laion/glm46-code-feedback-maxeps-131k
The laion/glm46-code-feedback-maxeps-131k model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It was specifically trained on the penfever/glm46-code-feedback-maxeps-131k dataset, indicating an optimization for processing and generating code-related feedback. This model is designed for tasks requiring nuanced understanding and generation within coding contexts, leveraging its 32768 token context length.
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Overview
This model, laion/glm46-code-feedback-maxeps-131k, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been fine-tuned using the penfever/glm46-code-feedback-maxeps-131k dataset, suggesting a specialization in tasks related to code analysis and feedback generation. The training process involved a learning rate of 4e-05, a cosine learning rate scheduler with a 0.1 warmup ratio, and was conducted over 7 epochs with a total batch size of 16 across 8 GPUs.
Key Characteristics
- Base Model: Qwen/Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32768 tokens
- Fine-tuning Dataset:
penfever/glm46-code-feedback-maxeps-131k - Training Hyperparameters: Utilized AdamW_Torch_Fused optimizer, 4e-05 learning rate, and 7 epochs.
Potential Use Cases
Given its fine-tuning on a code-feedback dataset, this model is likely suitable for applications such as:
- Automated code review and suggestion systems.
- Generating explanations or improvements for code snippets.
- Assisting developers with understanding and refining their code based on provided feedback.