ermiaazarkhalili/VibeThinker-3B-SFT-Claude-Opus-Reasoning-Unsloth
VibeThinker-3B-SFT-Claude-Opus-Reasoning-Unsloth is a 3.1 billion parameter language model developed by ermiaazarkhalili, fine-tuned from VibeThinker-3B. It is specifically optimized for reasoning distillation and chain-of-thought learning, utilizing the claude-reasoning-distillation dataset. The model leverages Unsloth for efficient training, resulting in faster fine-tuning and reduced VRAM usage. This model is designed to excel in tasks requiring step-by-step reasoning, with a context length of 2048 tokens.
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Model Overview
This model, VibeThinker-3B-SFT-Claude-Opus-Reasoning-Unsloth, is a 3.1 billion parameter language model developed by ermiaazarkhalili. It is a fine-tuned version of the VibeThinker-3B base model, specifically optimized for reasoning distillation and chain-of-thought capabilities.
Key Capabilities & Features
- Reasoning Distillation: Fine-tuned on the claude-reasoning-distillation dataset, which contains over 10,000 samples of Claude's reasoning traces with
<think>blocks. - Efficient Training: Utilizes Unsloth for fine-tuning, enabling 2x faster training and 60% less VRAM consumption compared to standard methods.
- QLoRA Fine-tuning: Trained using QLoRA (4-bit) with a 2048 token context window.
- Accessibility: Available in GGUF versions for CPU and edge inference, supporting platforms like Ollama and llama.cpp.
Use Cases
This model is particularly well-suited for:
- Reasoning Tasks: Ideal for applications requiring step-by-step problem-solving and logical deduction.
- Educational Tools: Can be integrated into systems that teach or demonstrate complex reasoning processes.
- Resource-Efficient Deployment: Its 3.1B parameter size and Unsloth optimization make it suitable for environments with limited computational resources.
Limitations
- Primarily trained on English data.
- Knowledge is limited to the base model's training data cutoff.
- May exhibit hallucinations and is not extensively safety-tuned.