## Model Composition

- `NurtureAI/neural-chat-7b-v3-16k`: Weight - 30%
- `xDAN-AI/xDAN-L1-Chat-RL-v1`: Weight - 30%
- `rwitz/go-bruins-v2`: Weight - 30%
- `segmed/MedMistral-7B-v0.1`: Weight - 10%

### Code Snippet for Model Merging
The following Python code demonstrates how to create this mixed model using the LM-Cocktail approach:

```python
from LM_Cocktail import mix_models_by_layers

model = mix_models_by_layers(
    model_names_or_paths=[
        "NurtureAI/neural-chat-7b-v3-16k", 
        "xDAN-AI/xDAN-L1-Chat-RL-v1", 
        "rwitz/go-bruins-v2", 
        "segmed/MedMistral-7B-v0.1"
    ],
    model_type='decoder', 
    weights=[0.3, 0.3, 0.3, 0.1],
    output_path='./mixed_llm'
)
```
---
license: apache-2.0
---
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers"}
transformers safetensors mistral text-generation en license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us