How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "noirchan/Qwen2.5-Coder-7B_math_mergeTIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "noirchan/Qwen2.5-Coder-7B_math_mergeTIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/noirchan/Qwen2.5-Coder-7B_math_mergeTIES
Quick Links

merged_model

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 Qwen/Qwen2.5-7B-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: Qwen/Qwen2.5-Coder-7B-Instruct
    #no parameters necessary for base model
  - model: Qwen/Qwen2.5-Math-7B-Instruct
    parameters:
      density: 0.5
      weight: 0.5
  - model: Qwen/Qwen2.5-Coder-7B-Instruct
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: Qwen/Qwen2.5-7B-Instruct
parameters:
  normalize: false
  int8_mask: true
dtype: float16
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Safetensors
Model size
8B params
Tensor type
F16
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