Instructions to use MatrixC7/Meidebenne-120b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatrixC7/Meidebenne-120b-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MatrixC7/Meidebenne-120b-v1.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MatrixC7/Meidebenne-120b-v1.0") model = AutoModelForCausalLM.from_pretrained("MatrixC7/Meidebenne-120b-v1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MatrixC7/Meidebenne-120b-v1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MatrixC7/Meidebenne-120b-v1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MatrixC7/Meidebenne-120b-v1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MatrixC7/Meidebenne-120b-v1.0
- SGLang
How to use MatrixC7/Meidebenne-120b-v1.0 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 "MatrixC7/Meidebenne-120b-v1.0" \ --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": "MatrixC7/Meidebenne-120b-v1.0", "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 "MatrixC7/Meidebenne-120b-v1.0" \ --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": "MatrixC7/Meidebenne-120b-v1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MatrixC7/Meidebenne-120b-v1.0 with Docker Model Runner:
docker model run hf.co/MatrixC7/Meidebenne-120b-v1.0
- Xet hash:
- 90d12ab9941c78b69b73c0f0f34d3e8fc5e2123d0276c7e38a786d308a5bcc5e
- Size of remote file:
- 9.8 GB
- SHA256:
- 018a08598c39ced9d7cbd2756ee26c2d660692da597d3dc4e666fec2b0845caa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.