ferrazzipietro/Qwen3-1.7B-reas-int-065-3-epochs
The ferrazzipietro/Qwen3-1.7B-reas-int-065-3-epochs model is a 1.7 billion parameter language model, fine-tuned from the Qwen/Qwen3-1.7B architecture. This model has undergone specific fine-tuning over 3 epochs, suggesting an optimization for particular tasks, though the exact dataset and primary differentiator are not specified. It is designed for general language understanding and generation within its 32768 token context window.
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Model Overview
This model, ferrazzipietro/Qwen3-1.7B-reas-int-065-3-epochs, is a fine-tuned variant of the Qwen3-1.7B base model. It features approximately 1.7 billion parameters and supports a context length of 32768 tokens. The fine-tuning process involved 3 epochs, utilizing a learning rate of 5e-06 and a total batch size of 64 across 2 GPUs.
Training Details
The model was trained using specific hyperparameters, including an AdamW optimizer with betas=(0.9, 0.95) and epsilon=1e-12. A cosine learning rate scheduler with a warmup ratio of 0.1 was employed. The training environment utilized Transformers 4.57.0, Pytorch 2.14.0+cu130, Datasets 5.0.1, and Tokenizers 0.22.2.
Key Characteristics
- Base Model: Qwen/Qwen3-1.7B
- Parameter Count: 1.7 billion
- Context Window: 32768 tokens
- Fine-tuning: 3 epochs with a focus on specific, though currently unspecified, reasoning or instruction-following tasks.
Intended Use
While specific intended uses and limitations are not detailed in the provided information, as a fine-tuned Qwen3-1.7B model, it is generally suitable for various natural language processing tasks, including text generation, summarization, and question answering, particularly where its fine-tuning has enhanced performance.