cuong1692001/Terminal-complete-16k
Terminal-complete-16k by cuong1692001 is an 8 billion parameter language model fine-tuned from Qwen3-8B. This model is specifically trained on the qwen_data_complete dataset, indicating a focus on comprehensive data for enhanced performance. With a notable context length of 32768 tokens, it is designed for applications requiring extensive contextual understanding and generation.
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Model Overview
cuong1692001/Terminal-complete-16k is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. This model leverages the qwen_data_complete dataset for its training, suggesting an optimization for tasks that benefit from a broad and complete data foundation.
Key Characteristics
- Base Model: Qwen3-8B
- Parameter Count: 8 billion
- Context Length: 32768 tokens, enabling processing of significantly longer inputs and generating more coherent, extended outputs.
- Training Data: Fine-tuned on the
qwen_data_completedataset.
Training Details
The model was trained with the following hyperparameters:
- Learning Rate: 1e-05
- Optimizer: AdamW_Torch with betas=(0.9, 0.999) and epsilon=1e-08
- Epochs: 2.0
- Batch Size: A total training batch size of 4 (1 per device across 4 GPUs).
Potential Use Cases
Given its substantial context window and fine-tuning on a comprehensive dataset, Terminal-complete-16k is potentially well-suited for applications requiring:
- Long-form content generation: Drafting articles, reports, or creative writing pieces that demand extensive context.
- Complex document analysis: Summarizing, extracting information, or answering questions from large texts.
- Conversational AI: Maintaining long, coherent dialogues with extended memory.
Further details on specific intended uses, limitations, and evaluation data are not yet provided in the model card.