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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Meidebenne-120b-v1.0

This is a Frankenstein merge of sophosympatheia/Midnight-Rose-70B-v1.0 created using mergekit.

Hence, the name of this merged model, 'Meidebenne', is inspired by a variety of the deeper Midnight-Rose species.

For detailed instructions, it is recommended to refer to the original page of the sophosympatheia/Midnight-Rose-70B-v1.0 repository.

Merge Details

Merge Method

This model was merged using the passthrough merge method, which is exactly copied from nsfwthrowitaway69/Venus-120b-v1.2 / cognitivecomputations/MegaDolphin-120b.

Models Merged

The following models were included in the merge:

sophosympatheia/Midnight-Rose-70B-v1.0

Configuration

The following YAML configuration was used to produce this model:

dtype: float16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 20]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"
- sources:
  - layer_range: [10, 30]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"
- sources:
  - layer_range: [20, 40]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"
- sources:
  - layer_range: [30, 50]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"
- sources:
  - layer_range: [40, 60]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"
- sources:
  - layer_range: [50, 70]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"
- sources:
  - layer_range: [60, 80]
    model: "sophosympatheia/Midnight-Rose-70B-v1.0"

Downloads last month: 8

Safetensors

Model size

120B params

Tensor type

F16

Model tree for MatrixC7/Meidebenne-120b-v1.0

Quantizations

2 models