metadata

inference: false
license: other
datasets:
  - jondurbin/airoboros-gpt4-1.2

Want to contribute? TheBloke's Patreon page

John Durbin's Airoboros 7B GPT4 1.2 GGML

These files are GGML format model files for John Durbin's Airoboros 7B GPT4 1.2.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

Repositories available

Prompt template

A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
USER: prompt
ASSISTANT:

Compatibility

Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`

I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit 2d5db48.

These are guaranteed to be compatbile with any UIs, tools and libraries released since late May.

New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`

These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.

They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt.

Explanation of the new k-quant methods

The new methods available are:

GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
airoboros-7b-gpt4-1.2.ggmlv3.q2_K.bin	q2_K	2	2.87 GB	5.37 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
airoboros-7b-gpt4-1.2.ggmlv3.q3_K_L.bin	q3_K_L	3	3.60 GB	6.10 GB	New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
airoboros-7b-gpt4-1.2.ggmlv3.q3_K_M.bin	q3_K_M	3	3.28 GB	5.78 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
airoboros-7b-gpt4-1.2.ggmlv3.q3_K_S.bin	q3_K_S	3	2.95 GB	5.45 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
airoboros-7b-gpt4-1.2.ggmlv3.q4_0.bin	q4_0	4	3.79 GB	6.29 GB	Original llama.cpp quant method, 4-bit.
airoboros-7b-gpt4-1.2.ggmlv3.q4_1.bin	q4_1	4	4.21 GB	6.71 GB	Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
airoboros-7b-gpt4-1.2.ggmlv3.q4_K_M.bin	q4_K_M	4	4.08 GB	6.58 GB	New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
airoboros-7b-gpt4-1.2.ggmlv3.q4_K_S.bin	q4_K_S	4	3.83 GB	6.33 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
airoboros-7b-gpt4-1.2.ggmlv3.q5_0.bin	q5_0	5	4.63 GB	7.13 GB	Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
airoboros-7b-gpt4-1.2.ggmlv3.q5_1.bin	q5_1	5	5.06 GB	7.56 GB	Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
airoboros-7b-gpt4-1.2.ggmlv3.q5_K_M.bin	q5_K_M	5	4.78 GB	7.28 GB	New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
airoboros-7b-gpt4-1.2.ggmlv3.q5_K_S.bin	q5_K_S	5	4.65 GB	7.15 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
airoboros-7b-gpt4-1.2.ggmlv3.q6_K.bin	q6_K	6	5.53 GB	8.03 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
airoboros-7b-gpt4-1.2.ggmlv3.q8_0.bin	q8_0	8	7.16 GB	9.66 GB	Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to run in `llama.cpp`

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

./main -t 10 -ngl 32 -m airoboros-7b-gpt4-1.2.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.\nUSER: write a story about llamas\nASSISTANT:"

Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

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

Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

Patreon special mentions: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke.

Thank you to all my generous patrons and donaters!

Original model card: John Durbin's Airoboros 7B GPT4 1.2

Overview

This is a qlora fine-tuned 7b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros

This is mostly an extension of 1.1, but with thousands of new training data and an update to allow "PLAINFORMAT" at the end of coding prompts to just print the code without backticks or explanations/usage/etc.

The dataset used to fine-tune this model is available here, with a specific focus on:

coding
math/reasoning (using orca style ELI5 instruction/response pairs)
trivia
role playing
multiple choice and fill-in-the-blank
context-obedient question answering
theory of mind
misc/general

This model was fine-tuned with a fork of qlora, which among other things was updated to use a slightly modified vicuna template to be compatible with the previous versions:

A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:

So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).

Usage

To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a --no-history option to prevent input tokenization errors.

pip install git+https://github.com/jondurbin/FastChat

Be sure you are pulling the latest branch!

Then, you can invoke it like so (after downloading the model):

python -m fastchat.serve.cli \
  --model-path airoboros-7b-gpt4-1.2 \
  --temperature 0.5 \
  --max-new-tokens 2048 \
  --no-history

Alternatively, please check out TheBloke's quantized versions:

Coding updates from gpt4/1.1:

I added a few hundred instruction/response pairs to the training data with "PLAINFORMAT" as a single, all caps term at the end of the normal instructions, which produce plain text output instead of markdown/backtick code formatting.

It's not guaranteed to work all the time, but mostly it does seem to work as expected.

So for example, instead of:

Implement the Snake game in python.

You would use:

Implement the Snake game in python.  PLAINFORMAT

Other updates from gpt4/1.1:

Several hundred role-playing data.
A few thousand ORCA style reasoning/math questions with ELI5 prompts to generate the responses (should not be needed in your prompts to this model however, just ask the question).
Many more coding examples in various languages, including some that use specific libraries (pandas, numpy, tensorflow, etc.)