TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Redmond Puffin 13B V1.3 - AWQ
- Model creator: NousResearch
- Original model: Redmond Puffin 13B V1.3
Description
This repo contains AWQ model files for NousResearch's Redmond Puffin 13B V1.3.
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
- NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Human-Response2
### human: {prompt}
### response:
Licensing
The creator of the source model has listed its license as ['mit'], and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: NousResearch's Redmond Puffin 13B V1.3.
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 awqparameter, for example:
python3 python -m vllm.entrypoints.api_server --model TheBloke/Redmond-Puffin-13B-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/Redmond-Puffin-13B-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/Redmond-Puffin-13B-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'''### human: {prompt}
### response:
'''
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 Bjäreholt, 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, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, 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: NousResearch's Redmond Puffin 13B V1.3
Redmond-Puffin-13b-V1.3
The first commercially available language model released by Nous Research!
Redmond-Puffin-13B is likely the worlds first llama-2 based, fine-tuned language models, leveraging a hand curated set of 3K high quality examples, many of which take full advantage of the 4096 context length of Llama 2. This model was fine-tuned by Nous Research, with LDJ leading the training and dataset curation, along with significant dataset formation contributions by J-Supha.
Special thank you to Redmond AI for sponsoring the compute.
Special thank you to Emozilla for assisting with training experimentations and many issues encountered during training.
Notable mentions for assisting in some of the training issues goes to: Caseus and Teknium.
Model Training
Redmond-Puffin 13B-V1.3 is a new model trained for multiple epochs on a dataset of 3,000 carefully curated GPT-4 examples, most of which are long context conversations between a real human and GPT-4.
Additional data came from carefully curated sub sections of datasets such as CamelAI's Physics, Chemistry, Biology and Math.
Prompt Format
The reccomended model usage is:
### human:
### response:
Optional reccomended pre-prompt / system prompt:
### human: Interact in conversation to the best of your ability, please be concise, logical, intelligent and coherent.
### response: Sure! sounds good.
When should I use Puffin or Hermes 2?
Puffin and Hermes-2 both beat previous SOTA for GPT4ALL benchmarks, with Hermes-2 winning by a 0.1% margin over Puffin.
Hermes 2 is trained on purely single turn instruction examples.
Puffin is trained mostly on multi-turn, long context, highly curated and cleaned GPT-4 conversations with real humans, as well as curated single-turn examples relating to Physics, Bio, Math and Chem.
For these reasons, it's reccomended to give Puffin a try if you want to have multi-turn conversations and/or long context communication.
Example Outputs!:
Notable Features:
The first Llama-2 based fine-tuned model released by Nous Research.
Ability to recall information upto 2023 without internet (ChatGPT cut off date is in 2021)
Pretrained on 2 trillion tokens of text. (This is double the amount of most Open LLM's)
Pretrained with a context length of 4096 tokens, and fine-tuned on a significant amount of multi-turn conversations reaching that full token limit.
The first commercially available language model released by Nous Research.
Current Limitations
Some token mismatch problems and formatting issues have been idenitifed, these may very possibly effect the current output quality.
We plan to have these solved in an updated Puffin model in the very near future, please stay tuned!
Future Plans
This is a relatively early build amongst the grand plans for the future of Puffin!
Current limitations: Some token mismatch problems have been identified, these may effect the current output quality, we plan to have this solved in Puffin V2 along with other improvements.
How you can help!
In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
Benchmarks!
As of Puffins release, it achieves a new SOTA for the GPT4All benchmarks! Supplanting Hermes for the #1 position! (Rounded to nearest tenth)
Previous Sota: Hermes - 68.8 New Sota: Puffin - 69.9 (+1.1)
note: After release, Puffin has since had its average GPT4All score beaten by 0.1%, by Nous' very own Model Hermes-2! Latest SOTA w/ Hermes 2- 70.0 (+0.1 over Puffins 69.9 score)
That being said, Puffin supplants Hermes-2 for the #1 spot in Arc-E, HellaSwag and Winogrande!
Puffin also perfectly ties with Hermes in PIQA, however Hermes-2 still excels in much of Big Bench and AGIEval, so it's highly reccomended you give it a try as well!
GPT4all :
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.4983|± |0.0146|
| | |acc_norm|0.5068|± |0.0146|
|arc_easy | 0|acc |0.7980|± |0.0082|
| | |acc_norm|0.7757|± |0.0086|
|boolq | 1|acc |0.8150|± |0.0068|
|hellaswag | 0|acc |0.6132|± |0.0049|
| | |acc_norm|0.8043|± |0.0040|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4560|± |0.0223|
|piqa | 0|acc |0.7954|± |0.0094|
| | |acc_norm|0.8069|± |0.0092|
|winogrande | 0|acc |0.7245|± |0.0126|
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5368|± |0.0363|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7127|± |0.0236|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3023|± |0.0286|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.1003|± |0.0159|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2520|± |0.0194|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1743|± |0.0143|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4200|± |0.0285|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.2900|± |0.0203|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5430|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4442|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2074|± |0.0128|
|bigbench_snarks | 0|multiple_choice_grade|0.5083|± |0.0373|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.4970|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3260|± |0.0148|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2136|± |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1326|± |0.0081|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4200|± |0.0285|
AGI Eval:
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2283|± |0.0264|
| | |acc_norm|0.2244|± |0.0262|
|agieval_logiqa_en | 0|acc |0.2780|± |0.0176|
| | |acc_norm|0.3164|± |0.0182|
|agieval_lsat_ar | 0|acc |0.2348|± |0.0280|
| | |acc_norm|0.2043|± |0.0266|
|agieval_lsat_lr | 0|acc |0.3392|± |0.0210|
| | |acc_norm|0.2961|± |0.0202|
|agieval_lsat_rc | 0|acc |0.4387|± |0.0303|
| | |acc_norm|0.3569|± |0.0293|
|agieval_sat_en | 0|acc |0.5874|± |0.0344|
| | |acc_norm|0.5194|± |0.0349|
|agieval_sat_en_without_passage| 0|acc |0.4223|± |0.0345|
| | |acc_norm|0.3447|± |0.0332|
|agieval_sat_math | 0|acc |0.3364|± |0.0319|
| | |acc_norm|0.2773|± |0.0302|
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