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---
tags:
- merge
- parameter_wise
- llm-adamerge
base_model: mistralai/Mistral-7B-v0.1
---
# Merged Model using LLM-AdaMerge (parameter_wise)
This model was created by merging multiple fine-tuned models using the LLM-AdaMerge approach with parameter_wise merging.
## Merge Details
- **Merge Type**: parameter_wise
- **Base Model**: mistralai/Mistral-7B-v0.1
- **Number of Models Merged**: 3
- **Models Merged**: instruct, math, code
- **Final Training Loss**: N/A
- **Training Epochs**: 0
## Lambda Coefficients
The following lambda coefficients were learned during training:
### Parameter-wise Lambdas
This model uses parameter-wise lambda coefficients. Total parameters with individual lambdas: 291
See the uploaded `learned_lambdas.json` file for detailed parameter-wise coefficients.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/model-name")
tokenizer = AutoTokenizer.from_pretrained("your-username/model-name")
# Use the model
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
## Training Configuration
See the uploaded `training_config.json` file for detailed training configuration.
## Citation
If you use this model, please cite the LLM-AdaMerge paper:
```bibtex
@article{llmadamerge2024,
title={LLM-AdaMerge: Adaptive Model Merging for Large Language Models},
author={...},
year={2024}
}
```
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