laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_warmup-ratio_0-05_Qwen3-32B

TEXT GENERATIONConcurrency Cost:2Model Size:32BQuant:FP8Ctx Length:32kPublished:Jan 20, 2026License:otherArchitecture:Transformer Cold

The laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_warmup-ratio_0-05_Qwen3-32B is a 32 billion parameter language model, fine-tuned from Qwen/Qwen3-32B. This model is specifically trained on the penfever/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning dataset, indicating a specialization in reasoning tasks within a StackExchange-like context. It is designed for applications requiring nuanced understanding and generation of content similar to technical Q&A forums, leveraging its 32768 token context length.

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Model Overview

This model, laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_warmup-ratio_0-05_Qwen3-32B, is a fine-tuned version of the Qwen/Qwen3-32B base model. It has been specifically adapted using the penfever/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning dataset.

Key Characteristics

  • Base Model: Qwen/Qwen3-32B, a 32 billion parameter language model.
  • Fine-tuning Dataset: Specialized on a dataset derived from StackExchange and Overflow sandboxes, suggesting an optimization for technical Q&A, problem-solving, and reasoning in structured, community-driven content.
  • Context Length: Inherits the 32768 token context length from its base model, enabling processing of extensive inputs and generating detailed responses.

Training Details

The model was trained with the following key hyperparameters:

  • Learning Rate: 4e-05
  • Optimizer: ADAMW_TORCH_FUSED
  • Epochs: 7.0
  • Batch Size: A total training batch size of 32 (with gradient accumulation steps of 2 across 16 devices).
  • LR Scheduler: Cosine scheduler with a warmup ratio of 0.05.

Intended Use Cases

Given its fine-tuning on StackExchange-like data, this model is likely well-suited for:

  • Generating answers to technical questions.
  • Summarizing discussions or solutions from Q&A forums.
  • Assisting with code-related queries or debugging scenarios.
  • Applications requiring strong reasoning capabilities within a technical or problem-solving domain.