Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
liminerity/Bitnet-Mistral.0.2-v3
liminerity/Bitnet-Mistral.0.2-v2
text-generation-inference
Instructions to use gate369/Bitnet-Mistral-1-and-2-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gate369/Bitnet-Mistral-1-and-2-slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gate369/Bitnet-Mistral-1-and-2-slerp")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gate369/Bitnet-Mistral-1-and-2-slerp") model = AutoModelForCausalLM.from_pretrained("gate369/Bitnet-Mistral-1-and-2-slerp") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gate369/Bitnet-Mistral-1-and-2-slerp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gate369/Bitnet-Mistral-1-and-2-slerp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gate369/Bitnet-Mistral-1-and-2-slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gate369/Bitnet-Mistral-1-and-2-slerp
- SGLang
How to use gate369/Bitnet-Mistral-1-and-2-slerp 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 "gate369/Bitnet-Mistral-1-and-2-slerp" \ --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": "gate369/Bitnet-Mistral-1-and-2-slerp", "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 "gate369/Bitnet-Mistral-1-and-2-slerp" \ --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": "gate369/Bitnet-Mistral-1-and-2-slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gate369/Bitnet-Mistral-1-and-2-slerp with Docker Model Runner:
docker model run hf.co/gate369/Bitnet-Mistral-1-and-2-slerp
- Xet hash:
- 1afd01a416cddb59a5495d49b180b3f3cc8a482ab7ea73b919807016c2667e17
- Size of remote file:
- 64.3 MB
- SHA256:
- df874a02fedc95eeccb9e35d7074c51603869b74d26803e0f15eb1d138656471
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