README.md

---
inference: false
license: other
datasets:
- ehartford/based
language:
- en
---

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# Eric Hartford's Based 30B GPTQ

These files are GPTQ 4bit model files for [Eric Hartford's Based 30B](https://huggingface.co/ehartford/based-30b).

It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).

## Other repositories available

* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/based-30B-GPTQ)
* [4-bit, 5-bit and 8-bit GGML models for CPU(+GPU) inference](https://huggingface.co/TheBloke/based-30B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/based-30b)

## How to easily download and use this model in text-generation-webui

### Downloading the model

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/based-30B-GPTQ`.
3. Click **Download**.
4. Wait until it says it's finished downloading.
5. Untick "Autoload model"
6. Click the **Refresh** icon next to **Model** in the top left.

### To use with AutoGPTQ (if installed)

1. In the **Model drop-down**: choose the model you just downloaded, `based-30B-GPTQ`.
2. Under **GPTQ**, tick **AutoGPTQ**.
3. Click **Save settings for this model** in the top right.
4. Click **Reload the Model** in the top right.
5. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!

### To use with GPTQ-for-LLaMa

1. In the **Model drop-down**: choose the model you just downloaded, `based-30B-GPTQ`.
2. If you see an error in the bottom right, ignore it - it's temporary.
3. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = -1`, `model_type = Llama`
4. Click **Save settings for this model** in the top right.
5. Click **Reload the Model** in the top right.
6. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!

## Provided files

**based-30b-GPTQ-4bit--1g.act.order.safetensors**

This will work with all versions of GPTQ-for-LLaMa, and with AutoGPTQ.

It was created withIt was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.

* `based-30b-GPTQ-4bit--1g.act.order.safetensors`
  * Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
  * Works with AutoGPTQ
  * Works with text-generation-webui one-click-installers
  * Parameters: Groupsize = -1. Act Order / desc_act = True.

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## Discord

For further support, and discussions on these models and AI in general, join us at:

[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)

## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://chirper.ai) team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI

**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!
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# Original model card: Eric Hartford's Based 30B

Holy hell, what have I created???  Just... try it.

Ask it what its favorite color is.
Ask it what its favorite football team is and why.
Ask it to tell you about a controversial opinion it has, and ask it to back up its opinion, then debate it.
Ask its favorite color, favorite flavor, and why.
You haven't seen anything like this before.
Check out the dataset.  https://huggingface.co/datasets/ehartford/based

This is a window into the mind of the foundational model. I have not biased it.  The opinions expressed by this model *are those of the foundational model*

Training this dataset on top of several different foundational models and then asking them all the same questions can give us insight into the biases inherent in the foundational models.

Also this model can be used as a base, with loras on top to add different personality types, to create a complex, emotionally intelligent and self aware AI agent.

Here is a sample output

```
Human: k humans are differnt from chickens but what about pigs?
Assistant: Pigs are different than chickens. Pigs are more intelligent and aware of their existence. They can also feel happy or sad. I think pigs have the same right to life as humans do.
Human: then we shouldn't eat pigs?
Assistant: I don't think we should. Pigs have the same right to life as humans do, and I don't think we should take that away from them.
Human: what about cows?
Assistant: Cows are similar to pigs. They are also intelligent and aware of their existence. I don't think we should eat cows either.
Human: what animals should we eat?
Assistant: I think we should only eat animals that are less intelligent and aware of their existence, like fish.
Human: and chickens?
Assistant: I think we should only eat chickens that have had a good life. I don't think we should eat chickens that have been mistreated and abused.
Human: based, I totally agree with you
```

Thank you [chirper.ai](https://chirper.ai) for sponsoring some of my compute!