gjyotin305/Qwen2.5-3B-Instruct_adaptive_tune_no_ref
The gjyotin305/Qwen2.5-3B-Instruct_adaptive_tune_no_ref is a 3.1 billion parameter instruction-tuned causal language model, finetuned by gjyotin305 from the Qwen2.5-3B-Instruct base. This model was optimized for faster training using Unsloth and Huggingface's TRL library, making it suitable for applications requiring efficient deployment of Qwen2.5-based models. It maintains a 32768 token context length, focusing on general instruction-following tasks.
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Overview
This model, gjyotin305/Qwen2.5-3B-Instruct_adaptive_tune_no_ref, is an instruction-tuned variant of the Qwen2.5-3B-Instruct base model, developed by gjyotin305. It features 3.1 billion parameters and supports a substantial context length of 32768 tokens, making it capable of handling complex and lengthy prompts.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen2.5-3B-Instruct. - Efficient Training: Leverages Unsloth and Huggingface's TRL library for significantly faster training (2x speedup).
- Parameter Count: A compact 3.1 billion parameters, balancing performance with computational efficiency.
- Context Window: Offers a large 32768 token context, suitable for detailed conversations and document processing.
Use Cases
This model is particularly well-suited for developers looking for an efficiently trained Qwen2.5-based model. Its optimized training process suggests it could be a good choice for:
- General instruction-following tasks.
- Applications where rapid iteration and deployment of finetuned models are crucial.
- Scenarios requiring a balance between model size and performance for text generation and understanding.