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lejelly/
test-size-dataset-task-wise-llm-adamerge-crossentropy-mistral-7b-instrcut-math-code

Name: test-size-dataset-task-wise-llm-adamerge-crossentropy-mistral-7b-instrcut-math-code
Availability: InStock

mistralai/Mistral-7B-v0.1

finetune

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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: N/A

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}
}

lejelly/test-size-dataset-task-wise-llm-adamerge-crossentropy-mistral-7b-instrcut-math-code