saurav1111/reasoning-finetuned-model
The saurav1111/reasoning-finetuned-model is a 3.1 billion parameter Qwen2.5-3B-Instruct model, developed by saurav1111 and fine-tuned for reasoning tasks. It was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. This model is designed for applications requiring efficient reasoning capabilities within a 32768 token context length.
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Model Overview
The saurav1111/reasoning-finetuned-model is a 3.1 billion parameter language model, developed by saurav1111. It is a fine-tuned version of the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2 architecture.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. - Training Efficiency: The model was trained 2x faster using Unsloth and Huggingface's TRL library, indicating an optimized training process.
- License: Distributed under the Apache-2.0 license.
Intended Use Cases
This model is specifically noted as a "reasoning-finetuned-model," suggesting its primary application is in tasks that require logical deduction, problem-solving, and complex understanding. Its 3.1 billion parameters and 32768 token context length make it suitable for:
- Reasoning Tasks: Applications demanding strong logical inference and analytical capabilities.
- Instruction Following: Benefiting from its instruction-tuned base, it can effectively follow complex prompts.
- Efficient Deployment: The use of Unsloth for training implies potential for efficient inference, making it suitable for resource-constrained environments or applications requiring faster response times.