chancharikm/all_sft_formats_balanced_20260222_ep6_lr3e5_qwen3-vl-8b
The chancharikm/all_sft_formats_balanced_20260222_ep6_lr3e5_qwen3-vl-8b model is a fine-tuned 8 billion parameter Qwen3-VL language model. It was trained on the all_sft_formats_balanced_20260222_1240 dataset, building upon a previous iteration. This model is designed for general language understanding and generation tasks, leveraging its 32768 token context length for processing extensive inputs.
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Overview
This model, chancharikm/all_sft_formats_balanced_20260222_ep6_lr3e5_qwen3-vl-8b, is a fine-tuned version of an existing Qwen3-VL 8 billion parameter model. It was specifically trained on the all_sft_formats_balanced_20260222_1240 dataset, indicating a focus on diverse supervised fine-tuning (SFT) formats. The training process involved a learning rate of 1e-07, a total batch size of 128 across 8 GPUs, and utilized the AdamW optimizer with a cosine learning rate scheduler over 3 epochs.
Key Training Details
- Base Model: Qwen3-VL 8B
- Dataset:
all_sft_formats_balanced_20260222_1240 - Learning Rate: 1e-07
- Optimizer: AdamW with betas=(0.9, 0.999)
- Epochs: 3.0
- Total Batch Size: 128 (across 8 GPUs with 2 gradient accumulation steps)
Intended Uses
While specific intended uses and limitations are not detailed in the provided model card, its fine-tuning on a balanced SFT dataset suggests applicability for a range of instruction-following and conversational tasks. Developers can leverage its 8 billion parameters and 32768 token context for applications requiring robust language understanding and generation.