metadata
tags:
- merge
- task_wise
- llm-adamerge
base_model: deepseek-ai/deepseek-coder-7b-base-v1.5
Merged Model using LLM-AdaMerge (task_wise)
This model was created by merging multiple fine-tuned models using the LLM-AdaMerge approach with task_wise merging.
Merge Details
- Merge Type: task_wise
- Base Model: deepseek-ai/deepseek-coder-7b-base-v1.5
- Number of Models Merged: 2
- Models Merged: math, code
- Final Training Loss: N/A
- Training Epochs: 0
Lambda Coefficients
The following lambda coefficients were learned during training:
Task-wise lambda coefficients are stored in the learned_lambdas.json file.
Usage
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:
@article{llmadamerge2024,
title={LLM-AdaMerge: Adaptive Model Merging for Large Language Models},
author={...},
year={2024}
}