Lili85/llama2-7b-squad-full
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Apr 3, 2026Architecture:Transformer Cold
Lili85/llama2-7b-squad-full is a 7 billion parameter Llama 2 model fine-tuned for question answering tasks. Developed by Lili85, this model leverages the TRL framework to specialize in extracting answers from provided contexts. It is designed for applications requiring precise information retrieval and response generation based on given questions.
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Model Overview
Lili85/llama2-7b-squad-full is a 7 billion parameter language model based on the meta-llama/Llama-2-7b-hf architecture. This model has been specifically fine-tuned using the TRL (Transformers Reinforcement Learning) framework, indicating an optimization process beyond standard supervised fine-tuning, though the README specifies SFT was used for this particular model.
Key Capabilities
- Question Answering: The model is fine-tuned for SQuAD (Stanford Question Answering Dataset) related tasks, making it proficient in understanding questions and generating relevant answers.
- Llama 2 Foundation: Benefits from the robust base capabilities of the Llama 2 7B model, including a 4096-token context length.
- TRL Framework: Utilizes the TRL library for its training procedure, suggesting a focus on efficient and effective fine-tuning methodologies.
Good For
- Information Extraction: Ideal for applications where specific answers need to be extracted from text based on user queries.
- Conversational AI: Can be integrated into chatbots or virtual assistants that require accurate and context-aware responses to questions.
- Research and Development: Suitable for researchers exploring fine-tuning techniques on Llama 2 for QA tasks, particularly with the TRL framework.