🚀 Mythalion Kimiko v2 - AWQ

This repository provides AWQ model files for nRuaif's Mythalion Kimiko v2, enabling efficient and fast low - bit weight quantization inference.

🚀 Quick Start

This README offers comprehensive guidance on using the AWQ model of Mythalion Kimiko v2, including installation, inference, and compatibility information.

✨ Features

• AWQ Quantization: AWQ is an efficient, accurate, and fast low - bit weight quantization method, currently supporting 4 - bit quantization. It provides faster inference on Transformer - based models compared to GPTQ, with equivalent or better quality.
• Multiple Inference Support: Supported by various inference frameworks such as Text Generation Webui, vLLM, Hugging Face Text Generation Inference (TGI), Transformers, and AutoAWQ.

📦 Installation

Install from text - generation - webui

1. Ensure you are using the latest version of [text - generation - webui](https://github.com/oobabooga/text - generation - webui). It is recommended to use the one - click installers.
2. Click the Model tab.
3. Under Download custom model or LoRA, enter TheBloke/Mythalion - Kimiko - v2 - AWQ.
4. Click Download.
5. After the download is complete, click the refresh icon next to Model in the top left.
6. In the Model dropdown, select Mythalion - Kimiko - v2 - AWQ.
7. Select Loader: AutoAWQ.
8. Click Load to load the model.

Install Required Python Packages for Python Inference

• Requires Transformers 4.35.0 or later.
• Requires [AutoAWQ](https://github.com/casper - hansen/AutoAWQ) 0.1.6 or later.

pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"

If you are using CUDA 11.8 and want to continue using PyTorch 2.0.1:

pip3 install https://github.com/casper - hansen/AutoAWQ/releases/download/v0.1.6/autoawq - 0.1.6+cu118 - cp310 - cp310 - linux_x86_64.whl

If you have problems installing [AutoAWQ](https://github.com/casper - hansen/AutoAWQ) using pre - built wheels:

pip3 uninstall -y autoawq
git clone https://github.com/casper - hansen/AutoAWQ
cd AutoAWQ
pip3 install .

💻 Usage Examples

Use in text - generation - webui

After installation, click the Text Generation tab and enter a prompt to start generating text.

Use in vLLM

from vllm import LLM, SamplingParams

prompts = [
    "Tell me about AI",
    "Write a story about llamas",
    "What is 291 - 150?",
    "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''{prompt}
'''

prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/Mythalion - Kimiko - v2 - AWQ", quantization="awq", dtype="auto")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Use in Hugging Face Text Generation Inference (TGI)

First, install the required Python package:

pip3 install huggingface - hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your - endpoint - url - here"

prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens=128,
                                  do_sample=True,
                                  temperature=0.7,
                                  top_p=0.95,
                                  top_k=40,
                                  repetition_penalty=1.1)

print(f"Model output: ", response)

Use in Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_name_or_path = "TheBloke/Mythalion - Kimiko - v2 - AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)

# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''

# Convert prompt to tokens
tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

generation_params = {
    "do_sample": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "max_new_tokens": 512,
    "repetition_penalty": 1.1
}

# Generate streamed output, visible one token at a time
generation_output = model.generate(
    tokens,
    streamer=streamer,
    **generation_params
)

# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
    tokens,
    **generation_params
)

# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)

# Inference is also possible via Transformers' pipeline
from transformers import pipeline

pipe = pipeline(
    "text - generation",
    model=model,
    tokenizer=tokenizer,
    **generation_params
)

pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)

📚 Documentation

Model Information

• Model creator: nRuaif
• Original model: [Mythalion Kimiko v2](https://huggingface.co/nRuaif/Mythalion - Kimiko - v2)

Repositories available

• [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mythalion - Kimiko - v2 - AWQ)
• [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mythalion - Kimiko - v2 - GPTQ)
• [2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mythalion - Kimiko - v2 - GGUF)
• [nRuaif's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/nRuaif/Mythalion - Kimiko - v2)

Prompt template

{prompt}

Provided files, and AWQ parameters

I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.

Models are released as sharded safetensors files.

Compatibility

The files provided are tested to work with:

• [text - generation - webui](https://github.com/oobabooga/text - generation - webui) using Loader: AutoAWQ.
• [vLLM](https://github.com/vllm - project/vllm) version 0.2.0 and later.
• [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text - generation - inference) version 1.1.0 and later.
• Transformers version 4.35.0 and later.
• [AutoAWQ](https://github.com/casper - hansen/AutoAWQ) version 0.1.1 and later.

🔧 Technical Details

About AWQ

AWQ is an efficient, accurate and blazing - fast low - bit weight quantization method, currently supporting 4 - bit quantization. Compared to GPTQ, it offers faster Transformers - based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

• [Text Generation Webui](https://github.com/oobabooga/text - generation - webui) - using Loader: AutoAWQ
• [vLLM](https://github.com/vllm - project/vllm) - version 0.2.2 or later for support for all model types.
• [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text - generation - inference)
• Transformers version 4.35.0 and later, from any code or client that supports Transformers
• [AutoAWQ](https://github.com/casper - hansen/AutoAWQ) - for use from Python code

📄 License

The license for this model is other.

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](llm - utils)!

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: Aemon Algiz.

Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel - Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann - Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: nRuaif's Mythalion Kimiko v2

No original model card was available.