loafeihong/llama-2-7B-factory-MetaMathQA-Muon-stage2
The loafeihong/llama-2-7B-factory-MetaMathQA-Muon-stage2 is a 7 billion parameter Llama-2-7b-chat-hf model fine-tuned on the MetaMath dataset. This model is specifically optimized for mathematical reasoning and problem-solving tasks. It leverages its Llama-2 base architecture to provide enhanced performance in quantitative domains. The primary use case for this model is to address complex mathematical queries and generate accurate solutions.
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Model Overview
This model, loafeihong/llama-2-7B-factory-MetaMathQA-Muon-stage2, is a specialized fine-tuned version of the Meta Llama 2 7B Chat model. It has been specifically adapted for mathematical reasoning tasks through training on the MetaMath dataset.
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-2-7b-chat-hf. - Parameter Count: 7 billion parameters.
- Optimization Focus: Enhanced for mathematical problem-solving and quantitative reasoning.
Training Details
The model was trained with the following key hyperparameters:
- Learning Rate: 1e-05
- Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.999) and epsilon=1e-08
- LR Scheduler: Cosine type with a warmup ratio of 0.1
- Epochs: 2.0
- Total Batch Size: 16 (achieved with
train_batch_size=1,gradient_accumulation_steps=2, andnum_devices=8)
Intended Use Cases
This model is particularly well-suited for applications requiring:
- Solving mathematical problems.
- Generating explanations for mathematical concepts.
- Assisting in quantitative analysis tasks.
Limitations
As noted in the original model card, further information regarding specific limitations and broader intended uses is needed. Users should evaluate its performance thoroughly for their specific mathematical applications.