kms7530/chemeng_qwen-math-7b_24_1_100_1_nonmath
The kms7530/chemeng_qwen-math-7b_24_1_100_1_nonmath is a 7.6 billion parameter Qwen2 model, fine-tuned from unsloth/Qwen2.5-Math-7B-Instruct-bnb-4bit. Developed by kms7530, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is specifically designed for mathematical and chemical engineering related tasks, leveraging its Qwen2.5-Math base for enhanced numerical reasoning.
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Model Overview
The kms7530/chemeng_qwen-math-7b_24_1_100_1_nonmath is a 7.6 billion parameter language model developed by kms7530. It is fine-tuned from the unsloth/Qwen2.5-Math-7B-Instruct-bnb-4bit base model, indicating a specialization in mathematical reasoning and potentially chemical engineering applications, given its name.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen2.5-Math-7B-Instruct-bnb-4bit, suggesting strong capabilities in mathematical problem-solving. - Training Efficiency: The model was trained 2x faster using Unsloth and Huggingface's TRL library, highlighting an optimized training process.
- Parameter Count: Features 7.6 billion parameters, offering a balance between performance and computational requirements.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Potential Use Cases
This model is likely well-suited for applications requiring:
- Mathematical Problem Solving: Leveraging its
Qwen2.5-Mathlineage for numerical tasks. - Chemical Engineering Contexts: The
chemengin its name suggests potential for specialized applications within chemical engineering domains, though specific training data is not detailed. - Instruction Following: As an instruction-tuned model, it should perform well in responding to specific prompts and tasks.