AitorConS/papertalk-qwen2.5-7b
AitorConS/papertalk-qwen2.5-7b is a 7.6 billion parameter language model fine-tuned from Qwen/Qwen2.5-7B using the TRL library. This model is specifically trained via Supervised Fine-Tuning (SFT) to enhance its conversational capabilities. It leverages a 32K context window, making it suitable for generating coherent and contextually relevant responses in dialogue-based applications.
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Overview
AitorConS/papertalk-qwen2.5-7b is a 7.6 billion parameter language model derived from the Qwen/Qwen2.5-7B base model. It has undergone Supervised Fine-Tuning (SFT) using the TRL library, a framework for Transformer Reinforcement Learning, to specialize its conversational abilities. The model maintains the Qwen2.5-7B's 32,768 token context length, allowing it to process and generate longer, more detailed responses while retaining context.
Key Capabilities
- Conversational AI: Fine-tuned for generating human-like responses in dialogue scenarios.
- Contextual Understanding: Benefits from a 32K context window, enabling it to handle complex and extended conversations.
- Base Model Performance: Inherits the robust capabilities of the Qwen2.5-7B architecture.
Training Details
This model was trained using Supervised Fine-Tuning (SFT) with the TRL library. The training environment utilized specific versions of key frameworks:
- PEFT: 0.19.1
- TRL: 1.5.1
- Transformers: 5.9.0
- Pytorch: 2.11.0+cu128
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Good For
- Developing chatbots and virtual assistants requiring nuanced conversational abilities.
- Applications where understanding and generating responses within a large context window is crucial.
- Research and experimentation with SFT techniques on a Qwen2.5 base model.