Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
text-generation-inference
Instructions to use mlabonne/ArchBeagle-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlabonne/ArchBeagle-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlabonne/ArchBeagle-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("mlabonne/ArchBeagle-7B") model = AutoModelForMultimodalLM.from_pretrained("mlabonne/ArchBeagle-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mlabonne/ArchBeagle-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlabonne/ArchBeagle-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/ArchBeagle-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlabonne/ArchBeagle-7B
- SGLang
How to use mlabonne/ArchBeagle-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 "mlabonne/ArchBeagle-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": "mlabonne/ArchBeagle-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 "mlabonne/ArchBeagle-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": "mlabonne/ArchBeagle-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlabonne/ArchBeagle-7B with Docker Model Runner:
docker model run hf.co/mlabonne/ArchBeagle-7B
- Xet hash:
- 21c8a0263c3a0111a2a1831ea888da8b506570807b0b59e5f300bcb9162b3e20
- Size of remote file:
- 9.94 GB
- SHA256:
- a183e533267bafb76319e9eff2216ae45927af92e0f95b3dc9b54acade5c5769
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