VladShash/qwen3-4b-feedback-no-repair
VladShash/qwen3-4b-feedback-no-repair is a 4 billion parameter language model, fine-tuned from formalmathatepfl/qwen3-4b-cpt. This model is specifically adapted for tasks related to feedback processing without repair mechanisms, building upon the Qwen3 architecture. It is intended for applications requiring specialized handling of feedback data, leveraging its 32768 token context length for comprehensive input processing.
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Overview
VladShash/qwen3-4b-feedback-no-repair is a 4 billion parameter language model, fine-tuned from the formalmathatepfl/qwen3-4b-cpt base model. This iteration focuses on processing feedback without incorporating repair mechanisms, suggesting a specialization in analysis or classification of feedback rather than generative correction.
Key Characteristics
- Base Model: Fine-tuned from
formalmathatepfl/qwen3-4b-cpt. - Parameter Count: 4 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Specialization: Designed for tasks involving feedback where no repair or generative correction is required.
Training Details
The model was trained with a learning rate of 2e-05 over 1.0 epochs, utilizing an AdamW optimizer with specific beta and epsilon values. Training was distributed across 8 GPUs with a total batch size of 8, and a cosine learning rate scheduler with a warmup ratio of 0.05 was employed.
Intended Use Cases
This model is suitable for applications where the primary goal is to understand, categorize, or analyze feedback data without generating corrective responses. Its large context window allows for processing extensive feedback inputs, making it potentially useful for sentiment analysis, issue detection, or summarization of user feedback in its raw form.