Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper • 2203.05482 • Published • 8
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: MrRobotoAI/X1+Samhita-kolluri/mistral-paper-critique-lora
- model: MrRobotoAI/Huldra-R1-v2.2-8b-DEEPSEEK
parameters:
weight: 1.0
merge_method: linear
normalize: true
dtype: float16
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# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "MrRobotoAI/X2"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrRobotoAI/X2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'