---
license: other
language:
- en
pipeline_tag: text-generation
---


# llama-3-neural-chat-v2.2-8b

<!-- Provide a quick summary of what the model is/does. -->


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/6XQuhjWNr6C4RbU9f1k99.png)



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO-Positive.
DPO-Positive dramatically improves performance over DPO. 

- **Developed by:** Locutusque
- **Model type:** Built with Meta Llama 3
- **Language(s) (NLP):** Many?
- **License:** Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE

## Quants

GGUF: https://huggingface.co/bartowski/llama-3-neural-chat-v2.2-8B-GGUF

ExLlamaV2: https://huggingface.co/bartowski/llama-3-neural-chat-v2.2-8B-exl2

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

This model has great performance in writing, coding, and math.

## Training Data
Recipe information will be coming soon. This language model's recipe is similar to Intel's Neural Chat.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Conversational AI. This model is also very uncensored, it will respond to pretty much any request regardless of the system prompt, use at your own risk.

## Evaluations

|              Tasks              |Version|     Filter     |n-shot|  Metric   |Value |   |Stderr|
|---------------------------------|-------|----------------|-----:|-----------|-----:|---|-----:|
|truthfulqa_mc2                   |      2|none            |     0|acc        |0.5232|±  |0.0151|
|gsm8k                            |      3|strict-match    |     5|exact_match|0.5974|±  |0.0135|
|                                 |       |flexible-extract|     5|exact_match|0.5974|±  |0.0135|
|agieval_nous                     |N/A    |none            |     0|acc_norm   |0.3841|±  |0.0094|
|                                 |       |none            |     0|acc        |0.3802|±  |0.0094|
| - agieval_aqua_rat              |      1|none            |     0|acc        |0.2598|±  |0.0276|
|                                 |       |none            |     0|acc_norm   |0.2520|±  |0.0273|
| - agieval_logiqa_en             |      1|none            |     0|acc        |0.3441|±  |0.0186|
|                                 |       |none            |     0|acc_norm   |0.3687|±  |0.0189|
| - agieval_lsat_ar               |      1|none            |     0|acc        |0.2217|±  |0.0275|
|                                 |       |none            |     0|acc_norm   |0.2348|±  |0.0280|
| - agieval_lsat_lr               |      1|none            |     0|acc        |0.3882|±  |0.0216|
|                                 |       |none            |     0|acc_norm   |0.3824|±  |0.0215|
| - agieval_lsat_rc               |      1|none            |     0|acc        |0.4944|±  |0.0305|
|                                 |       |none            |     0|acc_norm   |0.5019|±  |0.0305|
| - agieval_sat_en                |      1|none            |     0|acc        |0.6650|±  |0.0330|
|                                 |       |none            |     0|acc_norm   |0.6553|±  |0.0332|
| - agieval_sat_en_without_passage|      1|none            |     0|acc        |0.3981|±  |0.0342|
|                                 |       |none            |     0|acc_norm   |0.3981|±  |0.0342|
| - agieval_sat_math              |      1|none            |     0|acc        |0.3500|±  |0.0322|
|                                 |       |none            |     0|acc_norm   |0.3318|±  |0.0318|