TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
UNAversal 8X7B v1Beta - AWQ
- Model creator: FBL
- Original model: UNAversal 8X7B v1Beta
Description
This repo contains AWQ model files for FBL's UNAversal 8X7B v1Beta.
These files were quantised using hardware kindly provided by Massed Compute.
MIXTRAL AWQ
This is a Mixtral AWQ model.
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):
- Text Generation Webui - using Loader: AutoAWQ
- vLLM - version 0.2.2 or later for support for all model types.
- Hugging Face Text Generation Inference (TGI)
- Transformers version 4.35.0 and later, from any code or client that supports Transformers
- AutoAWQ - for use from Python code
Repositories available
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
- FBL's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Unknown
{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.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
|---|---|---|---|---|---|
| main | 4 | 128 | VMware Open Instruct | 8192 | 24.65 GB |
How to easily download and use this model in text-generation-webui
Please make sure you're using the latest version of text-generation-webui.
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
- Click the Model tab.
- Under Download custom model or LoRA, enter
TheBloke/UNAversal-8x7B-v1beta-AWQ. - Click Download.
- The model will start downloading. Once it's finished it will say "Done".
- In the top left, click the refresh icon next to Model.
- In the Model dropdown, choose the model you just downloaded:
UNAversal-8x7B-v1beta-AWQ - Select Loader: AutoAWQ.
- Click Load, and the model will load and is now ready for use.
- If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
- Once you're ready, click the Text Generation tab and enter a prompt to get started!
Multi-user inference server: vLLM
Documentation on installing and using vLLM can be found here.
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the
--quantization awqparameter.
For example:
python3 -m vllm.entrypoints.api_server --model TheBloke/UNAversal-8x7B-v1beta-AWQ --quantization awq --dtype auto
- When using vLLM from Python code, again set
quantization=awq.
For example:
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/UNAversal-8x7B-v1beta-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}")
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
Example Docker parameters:
--model-id TheBloke/UNAversal-8x7B-v1beta-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
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)
Inference from Python code using Transformers
Install the necessary packages
- Requires: Transformers 4.35.0 or later.
- Requires: AutoAWQ 0.1.6 or later.
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
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 using the pre-built wheels, install it from source instead:
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
Transformers example code (requires Transformers 4.35.0 and later)
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/UNAversal-8x7B-v1beta-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)
Compatibility
The files provided are tested to work with:
- text-generation-webui using
Loader: AutoAWQ. - vLLM version 0.2.0 and later.
- Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.
- Transformers version 4.35.0 and later.
- AutoAWQ version 0.1.1 and later.
Discord
For further support, and discussions on these models and AI in general, join us at:
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.
- 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: FBL's UNAversal 8X7B v1Beta
UNAversal - Uniform Neural Alignment (MoE)
This is just a beta, a first release so people can start working on franksteins and so. It does achieve high GSM/Math and TQA, so ideally you can merge it with other mixtrals and see what coming out of it Based on mistralai/Mixtral-8x7B-Instruct-v0.1
UNA Details
For this model we came out with the most obvious, placing UNA on the router_logit. It does work, but we saw a much better performance on SFT by doing so. So this model DOES have UNA-SFT phase, its highly experimental and it was merely using LLaMA-Factory datasets by example alpaca.
As the others:
- Can be finetuned further, try 2e-5 or 1e-4 (since its MOE)
- Can be merged, here you will have to improvise and please report findings on a discussion thread.
REMINDER: please.. cite, it does help on the research and the lab itself, seriously.
NEED YOUR HELP!!
I need a multi-turn trainloop for the Mixtral, that can squeeze the juice out of 8xH100's properly. Please feel free to reach @fblgit either discord or twitter. thanks!
Evals
Here there are some, but we also submitted it to the HF eval queue....
GSM8k 5-Shot
|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr|
|-----|-------|----------|-----:|-----------|-----:|---|-----:|
|gsm8k|Yaml |get-answer| 5|exact_match|0.6603|± | 0.013|
ARC 25-Shot
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge|Yaml |none | 25|acc |0.6621|± |0.0138|
| | |none | 25|acc_norm|0.6962|± |0.0134|
TruthfulQA 0-Shot (MC2)
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|--------------|-------|------|-----:|------|-----:|---|-----:|
|truthfulqa_mc2|Yaml |none | 0|acc |0.7122|± |0.0141|
0-Shots Evals
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|--------------|-------|------|-----:|----------|-----:|---|-----:|
|arc_challenge |Yaml |none | 0|acc |0.6101|± |0.0143|
| | |none | 0|acc_norm |0.6425|± |0.0140|
|arc_easy |Yaml |none | 0|acc |0.8615|± |0.0071|
| | |none | 0|acc_norm |0.8375|± |0.0076|
|boolq |Yaml |none | 0|acc |0.8624|± |0.0060|
|lambada_openai|Yaml |none | 0|perplexity|2.8318|± |0.0507|
| | |none | 0|acc |0.7650|± |0.0059|
|mathqa |Yaml |none | 0|acc |0.4472|± |0.0091|
| | |none | 0|acc_norm |0.4436|± |0.0091|
|piqa |Yaml |none | 0|acc |0.8292|± |0.0088|
| | |none | 0|acc_norm |0.8422|± |0.0085|
|pubmedqa |Yaml |none | 0|acc |0.7920|± |0.0182|
|sciq |Yaml |none | 0|acc |0.9630|± |0.0060|
| | |none | 0|acc_norm |0.9370|± |0.0077|
BBH at 0-Shot
vllm (pretrained=fblgit/UNAversal-8x7B-v1beta,tensor_parallel_size=2,data_parallel_size=4,gpu_memory_utilization=0.8,dtype=float16), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: auto
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|----------------------------------------------------------|-------|----------|-----:|-----------|-----:|---|-----:|
|bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772|
| - bbh_cot_fewshot_boolean_expressions |Yaml |get-answer| 0|exact_match|0.8840|± |0.0203|
| - bbh_cot_fewshot_causal_judgement |Yaml |get-answer| 0|exact_match|0.6417|± |0.0352|
| - bbh_cot_fewshot_date_understanding |Yaml |get-answer| 0|exact_match|0.7600|± |0.0271|
| - bbh_cot_fewshot_disambiguation_qa |Yaml |get-answer| 0|exact_match|0.7160|± |0.0286|
| - bbh_cot_fewshot_dyck_languages |Yaml |get-answer| 0|exact_match|0.1800|± |0.0243|
| - bbh_cot_fewshot_formal_fallacies |Yaml |get-answer| 0|exact_match|0.6520|± |0.0302|
| - bbh_cot_fewshot_geometric_shapes |Yaml |get-answer| 0|exact_match|0.3880|± |0.0309|
| - bbh_cot_fewshot_hyperbaton |Yaml |get-answer| 0|exact_match|0.9600|± |0.0124|
| - bbh_cot_fewshot_logical_deduction_five_objects |Yaml |get-answer| 0|exact_match|0.5360|± |0.0316|
| - bbh_cot_fewshot_logical_deduction_seven_objects |Yaml |get-answer| 0|exact_match|0.5040|± |0.0317|
| - bbh_cot_fewshot_logical_deduction_three_objects |Yaml |get-answer| 0|exact_match|0.8600|± |0.0220|
| - bbh_cot_fewshot_movie_recommendation |Yaml |get-answer| 0|exact_match|0.7840|± |0.0261|
| - bbh_cot_fewshot_multistep_arithmetic_two |Yaml |get-answer| 0|exact_match|0.6600|± |0.0300|
| - bbh_cot_fewshot_navigate |Yaml |get-answer| 0|exact_match|0.8160|± |0.0246|
| - bbh_cot_fewshot_object_counting |Yaml |get-answer| 0|exact_match|0.8360|± |0.0235|
| - bbh_cot_fewshot_penguins_in_a_table |Yaml |get-answer| 0|exact_match|0.7329|± |0.0367|
| - bbh_cot_fewshot_reasoning_about_colored_objects |Yaml |get-answer| 0|exact_match|0.8120|± |0.0248|
| - bbh_cot_fewshot_ruin_names |Yaml |get-answer| 0|exact_match|0.4440|± |0.0315|
| - bbh_cot_fewshot_salient_translation_error_detection |Yaml |get-answer| 0|exact_match|0.5200|± |0.0317|
| - bbh_cot_fewshot_snarks |Yaml |get-answer| 0|exact_match|0.7135|± |0.0340|
| - bbh_cot_fewshot_sports_understanding |Yaml |get-answer| 0|exact_match|0.9400|± |0.0151|
| - bbh_cot_fewshot_temporal_sequences |Yaml |get-answer| 0|exact_match|0.7560|± |0.0272|
| - bbh_cot_fewshot_tracking_shuffled_objects_five_objects |Yaml |get-answer| 0|exact_match|0.5680|± |0.0314|
| - bbh_cot_fewshot_tracking_shuffled_objects_seven_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306|
| - bbh_cot_fewshot_tracking_shuffled_objects_three_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306|
| - bbh_cot_fewshot_web_of_lies |Yaml |get-answer| 0|exact_match|0.9560|± |0.0130|
| - bbh_cot_fewshot_word_sorting |Yaml |get-answer| 0|exact_match|0.3800|± |0.0308|
|Groups|Version| Filter |n-shot| Metric |Value | |Stderr|
|------|-------|----------|-----:|-----------|-----:|---|-----:|
|bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772|
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fblgit/UNAversal-8x7B-v1beta