base_model: teknium/CollectiveCognition-v1-Mistral-7B
datasets:
- CollectiveCognition/chats-data-2023-09-27
inference: false
language:
- en
license: apache-2.0
model-index:
- name: CollectiveCognition-v1-Mistral-7B
results: []
model_creator: Teknium
model_name: CollectiveCognition v1 Mistral 7B
model_type: mistral
prompt_template: |
USER: {prompt}
ASSISTANT:
quantized_by: TheBloke
tags:
- mistral-7b
- instruct
- finetune
- gpt4
- synthetic data
- distillation
- sharegpt
TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
CollectiveCognition v1 Mistral 7B - AWQ
- Model creator: Teknium
- Original model: CollectiveCognition v1 Mistral 7B
Description
This repo contains AWQ model files for Teknium's CollectiveCognition v1 Mistral 7B.
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 Llama AWQ models for high-throughput concurrent inference in multi-user server scenarios.
As of September 25th 2023, preliminary Llama-only AWQ support has also been added to Huggingface Text Generation Inference (TGI).
Note that, at the time of writing, overall throughput is still lower than running vLLM or TGI 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
- Teknium's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: User-Assistant
USER: {prompt}
ASSISTANT:
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.
Note: at the time of writing, vLLM has not yet done a new release with AWQ support.
If you try the vLLM examples below and get an error about quantization being unrecognised, or other AWQ-related issues, please install vLLM from Github source.
- When using vLLM as a server, pass the
--quantization awqparameter, for example:
python3 python -m vllm.entrypoints.api_server --model TheBloke/CollectiveCognition-v1-Mistral-7B-AWQ --quantization awq --dtype half
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/CollectiveCognition-v1-Mistral-7B-AWQ", quantization="awq", dtype="half")
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}")
Serving this model from 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/CollectiveCognition-v1-Mistral-7B-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'''USER: {prompt}
ASSISTANT:
'''
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}")
How to use this AWQ model from Python code
Install the necessary packages
Requires: AutoAWQ 0.1.1 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/CollectiveCognition-v1-Mistral-7B-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'''USER: {prompt}
ASSISTANT:
'''
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 should be possible with transformers pipeline as well in future
# But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023)
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:
TGI merged AWQ support on September 25th, 2023: TGI PR #1054. Use the :latest Docker container until the next TGI release is made.
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: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Teknium's CollectiveCognition v1 Mistral 7B
Collective Cognition v1 - Mistral 7B
Model Description:
Collective Cognition v1 is a Mistral model fine-tuned using just 100 GPT-4 chats shared on Collective Cognition.
Special Features:
- Quick Training: This model was trained in just 3 minutes on a single 4090 with a qlora, and competes with 70B scale Llama-2 Models at TruthfulQA.
- Limited Data: Despite its exceptional performance, it was trained on only ONE HUNDRED data points, all of which were gathered from Collective Cognition, a platform reminiscent of ShareGPT.
- Extreme TruthfulQA Benchmark: The collective cognition models are competing strongly with top 70B models on the TruthfulQA benchmark despite the small dataset and qlora training!
Acknowledgements:
Special thanks to @a16z and all contributors to the Collective Cognition dataset for making the development of this model possible.
Dataset:
The model was trained using data from the Collective Cognition website. The efficacy of this dataset is demonstrated by the model's stellar performance, suggesting that further expansion of this dataset could yield even more promising results. The data is reminiscent of that collected from platforms like ShareGPT.
You can contribute to the growth of the dataset by sharing your own ChatGPT chats here.
You can download the datasets created by Collective Cognition here: https://huggingface.co/CollectiveCognition
Performance:
- TruthfulQA: Collective Cognition v1 and v1.1 in particular have notably outperformed several models on the TruthfulQA benchmark, highlighting its ability to understand and rectify common misconceptions.
The model follows a LIMA approach, by minimizing the base model's original training as little as possible and giving a small but very high quality dataset to enhance it's performance and style.
Usage:
Prompt Format:
USER: <prompt>
ASSISTANT:
OR
<system message>
USER: <prompt>
ASSISTANT:
Benchmarks:
Collective Cognition v1.0 TruthfulQA:
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3794|± |0.0170|
| | |mc2 |0.5394|± |0.0158|
GPT4All Benchmark Suite:
Collective Cognition v1.0 GPT4All:
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5401|± |0.0146|
| | |acc_norm|0.5572|± |0.0145|
|arc_easy | 0|acc |0.8102|± |0.0080|
| | |acc_norm|0.7992|± |0.0082|
|boolq | 1|acc |0.8538|± |0.0062|
|hellaswag | 0|acc |0.6459|± |0.0048|
| | |acc_norm|0.8297|± |0.0038|
|openbookqa | 0|acc |0.3380|± |0.0212|
| | |acc_norm|0.4360|± |0.0222|
|piqa | 0|acc |0.8085|± |0.0092|
| | |acc_norm|0.8232|± |0.0089|
|winogrande | 0|acc |0.7451|± |0.0122|
Average: 72.06%
AGIEval:
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.1890|± |0.0246|
| | |acc_norm|0.2047|± |0.0254|
|agieval_logiqa_en | 0|acc |0.2611|± |0.0172|
| | |acc_norm|0.3134|± |0.0182|
|agieval_lsat_ar | 0|acc |0.2087|± |0.0269|
| | |acc_norm|0.2217|± |0.0275|
|agieval_lsat_lr | 0|acc |0.3373|± |0.0210|
| | |acc_norm|0.3196|± |0.0207|
|agieval_lsat_rc | 0|acc |0.4201|± |0.0301|
| | |acc_norm|0.3978|± |0.0299|
|agieval_sat_en | 0|acc |0.5971|± |0.0343|
| | |acc_norm|0.5631|± |0.0346|
|agieval_sat_en_without_passage| 0|acc |0.4029|± |0.0343|
| | |acc_norm|0.3398|± |0.0331|
|agieval_sat_math | 0|acc |0.3045|± |0.0311|
| | |acc_norm|0.2864|± |0.0305|
Average: 33.08%
Training run on wandb here: https://wandb.ai/teknium1/collectivecognition-mistral-7b/runs/collectivecognition-mistral-6/workspace
Licensing:
Apache 2.0