TheBloke's picture
Update README.md
14bf9bb
|
raw
history blame
8.33 kB
metadata
license: other
datasets:
  - nomic-ai/gpt4all-j-prompt-generations
language:
  - en
inference: false
TheBlokeAI

GPT4All-13B-snoozy-GGML

These files are GGML format model files of Nomic.AI's GPT4all-13B-snoozy.

GGML files are for CPU inference using llama.cpp.

Repositories available

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.

Discord

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

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the 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 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!

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

This model has been finetuned from LLama 13B

  • Developed by: 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]

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