Instructions to use MatrixC7/Meidebenne-120b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Meidebenne-120b-v1.0 / model-00007-of-00025.safetensors

MatrixC7

Upload folder using huggingface_hub

2f01940 verified over 2 years ago

Download

History Blame Contribute Delete

9.93 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 17d8daec0be8471ffb31e9ac6221812f080fb10eda49d625a24927cf49255462
Size of remote file:: 9.93 GB
SHA256:: 6ee8a5fb903223082f4ba294241d3566a99ebd74eacab4369377a6d26b0ea52d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.