namezz/lvm-a-qwen2.5-7b-instruct-b-qwen2.5-7b-instruct
This model is a 7.6 billion parameter instruction-tuned causal language model, fine-tuned by namezz from the Qwen2.5-7B-Instruct base model. It is specifically optimized for mathematical reasoning, code generation, and general instruction following, leveraging datasets focused on these domains. The model demonstrates a validation loss of 0.0041, indicating strong performance in its specialized areas. Its primary use case is for applications requiring robust mathematical problem-solving and accurate code generation capabilities.
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Model Overview
This model, developed by namezz, is a fine-tuned variant of the Qwen2.5-7B-Instruct base model, featuring 7.6 billion parameters. It has been specialized through training on a combination of 7b_math_95k_2_train, 7b_code_100k_2_train, and 7b_instruction_100k_2_train datasets. This targeted fine-tuning aims to enhance its performance in specific domains.
Key Capabilities
- Mathematical Reasoning: Optimized for solving mathematical problems, as indicated by its training on a dedicated math dataset.
- Code Generation: Improved proficiency in generating code, benefiting from extensive code-centric training data.
- Instruction Following: Enhanced ability to follow general instructions, derived from its instruction-tuned base and further specialized training.
Training Details
The model was trained with a learning rate of 2e-05 over 2 epochs, utilizing a total batch size of 1024 across 4 GPUs. Evaluation metrics show a final validation loss of 0.0041, with a Token Mean Relative Error of 0.2722 and a Token Mean Sequence Mean Relative Error of 0.3018. These metrics suggest a high degree of accuracy in its specialized tasks.
Intended Use Cases
This model is particularly well-suited for applications requiring strong performance in:
- Automated mathematical problem-solving.
- Assisted code generation and completion.
- General-purpose instruction following in technical contexts.