jamesesqueleto/ornith_9b_enhancedreasoning

Model Overview

The jamesesqueleto/ornith_9b_enhancedreasoning model is a 9 billion parameter language model, fine-tuned from the Ornith 9B base architecture. Its primary objective is to significantly enhance the reasoning capabilities of the original model, making it more proficient in complex problem-solving scenarios.

Key Capabilities

This model has been specifically trained to excel in tasks requiring advanced reasoning, including:

• Mathematical problem-solving: Handling numerical and logical math challenges.
• Code generation and analysis: Understanding and generating programming logic.
• Tool-use scenarios: Interpreting and executing instructions for external tools.
• Multi-step problem solving: Breaking down and solving problems that require sequential logical steps.

Training Details

The model was fine-tuned on the SupraLabs/reasoning-summaries-61k dataset, which comprises 61,000 samples of reasoning traces paired with structured summaries. This dataset covers a diverse range of reasoning types, contributing to the model's enhanced logical deduction skills. The training involved a learning rate of 5e-5, a cosine LR scheduler, and ran for 3 epochs with a cutoff length of 10200 tokens. The loss showed a steady decline, indicating effective learning without divergence.

Usage and Format

The model is released as BF16 safetensors checkpoints, compatible with standard transformers-based loading pipelines. Developers can easily integrate it using AutoModelForCausalLM and AutoTokenizer from the Hugging Face transformers library. It maintains the general capabilities and limitations of the base Ornith 9B model, with a focused improvement on reasoning.