ermiaazarkhalili/FastContext-4B-SFT_base-SFT-Claude-Opus-Reasoning-Unsloth
The ermiaazarkhalili/FastContext-4B-SFT_base-SFT-Claude-Opus-Reasoning-Unsloth is a 4 billion parameter language model, fine-tuned from Microsoft's FastContext-1.0-4B-SFT using LoRA and Unsloth. This model is specifically supervised fine-tuned on a private Claude reasoning distillation dataset, indicating an optimization for reasoning tasks. It utilizes the Qwen3ForCausalLM architecture and has a context length of 32768 tokens, making it suitable for applications requiring strong reasoning capabilities within a substantial context.
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Model Overview
This model, ermiaazarkhalili/FastContext-4B-SFT_base-SFT-Claude-Opus-Reasoning-Unsloth, is a 4 billion parameter language model built upon Microsoft's FastContext-1.0-4B-SFT base model. It leverages the Qwen3ForCausalLM architecture and has been fine-tuned using LoRA (Low-Rank Adaptation) via Unsloth and TRL.
Key Capabilities & Training
- Reasoning Optimization: The model was supervised fine-tuned on a private dataset,
ermiaazarkhalili/claude-reasoning-distillation, specifically targeting reasoning tasks. This suggests an enhanced ability to process and generate logical responses. - Efficient Fine-tuning: Utilizes LoRA with a rank of 16 and alpha of 16, trained for 1 epoch with a learning rate of 0.0002, demonstrating an efficient fine-tuning approach.
- Context Length: Inherits a substantial context length of 32768 tokens, allowing it to handle extensive inputs for complex reasoning scenarios.
Limitations
- No Benchmark Evaluation: Currently, no downstream benchmark evaluations have been performed on this specific checkpoint; only training loss observations are available.
- Inherited Biases: The model inherits biases, knowledge cutoffs, 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 remains untested.
- Merged Adapters: The LoRA adapters are merged into the base weights, meaning the fine-tune cannot be detached from the base model.