Instructions to use MatLumber/Bisho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatLumber/Bisho with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatLumber/Bisho") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("MatLumber/Bisho") model = AutoModelForMultimodalLM.from_pretrained("MatLumber/Bisho") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatLumber/Bisho with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatLumber/Bisho" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatLumber/Bisho", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MatLumber/Bisho
- SGLang
How to use MatLumber/Bisho 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 "MatLumber/Bisho" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatLumber/Bisho", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "MatLumber/Bisho" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatLumber/Bisho", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MatLumber/Bisho with Docker Model Runner:
docker model run hf.co/MatLumber/Bisho
Upload 10 files
Browse files- config.json +1 -1
- eval_results.txt +1 -1
- generation_config.json +6 -0
- merges.txt +1 -1
- pytorch_model.bin +2 -2
- tokenizer.json +9 -5
- tokenizer_config.json +1 -2
- training_args.bin +2 -2
config.json
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eval_results.txt
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generation_config.json
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merges.txt
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tokenizer.json
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