How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mergekit-community/mergekit-ties-cbdfmuk"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mergekit-community/mergekit-ties-cbdfmuk",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/mergekit-community/mergekit-ties-cbdfmuk
Quick Links

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using OpenLLM-Ro/RoMistral-7b-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: OpenLLM-Ro/RoMistral-7b-Instruct
    parameters:
      density: 0.7
      weight: 0.7
  - model: mistralai/Mistral-7B-Instruct-v0.3

    parameters:
      density: 0.3
      weight: 0.3

merge_method: ties
base_model: OpenLLM-Ro/RoMistral-7b-Instruct

parameters:
  normalize: false
  int8_mask: true
dtype: float16
Downloads last month
1
Safetensors
Model size
7B params
Tensor type
F16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for mergekit-community/mergekit-ties-cbdfmuk

Paper for mergekit-community/mergekit-ties-cbdfmuk