t2ance/atlas-sft_qwen3_8b_pattern_teacher_gpqa_firstcall_full
The t2ance/atlas-sft_qwen3_8b_pattern_teacher_gpqa_firstcall_full model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It was specifically trained on the pattern_teacher_gpqa_firstcall dataset, suggesting an optimization for tasks related to pattern recognition, teaching, or question answering within a specific domain. With a context length of 32768 tokens, it is suitable for processing moderately long inputs relevant to its fine-tuning data.
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Model Overview
This model, t2ance/atlas-sft_qwen3_8b_pattern_teacher_gpqa_firstcall_full, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has undergone specific fine-tuning on the pattern_teacher_gpqa_firstcall dataset, indicating a specialized focus on tasks related to pattern identification, instructional content generation, or general-purpose question answering, particularly within the context of its training data.
Key Training Details
The model was trained for 1 epoch using a learning rate of 1e-05 and an AdamW optimizer. The training process involved a total batch size of 8, distributed across multiple GPUs, with a cosine learning rate scheduler and a warmup ratio of 0.05. The training utilized Transformers 4.57.6 and PyTorch 2.10.0+cu128.
Potential Use Cases
Given its fine-tuning on the pattern_teacher_gpqa_firstcall dataset, this model is likely best suited for applications that involve:
- Pattern Recognition: Identifying and generating sequences or structures based on learned patterns.
- Instructional Content: Assisting in the creation or understanding of teaching materials.
- Question Answering: Providing responses to queries, especially those aligned with the GPQA domain.
Users should note that specific performance metrics and detailed intended uses or limitations are not yet fully documented, and further evaluation is recommended for specific applications.