ermiaazarkhalili/Qwen3.5-9B-SFT-Claude-Opus-Reasoning-Unsloth

Model Overview

This model, ermiaazarkhalili/Qwen3.5-9B-SFT-Claude-Opus-Reasoning-Unsloth, is a 9.7 billion parameter language model based on the unsloth/Qwen3.5-9B architecture. It has been supervised fine-tuned (SFT) using LoRA (Low-Rank Adaptation) on a private dataset, ermiaazarkhalili/claude-reasoning-distillation, specifically configured for SFT.

Key Characteristics

• Base Model: Built on unsloth/Qwen3.5-9B.
• Parameters: Contains 9.7 billion parameters.
• Fine-tuning: Utilizes LoRA with a rank of 64 and alpha of 64, trained for 1 epoch with a learning rate of 0.0002.
• Training Data: Fine-tuned on a private dataset derived from Claude's reasoning capabilities, suggesting an optimization for complex reasoning tasks.
• Methodology: Training was performed using Unsloth and TRL, with 4-bit QLoRA precision and a maximum sequence length of 2048.

Limitations

• No Benchmarks: No downstream benchmark evaluations have been conducted; reported numbers are solely training-loss observations.
• Inherited Biases: Inherits biases, knowledge cutoff, and potential failure modes from its base model.
• Specialized Fine-tuning: Fine-tuned on a single instruction-following dataset, meaning its performance outside this specific distribution is untested.
• Merged Adapters: LoRA adapters are merged into the base weights, preventing detachment from this fine-tune.

Potential Use Cases

This model is likely well-suited for applications requiring enhanced reasoning abilities, particularly those that align with the characteristics of the Claude reasoning distillation dataset it was trained on. Developers should consider its specialized fine-tuning for tasks where logical deduction and complex problem-solving are critical.