How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/llama-3-stella-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": "nbeerbower/llama-3-stella-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/nbeerbower/llama-3-stella-8B
Quick Links

llama-3-stella-8B

This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT

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 nbeerbower/llama-3-bophades-v3-8B 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: nbeerbower/llama-3-bible-dpo-8B
    - model: nbeerbower/llama-3-wissenschaft-8B-v2
    - model: nbeerbower/llama-3-gutenberg-8B
    - model: nbeerbower/llama-3-dragonmaid-8B-v2
    - model: bunnycore/Cognitron-8B
    - model: lodrick-the-lafted/Olethros-8B
    - model: theo77186/Llama-3-8B-Instruct-norefusal
    - model: openlynn/Llama-3-Soliloquy-8B-v2
merge_method: model_stock
base_model: nbeerbower/llama-3-bophades-v3-8B
dtype: bfloat16

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