Selfrag

Models by this creator

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selfrag_llama2_7b

selfrag

Total Score

68

The selfrag_llama2_7b model is a 7B Self-RAG model developed by selfrag. It generates outputs for diverse user queries and includes "reflection tokens" that allow the model to adaptively call the retrieval system and critically evaluate its own output and retrieved passages. The Self-RAG training process uses an instruction-following corpus with interleaved passages and reflection tokens, enabling efficient and stable learning with fine-grained feedback. This model is similar to other Self-RAG and RAG-based models like the SciPhi-Self-RAG-Mistral-7B-32k and LlamaGuard-7b models, which also incorporate retrieval and reflection mechanisms to enhance their capabilities. Model inputs and outputs Inputs Instruction**: A user query or task that the model should respond to. Retrieval Passage**: An optional passage of text that the model can use to provide a more informed and grounded response. Outputs Response**: The model's generated output that responds to the given instruction or query. This output may reference or leverage the provided retrieval passage. Reflection Tokens**: Special tokens embedded in the output that indicate when the model is calling the retrieval system or critiquing its own generation. Capabilities The selfrag_llama2_7b model can generate diverse and informative responses to a wide range of user queries by adaptively leveraging an external retrieval system. Its use of reflection tokens allows it to provide fine-grained feedback on its own output quality and identify areas where additional retrieval or revision is needed. This enables the model to produce more coherent and grounded responses compared to traditional language models. What can I use it for? The selfrag_llama2_7b model could be useful for applications that require generating informative and context-aware text responses, such as: Conversational AI**: The model's ability to critically evaluate its own responses and call on additional information sources makes it well-suited for open-ended dialog systems. Question Answering**: By using retrieval passages, the model can provide more accurate and detailed answers to factual queries. Content Creation**: The model could be used to generate high-quality written content like news articles, product descriptions, or creative stories by leveraging relevant background information. Things to try One interesting aspect of the selfrag_llama2_7b model is its use of reflection tokens to guide the output generation. You could experiment with prompting the model to provide more detailed self-evaluations or to explicitly request certain types of reflection (e.g., "Can you explain your reasoning for the response and identify any areas where you are uncertain?"). This could help you better understand the model's internal decision-making process and identify ways to further improve its performance. Another interesting area to explore would be fine-tuning the model on domain-specific datasets to enhance its capabilities for particular applications. The ability to adaptively retrieve and integrate relevant information makes the selfrag_llama2_7b model well-suited for transfer learning and specialized use cases.

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Updated 5/17/2024

selfrag_llama2_13b

selfrag

Total Score

60

The selfrag_llama2_13b model is a 13B Self-RAG model developed by selfrag. Similar to the selfrag_llama2_7b model, it generates outputs to diverse user queries and includes "reflection tokens" to adaptively call the retrieval system and critique its own output and retrieved passages. The Self-RAG training approach involves interleaving passages and reflection tokens in the instruction-following corpus, enabling efficient and stable learning with fine-grained feedback. At inference, the model leverages these reflection tokens to sample the best output aligning with user preferences, as described in the paper. Model Inputs and Outputs Inputs Text instructions or queries from the user Outputs Text responses generated by the model, which may include: Direct answers to questions Descriptions, explanations, or analysis related to the input Reflection tokens indicating the model's self-assessment of its output Capabilities The selfrag_llama2_13b model is capable of generating informative and coherent responses to a wide range of user queries. It can draw upon its training data to provide factual information, offer analysis and opinions, and even critically evaluate its own outputs. The use of reflection tokens allows the model to dynamically adapt its response generation based on the specific needs of the user. What Can I Use It For? The selfrag_llama2_13b model could be useful for building conversational AI assistants that can engage in open-ended dialog, answer questions, and provide insights. Its self-reflective capabilities make it well-suited for applications where the user's needs and preferences may evolve over the course of an interaction. Things to Try One interesting aspect of the selfrag_llama2_13b model is its ability to critique its own outputs. You could try prompting the model with a query and observing how it uses the reflection tokens to assess the quality and relevance of its response. This could provide valuable feedback for refining the model or tailoring it to specific use cases.

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Updated 5/17/2024