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--- |
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license: llama3.1 |
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base_model: meta-llama/Meta-Llama-3.1-70B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Reflection Llama-3.1 70B |
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**Reflection Llama-3.1 70B is (currently) the world's top open-source LLM, trained with a new technique called Reflection-Tuning that teaches a LLM to detect mistakes in its reasoning and correct course.** |
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The model was trained on synthetic data generated by [Glaive](https://glaive.ai). If you're training a model, Glaive is incredible — use them. |
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You can [try the model here](https://reflection-playground-production.up.railway.app/). |
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## Benchmarks |
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All benchmarks tested have been checked for contamination by running [LMSys's LLM Decontaminator](https://github.com/lm-sys/llm-decontaminator). When benchmarking, we isolate the `<output>` and benchmark on solely that section. |
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Trained from Llama 3.1 70B Instruct, you can sample from Reflection Llama-3.1 70B using the same code, pipelines, etc. as any other Llama model. It even uses the stock Llama 3.1 chat template format (though, we've trained in a few new special tokens to aid in reasoning and reflection). |
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During sampling, the model will start by outputting reasoning inside `<thinking>` and `</thinking>` tags, and then once it is satisfied with its reasoning, it will output the final answer inside `<output>` and `</output>` tags. Each of these tags are special tokens, trained into the model. |
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This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user. |
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Inside the `<thinking>` section, the model may output one or more `<reflection>` tags, which signals the model has caught an error in its reasoning and will attempt to correct it before providing a final answer. |
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## System Prompt |
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The system prompt used for training this model is: |
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``` |
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You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags. |
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``` |
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We recommend using this exact system prompt to get the best results from Reflection Llama-3.1 70B. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model. |
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## Chat Format |
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As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example: |
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``` |
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<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
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You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<|eot_id|><|start_header_id|>user<|end_header_id|> |
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what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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``` |
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## Tips for Performance |
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- We are initially recommending a `temperature` of `.7` and a `top_p` of `.95`. |
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- For increased accuracy, append `Think carefully.` at the end of your messages. |
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## Dataset / Report |
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Both the dataset and a brief report detailing how we trained this model will be released next week, alongside our Reflection 405B model that we expect will be the top-performing LLM in the world, including closed-source models. |
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--- |
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Thanks to Jason Kuperberg and Josh Bickett from the [HyperWrite](https://hyperwriteai.com) team for reviewing drafts of the report we'll be releasing next week. |
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Also, we know right now the model is split into a ton of files. We'll condense this soon to make the model easier to download and work with! |
