This repo contains GGUF format model files for SkunkworksAI's Mistralic 7B-1.

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.

Here is an incomplate list of clients and libraries that are known to support GGUF:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### System: {system_message}

### Instruction: {prompt}

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

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.

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.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

Under Download Model, you can enter the model repo: TheBloke/Mistralic-7B-1-GGUF and below it, a specific filename to download, such as: mistralic-7b-1.Q4_K_M.gguf.

Then click Download.

I recommend using the huggingface-hub Python library:

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

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

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

Change -c 2048 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.

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

Further instructions here: text-generation-webui/docs/llama.cpp.md.

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

Run one of the following commands, according to your system:

Here are guides on using llama-cpp-python and ctransformers with LangChain:

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

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.

Special thanks to: Aemon Algiz.

Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Special thanks to Together Compute for sponsoring Skunkworks with compute!

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device('cuda')
system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"

def generate_prompt(instruction, input=None):
    if input:
      prompt = f"### System:\n{system_prompt}\n\n"
    else:
      prompt = f"### System:\n{system_no_input_prompt}\n\n"


    prompt += f"### Instruction:\n{instruction}\n\n"


    if input:
        prompt += f"### Input:\n{input}\n\n"
    return prompt + """### Response:\n"""


device = "cuda"

model = AutoModelForCausalLM.from_pretrained("SkunkworksAI/Mistralic-7B-1")
tokenizer = AutoTokenizer.from_pretrained("SkunkworksAI/Mistralic-7B-1")


while True:
    instruction = input("Enter Instruction: ")
    instruction = generate_prompt(instruction)
    inputs = tokenizer(instruction, return_tensors="pt", return_attention_mask=False)

    outputs = model.generate(**inputs, max_length=1000, do_sample=True, temperature=0.01, use_cache=True, eos_token_id=tokenizer.eos_token_id)
    text = tokenizer.batch_decode(outputs)[0]
    print(text)

Average: 0.72157

For comparison:

mistralai/Mistral-7B-v0.1 scores 0.7116

mistralai/Mistral-7B-Instruct-v0.1 scores 0.6794