airmgsa/qwen2.5-finetuned
The airmgsa/qwen2.5-finetuned model is a 1.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. Utilizing a 131072 token context length, this model has been specifically adapted using the TRL framework. It is designed for general text generation tasks, particularly those requiring instruction following based on its fine-tuning.
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Model Overview
The airmgsa/qwen2.5-finetuned model is a specialized version of the Qwen2.5-1.5B-Instruct base model, developed by Qwen. This instruction-tuned causal language model features 1.5 billion parameters and supports an extensive 131072 token context length, making it suitable for processing longer inputs and generating coherent, extended responses.
Key Characteristics
- Base Model: Fine-tuned from
Qwen/Qwen2.5-1.5B-Instruct. - Training Framework: The model underwent Supervised Fine-Tuning (SFT) using the TRL library, indicating a focus on improving instruction-following capabilities and response quality.
- Parameter Count: With 1.5 billion parameters, it offers a balance between performance and computational efficiency.
- Context Window: Its large context window allows for handling complex prompts and maintaining conversational coherence over extended interactions.
Recommended Use Cases
This model is well-suited for general text generation tasks where instruction adherence is important. Developers can leverage it for:
- Question Answering: Generating direct and relevant answers to user queries.
- Creative Writing: Assisting in generating various forms of text based on specific instructions.
- Conversational AI: Building chatbots or virtual assistants that can follow user prompts effectively.
Technical Details
The fine-tuning process utilized specific versions of popular machine learning frameworks:
- TRL: 0.24.0
- Transformers: 4.57.1
- Pytorch: 2.9.0
- Datasets: 4.3.0
- Tokenizers: 0.22.1