model run hf.co/codegood/MistHermes
- Xet hash:
- d6faf3ab141ad786b662b5897a6d2697b38a7ff7628c39df89eb649c24415b8c
- Size of remote file:
- 872 MB
- SHA256:
- 05d818b09e974a59d62e21c20d13fb37516faf386f8bd0258a04c01d5ecf669e
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