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TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
OpenOrca Stx - AWQ
- Model creator: Lightblue Technology Inc.
- Original model: OpenOrca Stx
Description
This repo contains AWQ model files for Lightblue Technology Inc.'s OpenOrca Stx.
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.
It is also now supported by continuous batching server vLLM, allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
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
- Lightblue Technology Inc.'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: None
{prompt}
Provided files and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
Serving this model from vLLM
Documentation on installing and using vLLM can be found here.
- When using vLLM as a server, pass the
--quantization awq
parameter, for example:
python3 python -m vllm.entrypoints.api_server --model TheBloke/OpenOrca_Stx-AWQ --quantization awq
When using vLLM from Python code, pass the quantization=awq
parameter, for example:
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/OpenOrca_Stx-AWQ", quantization="awq")
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}")
How to use this AWQ model from Python code
Install the necessary packages
Requires: AutoAWQ 0.0.2 or later
pip3 install autoawq
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 .
You can then try the following example code
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/OpenOrca_Stx-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
# Inference can also be done using transformers' pipeline
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
Compatibility
The files provided are tested to work with AutoAWQ, and vLLM.
Huggingface Text Generation Inference (TGI) is not yet compatible with AWQ, but a PR is open which should bring support soon: TGI PR #781.
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: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjรคreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, ์ค๊ต ๊น, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, ้ฟๆ, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Lightblue Technology Inc.'s OpenOrca Stx
About
This model is Lightblue's QLoRA finetune of OpenOrca's Open-Orca/OpenOrcaxOpenChat-Preview2-13B model on Japanese fine-tuning datasets.
This model specialises on answering Closed Question Answering in Japanese. Input a piece of reference text, ask a question, and see the model answer based on the reference text.
We trained on equal samples of the following three datasets:
which resulted in a dataset of 13,167 samples total.
These three datasets were chosen as they represent three distinct fine-tuning tasks (Text simplification, question answering, and text summarization, respectively) which we hypothesize can help to improve the language models suitability for dealing with Japanese data. These three datasets make up the model name: STX.
With these datasets, we achieve the following scores on the JGLUE benchmark:
Model Name | Open-Orca/OpenOrcaxOpenChat-Preview2-13B | lightblue/openorca_stx |
---|---|---|
jsquad-1.1-0.3 | 0.692 | 0.836 |
jcommonsenseqa-1.1-0.3 | 0.831 | 0.782 |
jnli-1.1-0.3 | 0.504 | 0.48 |
marc_ja-1.1-0.3 | 0.936 | 0.959 |
Our model achieves much better results on the question answering benchmark (JSQuAD) than the base checkpoint without monstrous degradation of performance on multi-choice question benchmarks (JCommonSense, JNLI, MARC-Ja) purely through QLoRA training. This shows the potential for applying strong language models such as Open-Orca/OpenOrcaxOpenChat-Preview2-13B to minimal QLoRA fine-tuning using Japanese fine-tuning datasets to achieve better results at narrow NLP tasks.
How to use
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(
model_dir, torch_dtype=torch.bfloat16, device_map='auto',
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
def do_closed_qa(context, question):
return context + "\n\n" + question
test_article = """ใใขใใใใฎใฌใใผใใชใผใซใใชใผใใปใใคใฑใซ้ธๆใใใใใฌใคใถใผใฉใขใณRGใใใๆฌไบบๅ
ฌ่ชใฎใขใใใใงใใใใฉใฐใใผใใกใณใฎๅๅฟใซๅฐใ้ฉใใใใใงใใ
ใใชใผใใปใใคใฑใซ้ธๆใฎใขใใใใฏใไฝใใใฃใใใงใใใ
ใ2015ๅนดใฎใฏใผใซใใซใใ๏ผWๆฏ๏ผใคใณใฐใฉใณใๅคงไผใงๆฅๆฌใๅใขใใชใซใๅใใๆฌกใฎๆฅใใไบฌ้ฝใงใฎ็ช็ตใญใฑใงใใใๅฝๆใฏใใขใใใซใฎๅ
ฑๅๅตๆฅญ่
ในใใฃใผใใปใธใงใใบใฎใขใใใใฐใใใงใใใใไธ็ทใซใญใฑใใใฆใใใธใฃใณใฐใซใใฑใใใใใใชใผใใปใใคใฑใซใซไผผใฆใพใใใใธใงใใบใฎใพใพใใใใใใใใชใใงใใ๏ผใใจ่จใใใใฎใๅงใพใใงใใ
ใใใ ใใฟใใช็ฅ่ญใใชใใใฉใฐใใผใทใงใใใๆขใใๆฅๆฌไปฃ่กจใฎใฆใใใผใ ใๅฃฒใๅใใ ใฃใใฎใงใ่ตคใฃใฝใใฆใใใผใ ใจใใใใใฎ็ญใใณใใฏใใฆใใจใใใใSNSใงใใชใผใใปใใคใฑใซใงใใใฃใฆใใฃใฑใๅ็ใ่ผใใพใใใ
ใใใใจใใใใ่ฆใใชใผใใใๆฌไบบใใDM๏ผใใคใฌใฏใใกใใปใผใธ๏ผใๅฑใใพใใใใใขใใใใใใใจใใใใใพใใใใใขใใใใใใใชใใๅใฎใฆใใใผใ ใ้ใใพใใฎใง็ใฆใใ ใใใใจใWๆฏๅพใซใฆใใใผใ 2็ใจใใณใใใฝใใฏในใชใฉใใปใใพใซ้ใฃใฆใใฆใใใพใใใไป็ใฆใใใฎใใใใงใใ
ใใใพใงใๆฐใ
ใฎ่ๅไบบใใขใใใใใฆใใใใพใใใใชใผใ้ธๆใฎใใฟใฎๅ้ฟใฏใใใใงใใใใ
ใใๅใฏใฉใฐใใผ็ต้จใใชใใงใใใใฉใฐใใผใๅ
จ็ถ็ฅใใชใใฃใใใฉใใใฃใฑใๆฌไบบใใใฆใใใผใ ใ้ ใใฆใใฃใฆใใโๅฐ็ฑ ๏ผใใใใ๏ผโใฟใใใชใฎใใใฃใฆใใใใใคใฏใชใผใใใๆฌไบบใซ่ชใใใใฆใใใจใไธ็ฎ็ฝฎใใใฆใใใฎใใชใจๆใใพใใ
ใใใใฃใฆใใใใจใฏใ่ฆใ็ฎใๆฌไบบใซๅฏใใฆใฏใณใใผใ ใฃใฆ่จใใ ใใชใใงใใใฉใญใใใใงใใใใใใชใผใใใใ ใใจ่จใฃใฆใใใใพใใ
ใใใชใผใใใใจๅฎ้ใซไผใใใจใชใใฆใ็ฐกๅใซใฏใงใใชใใใใชใใงใใใใงใใใชใผใใใใฎใพใญใใใฆใใRGใซใฏไผใใใใใฟใใใช๏ผ็ฌ๏ผใไฝใ ใใใชใๆๅใช็ฅ็คพใฎๆฏ็คพใฎใใใชๅญๅจใงใใใญใใใใใใใใใใจใใๆๅณใงใฏไปใฎใขใใใใจใฏใใใ้ใใพใใญใ
"""
test_question = "ใใชใผใใปใใคใฑใซใฏไฝใ้ใฃใฆใใพใใใ๏ผ"
pipe(do_closed_qa(test_article, question), max_new_tokens=128, temperature=0)[0]["generated_text"]
# "ใฆใใใผใ 2็ใจใใณใใใฝใใฏในใชใฉ"
Training details
This model was trained for 1000 steps (1.2 epochs) with the model being evaluated every 50 steps. We then chose the best model from these evaluations based on validation loss. We used the qlora package from artidoro. We trained with the following hyperparameters:
Per device evaluation batch size: 16
Per device train batch size: 8
LoRA (lora_r): 64
LoRA alpha (lora_alpha): 16
LoRA modules: all
Double quantization: Enabled
Quantization type: nf4
BF16: Enabled
Bits: 4
Warmup ratio: 0.03
Learning rate scheduler type: Constant
Gradient checkpointing: Enabled
Gradient accumulation steps: 2
Learning rate: 0.0002
Adam beta2: 0.999
Maximum gradient norm: 0.3
LoRA dropout: 0.05
Weight decay: 0.0
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Base model
lightblue/openorca_stx