bagel-dpo-7b-v0.1
Maintainer: jondurbin
42
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The bagel-dpo-7b-v0.1 model is a fine-tuned version of jondurbin/bagel-7b-v0.1 that has been optimized using direct preference optimization (DPO). It was created by jondurbin and is based on the bagel framework. This DPO version aims to address issues where the original model may have refused requests, providing a more robust and uncensored assistant.
Model inputs and outputs
The bagel-dpo-7b-v0.1 model is a large language model capable of generating human-like text. It takes in natural language prompts as input and produces coherent, contextual responses.
Inputs
- Free-form text prompts that can cover a wide range of topics and tasks, such as:
- Questions or statements that require reasoning, analysis, or generation
- Requests for creative writing, code generation, or task completion
- Open-ended conversations
Outputs
- Coherent, contextual text responses that aim to fulfill the given prompt or continue the conversation
- Responses can range from short phrases to multi-paragraph outputs
Capabilities
The bagel-dpo-7b-v0.1 model demonstrates strong performance across a variety of benchmarks, including ARC Challenge, BoolQ, GSM8K, HellaSwag, MMLU, OpenBookQA, PIQA, TruthfulQA, and Winogrande. It outperforms the original bagel-7b-v0.1 model on many of these tasks.
What can I use it for?
The bagel-dpo-7b-v0.1 model can be used for a wide range of natural language processing and generation tasks, such as:
- Question-answering and information retrieval
- Conversational AI and chatbots
- Creative writing and storytelling
- Code generation and programming assistance
- Summarization and content generation
Given its improved performance over the original bagel-7b-v0.1 model, the bagel-dpo-7b-v0.1 may be particularly well-suited for applications that require more robust and uncensored responses.
Things to try
One interesting aspect of the bagel-dpo-7b-v0.1 model is its use of multiple prompt formats, including Vicuna, Llama-2 chat, and a ChatML-inspired format. This allows the model to generalize better to a variety of prompting styles. You could experiment with these different formats to see which works best for your specific use case.
Additionally, the model was trained on a diverse set of data sources, including various instruction datasets, plain text, and DPO pairs. This broad training data may enable the model to excel at a wide range of tasks. You could try prompting the model with different types of queries and observe its performance.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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