laion/allenai-sera-unified-316__Qwen3-8B
The laion/allenai-sera-unified-316__Qwen3-8B model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It was trained on the laion/allenai-sera-unified-316 dataset, suggesting a focus on unified or specific data processing tasks. With a context length of 32768 tokens, it is designed for applications requiring extensive contextual understanding. This model is suitable for tasks benefiting from its specialized fine-tuning and large context window.
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Model Overview
This model, laion/allenai-sera-unified-316__Qwen3-8B, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been specifically fine-tuned on the /e/data1/datasets/playground/ot/hf_hub/datasets--laion--allenai-sera-unified-316/snapshots/ef551d7ec9bb11780e15657490451a6fc6842c46_thinking_preprocessed dataset.
Key Training Details
The fine-tuning process involved several specific hyperparameters:
- Learning Rate: 4e-05
- Batch Sizes: A
train_batch_sizeof 1 andeval_batch_sizeof 8, leading to atotal_train_batch_sizeof 96 across 32 devices with agradient_accumulation_stepsof 3. - Optimizer: Utilized
ADAMW_TORCH_FUSEDwith betas=(0.9, 0.98) and epsilon=1e-08. - Scheduler: A cosine learning rate scheduler with a warmup ratio of 0.1.
- Epochs: Trained for 7.0 epochs.
Framework Versions
The training environment used:
- Transformers 4.57.6
- Pytorch 2.9.1+cu130
- Datasets 4.7.0
- Tokenizers 0.22.2
Potential Use Cases
Given its fine-tuning on a specific dataset, this model is likely optimized for tasks related to the nature of the laion/allenai-sera-unified-316 data. Its 8 billion parameters and 32768 token context length make it suitable for applications requiring deep contextual understanding and processing of moderately complex language tasks.