laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_num-train-epochs_7.0_Qwen3-32B

TEXT GENERATIONPricing:Input $0.408 / Cached $0.0816 / Output $1.972Concurrent Unit Cost:2Model Size:32BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jan 11, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_num-train-epochs_7.0_Qwen3-32B model is a 32 billion parameter language model fine-tuned from Qwen/Qwen3-32B. It was specifically trained on the open-athena/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning dataset, suggesting an optimization for reasoning tasks, potentially within the domain of StackExchange-like content. With a 32768 token context length, it is designed for processing and generating detailed, context-rich responses, making it suitable for applications requiring in-depth understanding and generation of technical or Q&A-style content.

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Model Overview

This model, laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_num-train-epochs_7.0_Qwen3-32B, is a 32 billion parameter language model derived from the Qwen3-32B architecture. It has been fine-tuned on the open-athena/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning dataset, indicating a specialized focus on reasoning and potentially question-answering tasks, particularly those found in technical forums like StackExchange.

Key Characteristics

  • Base Model: Qwen/Qwen3-32B
  • Parameter Count: 32 billion parameters
  • Context Length: 32768 tokens, enabling processing of extensive inputs and generating comprehensive outputs.
  • Training Focus: Fine-tuned on a dataset explicitly mentioning "reasoning" and "stackexchange-overflow-sandboxes," suggesting an emphasis on logical inference and structured Q&A formats.

Training Details

The model underwent 7.0 epochs of training with a learning rate of 4e-05, using a total batch size of 32 across 16 GPUs. The training utilized the AdamW_Torch_Fused optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio.

Potential Use Cases

Given its fine-tuning on a reasoning-focused dataset, this model is likely well-suited for:

  • Technical Q&A systems: Generating answers to complex technical questions.
  • Code explanation and debugging: Understanding and explaining code snippets or debugging scenarios.
  • Content generation for forums: Creating detailed responses or articles in technical domains.
  • Reasoning tasks: Applications requiring logical deduction and problem-solving based on provided context.

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