ermiaazarkhalili/Qwen3.5-4B-SFT-Claude-Opus-Reasoning-Unsloth
The ermiaazarkhalili/Qwen3.5-4B-SFT-Claude-Opus-Reasoning-Unsloth is a 4.7 billion parameter language model, fine-tuned from unsloth/Qwen3.5-4B. This model was supervised fine-tuned using LoRA on a private Claude reasoning distillation dataset, specifically targeting improved reasoning capabilities. It is designed for instruction-following tasks, inheriting the Qwen3.5 architecture and an Apache-2.0 license.
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Model Overview
This model, ermiaazarkhalili/Qwen3.5-4B-SFT-Claude-Opus-Reasoning-Unsloth, is a 4.7 billion parameter language model based on the Qwen3.5ForConditionalGeneration architecture. It is a LoRA fine-tune of the unsloth/Qwen3.5-4B base model, specifically optimized through supervised fine-tuning (SFT) using the ermiaazarkhalili/claude-reasoning-distillation dataset.
Key Characteristics
- Base Model:
unsloth/Qwen3.5-4B - Fine-tuning Method: LoRA SFT via Unsloth and TRL libraries.
- Training Data: Private
ermiaazarkhalili/claude-reasoning-distillationdataset, configured for SFT. - LoRA Configuration: Utilizes a LoRA rank (r) of 64 and alpha of 64, with a learning rate of 0.0002 over 1 epoch.
- Precision: Trained in 4-bit QLoRA precision.
- Max Sequence Length: Supports a maximum sequence length of 2048 tokens.
Limitations
- No Benchmarks: No downstream benchmark evaluations have been conducted; only training loss observations are available.
- Inherited Biases: Inherits biases, knowledge cutoff, and potential failure modes from its base model.
- Specific Fine-tuning: Its behavior outside the distribution of the single instruction-following dataset it was fine-tuned on is untested.
- Merged Adapters: LoRA adapters are merged into the base weights, preventing detachment from this fine-tune.