laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_lr_1e-5_Qwen3-32B
The laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_lr_1e-5_Qwen3-32B 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 a question-answering or technical discussion context. With a context length of 32768 tokens, it is designed to handle extensive input for complex problem-solving.
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Model Overview
This model, laion/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning_lr_1e-5_Qwen3-32B, is a fine-tuned variant of the Qwen3-32B architecture, featuring 32 billion parameters. It has been specialized through training on the open-athena/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning dataset.
Key Characteristics
- Base Model: Qwen/Qwen3-32B
- Parameter Count: 32 billion
- Context Length: 32768 tokens, enabling processing of substantial input texts.
- Training Focus: Fine-tuned on a dataset indicative of reasoning and problem-solving, likely related to technical discussions or question-answering scenarios, such as those found on StackExchange.
Training Details
The model was trained with a learning rate of 1e-06, a total batch size of 32, and utilized the ADAMW_TORCH_FUSED optimizer. Training spanned 6 epochs with a cosine learning rate scheduler and a warmup ratio of 0.1.
Potential Use Cases
Given its fine-tuning on a reasoning-focused dataset, this model is likely well-suited for:
- Complex question answering.
- Technical problem-solving and explanation generation.
- Content generation requiring logical inference.
- Summarization of detailed technical discussions.