minh9212/qwen3-0.6b-SFTchat_math
The minh9212/qwen3-0.6b-SFTchat_math is a 0.8 billion parameter Qwen3-based causal language model developed by minh9212. Fine-tuned from unsloth/Qwen3-0.6B-unsloth-bnb-4bit, this model is optimized for chat and mathematical tasks. It was trained using Unsloth and Huggingface's TRL library, offering a 32768 token context length.
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Overview
The minh9212/qwen3-0.6b-SFTchat_math is a 0.8 billion parameter language model based on the Qwen3 architecture. Developed by minh9212, this model is a fine-tuned version of unsloth/Qwen3-0.6B-unsloth-bnb-4bit and is specifically designed for chat interactions and mathematical problem-solving. It leverages the Unsloth library for accelerated training, achieving a 2x speed improvement, alongside Huggingface's TRL library.
Key Capabilities
- Chat-optimized: Designed for conversational AI applications.
- Mathematical proficiency: Fine-tuned to handle mathematical tasks.
- Efficient Training: Utilizes Unsloth for faster training, making it resource-efficient.
- Context Length: Supports a substantial context window of 32768 tokens.
Good For
- Applications requiring a compact yet capable model for general chat.
- Scenarios where mathematical reasoning and problem-solving are key.
- Developers looking for a model trained with efficient methods like Unsloth.