For AutoAWQ inference, please install AutoAWQ 0.1.8 or later.
Support via Transformers is also available, but currently requires installing Transformers from Github: pip3 install git+https://github.com/huggingface/transformers.git
vLLM: version 0.2.6 is confirmed to support Mixtral AWQs.
TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!)
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.
AWQ models are supported by (note that not all of these may support Mixtral models yet - see above):
Please ensure you are using vLLM version 0.2 or later.
When using vLLM as a server, pass the --quantization awq parameter.
For example:
python3 -m vllm.entrypoints.api_server --model TheBloke/SauerkrautLM-Mixtral-8x7B-Instruct-AWQ --quantization awq --dtype auto
When using vLLM from Python code, again set quantization=awq.
For example:
python
1from vllm import LLM, SamplingParams
23prompts =[4"Tell me about AI",5"Write a story about llamas",6"What is 291 - 150?",7"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",8]9prompt_template=f'''[INST] {prompt} [/INST]
10'''1112prompts =[prompt_template.format(prompt=prompt)for prompt in prompts]1314sampling_params = SamplingParams(temperature=0.8, top_p=0.95)1516llm = LLM(model="TheBloke/SauerkrautLM-Mixtral-8x7B-Instruct-AWQ", quantization="awq", dtype="auto")1718outputs = llm.generate(prompts, sampling_params)1920# Print the outputs.21for output in outputs:22 prompt = output.prompt
23 generated_text = output.outputs[0].text
24print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0
Transformers example code (requires Transformers 4.35.0 and later)
python
1from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
23model_name_or_path ="TheBloke/SauerkrautLM-Mixtral-8x7B-Instruct-AWQ"45tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)6model = AutoModelForCausalLM.from_pretrained(7 model_name_or_path,8 low_cpu_mem_usage=True,9 device_map="cuda:0"10)1112# Using the text streamer to stream output one token at a time13streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)1415prompt ="Tell me about AI"16prompt_template=f'''[INST] {prompt} [/INST]
17'''1819# Convert prompt to tokens20tokens = tokenizer(21 prompt_template,22 return_tensors='pt'23).input_ids.cuda()2425generation_params ={26"do_sample":True,27"temperature":0.7,28"top_p":0.95,29"top_k":40,30"max_new_tokens":512,31"repetition_penalty":1.132}3334# Generate streamed output, visible one token at a time35generation_output = model.generate(36 tokens,37 streamer=streamer,38**generation_params
39)4041# Generation without a streamer, which will include the prompt in the output42generation_output = model.generate(43 tokens,44**generation_params
45)4647# Get the tokens from the output, decode them, print them48token_output = generation_output[0]49text_output = tokenizer.decode(token_output)50print("model.generate output: ", text_output)5152# Inference is also possible via Transformers' pipeline53from transformers import pipeline
5455pipe = pipeline(56"text-generation",57 model=model,58 tokenizer=tokenizer,59**generation_params
60)6162pipe_output = pipe(prompt_template)[0]['generated_text']63print("pipeline output: ", pipe_output)64
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: 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: VAGO solutions's SauerkrautLM Mixtral 8X7B Instruct
SauerkrautLM
VAGO solutions SauerkrautLM-Mixtral-8x7B-Instruct
Introducing SauerkrautLM-Mixtral-8x7B-Instruct – our Sauerkraut version of the powerful Mixtral-8x7B-Instruct!
Aligned with DPO
SauerkrautLM-Mixtral-8x7B-Instruct was trained with mix of German data augmentation and translated data.
Aligned through DPO with our new German SauerkrautLM-DPO dataset based on parts of the SFT SauerkrautLM dataset
as chosen answers and Sauerkraut-7b-HerO as rejected answers. Added with additional translated Parts of the HuggingFaceH4/ultrafeedback_binarized (Our dataset do not contain any TruthfulQA prompts - check Data Contamination Test Results) and argilla/distilabel-math-preference-dpo.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
Data Contamination Test Results
Some models on the HuggingFace leaderboard had problems with wrong data getting mixed in.
We checked our SauerkrautLM-DPO dataset with a special test [1] on a smaller model for this problem.
The HuggingFace team used the same methods [2, 3].
Our results, with result < 0.1, %: being well below 0.9, indicate that our dataset is free from contamination.
The data contamination test results of HellaSwag and Winograde will be added once [1] supports them.
[INST] Instruction [/INST] Model answer [INST] Follow-up instruction [/INST]
Evaluation
Harness
*evaluated with lm-evaluation-harness v0.3.0 - mmlu coming soon
*All benchmarks were performed with a sliding window of 4096. New Benchmarks with Sliding Window null coming soon
Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at Dr. Daryoush Vaziri. We are also grateful for your feedback and suggestions.
Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
Acknowledgement
Many thanks to argilla and Huggingface for providing such valuable datasets to the Open-Source community. And of course a big thanks to MistralAI for providing the open source community with their latest technology!
