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/L3.1-Athena-h-8B"
# 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/L3.1-Athena-h-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/mergekit-community/L3.1-Athena-h-8B
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 Model Stock merge method using meta-llama/Llama-3.1-8B-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:

out_dtype: bfloat16
merge_method: model_stock
base_model: meta-llama/Llama-3.1-8B-Instruct
models:
  - model: mergekit-community/L3.1-Athena-a-8B
    parameters:
      weight: 0.1
  - model: mergekit-community/L3.1-Athena-b-8B
    parameters:
      weight: 0.2
  - model: mergekit-community/L3.1-Athena-c-8B
    parameters:
      weight: 0.4
  - model: mergekit-community/L3.1-Athena-d-8B
    parameters:
      weight: 0.4
  - model: AtlaAI/Selene-1-Mini-Llama-3.1-8B
    parameters:
      weight: 0.6
  - model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
    parameters:
      weight: 0.6
  - model: DavidAU/DeepSeek-BlackRoot-R1-Distill-Llama-3.1-8B
    parameters:
      weight: 0.8
  - model: Skywork/Skywork-o1-Open-Llama-3.1-8B
    parameters:
      weight: 0.8
  - model: meta-llama/Llama-3.1-8B
    parameters:
      weight: 0.8
  - model: mergekit-community/L3.1-Athena-e-8B
    parameters:
      weight: 0.8
  - model: DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B
  - model: mergekit-community/L3.1-Athena-f-8B
  - model: mergekit-community/L3.1-Athena-g-8B
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Model size
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Tensor type
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