swadeshb/Llama-3.2-3B-Instruct-AMPO-V1-6
swadeshb/Llama-3.2-3B-Instruct-AMPO-V1-6 is a 3.2 billion parameter instruction-tuned causal language model, fine-tuned from Meta's Llama-3.2-3B-Instruct. This model was trained using the GRPO method, which is designed to enhance mathematical reasoning capabilities. It is optimized for tasks requiring robust logical and mathematical problem-solving, leveraging a 32768 token context length.
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Model Overview
swadeshb/Llama-3.2-3B-Instruct-AMPO-V1-6 is a 3.2 billion parameter instruction-tuned model, building upon the meta-llama/Llama-3.2-3B-Instruct base. It was fine-tuned using the TRL library and incorporates the GRPO (Gradient-based Reward Policy Optimization) method, as introduced in the DeepSeekMath paper, to enhance its reasoning abilities.
Key Capabilities
- Enhanced Mathematical Reasoning: The primary differentiator of this model is its training with the GRPO method, specifically aimed at improving performance on mathematical and logical reasoning tasks.
- Instruction Following: As an instruction-tuned model, it is designed to understand and execute user prompts effectively.
- Large Context Window: Supports a context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model's training procedure leveraged the TRL framework (version 0.23.0) and PyTorch (version 2.8.0+cu126). The application of GRPO, a technique highlighted in the DeepSeekMath research, suggests a focus on robust problem-solving rather than general conversational fluency.
Ideal Use Cases
This model is particularly well-suited for applications requiring:
- Mathematical Problem Solving: Tasks involving arithmetic, algebra, calculus, or other quantitative reasoning.
- Logical Deduction: Scenarios where the model needs to follow complex rules or infer conclusions from given premises.
- Instruction-based Generation: General instruction-following tasks where a strong reasoning backbone is beneficial.