README.md

---
license: gpl
datasets:
- nomic-ai/gpt4all-j-prompt-generations
language:
- en
inference: false
---
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# GPT4All-13B-snoozy-GGML

These files are GGML format model files of [Nomic.AI's GPT4all-13B-snoozy](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy).

GGML files are for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).

## Repositories available

* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/GPT4ALL-13B-snoozy-GPTQ).
* [4bit and 5bit GGML models for GPU inference](https://huggingface.co/TheBloke/GPT4ALL-13B-snoozy-GGML).
* [Nomic.AI's original model in float32 HF for GPU inference](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy).

## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!

llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508

I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them.

For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`.

## Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
`GPT4All-13B-snoozy.ggmlv3.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | 4-bit. |
`GPT4All-13B-snoozy.ggmlv3.q4_1.bin` | q4_1 | 4bit | 8.95GB | 11.5GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
`GPT4All-13B-snoozy.ggmlv3.q5_0.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | 5-bit. Higher accuracy, higher resource usage and slower inference.  |
`GPT4All-13B-snoozy.ggmlv3.q5_1.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | 5-bit. Even higher accuracy, higher resource usage and slower inference. |
`GPT4All-13B-snoozy.ggmlv3.q8_0.bin` | q5_1 | 5bit | 9.76GB | 17GB | 5-bit. Even higher accuracy, higher resource usage and slower inference. |

## How to run in `llama.cpp`

I use the following command line; adjust for your tastes and needs:

```
./main -t 12 -m GPT4All-13B-snoozy.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a story about llamas
### Response:"
```
Change `-t 12` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).

## Want to support my work?

I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.

So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.

Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.

* Patreon: coming soon! (just awaiting approval)
* Ko-Fi: https://ko-fi.com/TheBlokeAI
* Discord: https://discord.gg/UBgz4VXf
# Original Model Card for GPT4All-13b-snoozy

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.

## Model Details

### Model Description

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

This model has been finetuned from LLama 13B

- **Developed by:** [Nomic AI](https://home.nomic.ai)
- **Model Type:** A finetuned LLama 13B model on assistant style interaction data
- **Language(s) (NLP):** English
- **License:** Apache-2
- **Finetuned from model [optional]:** LLama 13B

This model was trained on `nomic-ai/gpt4all-j-prompt-generations` using `revision=v1.3-groovy`

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [https://github.com/nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
- **Base Model Repository:** [https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama)
- **Demo [optional]:** [https://gpt4all.io/](https://gpt4all.io/)


### Results

Results on common sense reasoning benchmarks

```
  Model                     BoolQ       PIQA     HellaSwag   WinoGrande    ARC-e      ARC-c       OBQA
  ----------------------- ---------- ---------- ----------- ------------ ---------- ---------- ----------
  GPT4All-J 6B v1.0          73.4       74.8       63.4         64.7        54.9       36.0       40.2
  GPT4All-J v1.1-breezy      74.0       75.1       63.2         63.6        55.4       34.9       38.4
  GPT4All-J v1.2-jazzy       74.8       74.9       63.6         63.8        56.6       35.3       41.0
  GPT4All-J v1.3-groovy      73.6       74.3       63.8         63.5        57.7       35.0       38.8
  GPT4All-J Lora 6B          68.6       75.8       66.2         63.5        56.4       35.7       40.2
  GPT4All LLaMa Lora 7B      73.1       77.6       72.1         67.8        51.1       40.4       40.2
  GPT4All 13B snoozy        *83.3*      79.2       75.0        *71.3*       60.9       44.2       43.4
  Dolly 6B                   68.8       77.3       67.6         63.9        62.9       38.7       41.2
  Dolly 12B                  56.7       75.4       71.0         62.2       *64.6*      38.5       40.4
  Alpaca 7B                  73.9       77.2       73.9         66.1        59.8       43.3       43.4
  Alpaca Lora 7B             74.3      *79.3*      74.0         68.8        56.6       43.9       42.6
  GPT-J 6B                   65.4       76.2       66.2         64.1        62.2       36.6       38.2
  LLama 7B                   73.1       77.4       73.0         66.9        52.5       41.4       42.4
  LLama 13B                  68.5       79.1      *76.2*        70.1        60.0      *44.6*      42.2
  Pythia 6.9B                63.5       76.3       64.0         61.1        61.3       35.2       37.2
  Pythia 12B                 67.7       76.6       67.3         63.8        63.9       34.8       38.0
  Vicuña T5                  81.5       64.6       46.3         61.8        49.3       33.3       39.4
  Vicuña 13B                 81.5       76.8       73.3         66.7        57.4       42.7       43.6
  Stable Vicuña RLHF         82.3       78.6       74.1         70.9        61.0       43.5      *44.4*
  StableLM Tuned             62.5       71.2       53.6         54.8        52.4       31.1       33.4
  StableLM Base              60.1       67.4       41.2         50.1        44.9       27.0       32.0
```