lucataco / ollama-reflection-70b

Ollama Reflection 70b

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Input

Output

Run time and cost

This model costs approximately $0.048 to run on Replicate, or 20 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 35 seconds. The predict time for this model varies significantly based on the inputs.

Readme

About

This is a Cog implementation of Ollama’s Reflection 70b model using the default Q4_0 weights

Hugging Face

Reflection Llama-3.1 70B

| IMPORTANT UPDATE – There was an issue with the model when we first uploaded it. If you tried it and didn’t have good results, please, try again, we think we’ve fixed the issue.

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.

The model was trained on synthetic data generated by Glaive. If you’re training a model, Glaive is incredible — use them.

You can try the model here.

Benchmarks

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All benchmarks tested have been checked for contamination by running LMSys’s LLM Decontaminator. When benchmarking, we isolate the <output> and benchmark on solely that section.

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).

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.

This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user.

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.

Tips for Performance

  • We are initially recommending a temperature of .7 and a top_p of .95.
  • For increased accuracy, append Think carefully. at the end of your messages.

Dataset / Report

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.

Thanks to Jason Kuperberg and Josh Bickett from the HyperWrite team for reviewing drafts of the report we’ll be releasing next week.