File size: 6,312 Bytes

---
license: llama3
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
tags:
- llama
base_model: mattshumer/ref_70_e3
pipeline_tag: text-generation
library_name: ggml
datasets:
- froggeric/imatrix
metrics:
- perplexity
---

# Reflection-Llama-3.1-70B-GGUF

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6604e5b21eb292d6df393365/lQJH2XICEKaACm9lfH7ZM.webp)

GGUF quantized models of [mattshumer/ref_70_e3](https://huggingface.co/mattshumer/ref_70_e3)

> This is the new, working version of the Reflection Llama 3.1 70B model.

**Reflection Llama-3.1 70B is (purportedly) 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.**

| Quantization | Size   | Split | iMatrix |
| ------------ | ------ | ----- | ------- |
| FP16         | 141GB  | true  | false   |
| Q8_0_L       | ??.?GB | true  | false   |
| Q8_0         | ??.?GB | true  | false   |
| Q6_K_L       | ??.?GB | true  | false   |
| Q6_K         | 57.9GB | true  | false   |
| Q5_K_L       | 52.6GB | true  | false   |
| Q5_K_M       | ??.?GB | true  | false   |
| Q5_K_S       | 48.7GB | false | false   |
| Q4_K_L       | 45.3GB | false | false   |
| Q4_K_M       | ??.?GB | false | false   |
| Q4_K_S       | 40.3GB | false | false   |
| IQ4_NL       | 38.2GB | false | true    |
| IQ4_XS       | ??.?GB | false | true    |
| Q3_K_XL      | 37.2GB | false | false   |
| Q3_K_L       | 37.1GB | false | false   |
| Q3_K_M       | 34.3GB | false | false   |
| IQ3_M        | ??.?GB | false | true    |
| Q3_K_S       | ??.?GB | false | false   |
| IQ3_S        | ??.?GB | false | true    |
| Q2_K_L       | 29.4GB | false | false   |
| IQ3_XS       | ??.?GB | false | true    |
| IQ3_XXS      | ??.?GB | false | true    |
| Q2_K         | ??.?GB | false | true    |
| Q2_K_S       | ??.?GB | false | true    |
| IQ2_M        | 23.0GB | false | true    |
| IQ2_S        | 21.2GB | false | true    |
| IQ2_XS       | 20.2GB | false | true    |
| IQ2_XXS      | 18.2GB | false | true    |
| IQ1_M        | 16.0GB | false | true    |
| IQ1_S        | 14.6GB | false | true    |

The `_L` or `_XL` suffix means that the token embeddings and output weight are at fp16 precision.

The iMatrix dataset is bartowski's, which you can find here: [calibration_datav3.txt](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)

Computation is done on static Q6_K for 125 chunks.

## Model Info

The model was not trained on 3 epoches, because it's identical to the 2nd epoch run [mattshumer/Reflection-Llama-3.1-70B-ep2-working](https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B-ep2-working) (it's possible this is also fake). 

The fine-tuning was done using LoRA with rank 256 on the Llama-3.1-70B-Instruct model.

## Benchmarks
<div style="position: relative;">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/60518f3731c5be7f3dd5ebc3/zNs-ZFs0SbnomH7mikiOU.png" alt="Your image description">
  <div style="position: absolute; top: 50%; left: -20%; width: 140%; height: 5px; background-color: red; transform: rotate(10deg);"></div>
  <div style="position: absolute; top: 50%; left: -20%; width: 140%; height: 5px; background-color: red; transform: rotate(-10deg);"></div>
</div>

**Warning: These are likely false scores and cannot be replicated with this model.**

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.

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 generating 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.

## System Prompt

The system prompt used for training this model is:

```
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.
```

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.

## Chat Format

The model uses the standard Llama 3.1 chat format. Here’s an example:

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

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|>

What is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Tips for Performance

- A temperature of `.7` and a Top P of `.95` is recommended.
- For increased accuracy, append `Think carefully.` at the end of your prompt.

## 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](https://hyperwriteai.com) team for reviewing drafts of the report we'll be releasing next week.