metadata

base_model: chargoddard/mixtralnt-4x7b-test
inference: false
license: cc-by-nc-4.0
model_creator: Charles Goddard
model_name: Mixtralnt 4X7B Test
model_type: mixtral
prompt_template: |
  {prompt}
quantized_by: TheBloke

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Mixtralnt 4X7B Test - GGUF

Model creator: Charles Goddard
Original model: Mixtralnt 4X7B Test

Description

This repo contains GGUF format model files for Charles Goddard's Mixtralnt 4X7B Test.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

EXPERIMENTAL - REQUIRES LLAMA.CPP PR

These are experimental GGUF files, created using a llama.cpp PR found here: https://github.com/ggerganov/llama.cpp/pull/4406.

THEY WILL NOT WORK WITH LLAMA.CPP FROM main, OR ANY DOWNSTREAM LLAMA.CPP CLIENT - such as LM Studio, llama-cpp-python, text-generation-webui, etc.

To test these GGUFs, please build llama.cpp from the above PR.

I have tested CUDA acceleration and it works great. I have not yet tested other forms of GPU acceleration.

Repositories available

Prompt template: Unknown

{prompt}

Explanation of quantisation methods

Click to see details

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

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
mixtralnt-4x7b-test.Q2_K.gguf	Q2_K	2	8.06 GB	10.56 GB	smallest, significant quality loss - not recommended for most purposes
mixtralnt-4x7b-test.Q3_K_S.gguf	Q3_K_S	3	10.43 GB	12.93 GB	very small, high quality loss
mixtralnt-4x7b-test.Q3_K_M.gguf	Q3_K_M	3	10.52 GB	13.02 GB	very small, high quality loss
mixtralnt-4x7b-test.Q3_K_L.gguf	Q3_K_L	3	10.61 GB	13.11 GB	small, substantial quality loss
mixtralnt-4x7b-test.Q4_0.gguf	Q4_0	4	13.62 GB	16.12 GB	legacy; small, very high quality loss - prefer using Q3_K_M
mixtralnt-4x7b-test.Q4_K_S.gguf	Q4_K_S	4	13.62 GB	16.12 GB	small, greater quality loss
mixtralnt-4x7b-test.Q4_K_M.gguf	Q4_K_M	4	13.64 GB	16.14 GB	medium, balanced quality - recommended
mixtralnt-4x7b-test.Q5_0.gguf	Q5_0	5	16.63 GB	19.13 GB	legacy; medium, balanced quality - prefer using Q4_K_M
mixtralnt-4x7b-test.Q5_K_S.gguf	Q5_K_S	5	16.63 GB	19.13 GB	large, low quality loss - recommended
mixtralnt-4x7b-test.Q5_K_M.gguf	Q5_K_M	5	16.63 GB	19.13 GB	large, very low quality loss - recommended
mixtralnt-4x7b-test.Q6_K.gguf	Q6_K	6	19.82 GB	22.32 GB	very large, extremely low quality loss
mixtralnt-4x7b-test.Q8_0.gguf	Q8_0	8	25.67 GB	28.17 GB	very large, extremely low quality loss - not recommended

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 download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download TheBloke/mixtralnt-4x7b-test-GGUF mixtralnt-4x7b-test.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download TheBloke/mixtralnt-4x7b-test-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/mixtralnt-4x7b-test-GGUF mixtralnt-4x7b-test.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Example `llama.cpp` command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m mixtralnt-4x7b-test.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"

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

Change -c 32768 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

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

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in `text-generation-webui`

Not currently supported

How to run from Python code

Not currently supported

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!

Thanks to Clay from gpus.llm-utils.org!

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.

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Special thanks to: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Charles Goddard's Mixtralnt 4X7B Test

Mixtraln't 4x7B

Oh boy, a new model architecture in Transformers! Time to do profane things with it.

What if instead of training a MoE from scratch, we took some pre-trained Mistral models and shoved them in a little clown car?

Uses parts from the following models:

Works and generates coherent text. The big question here is if the hack I used to populate the MoE gates works well enough to take advantage of all of the experts. Let's find out!

Prompt format: maybe alpaca??? or chatml??? life is full of mysteries