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zephyr-7B-beta-AWQ

Text Generation
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Safetensors
English
mistral
Generated from Trainer
conversational
text-generation-inference
4-bit precision
awq
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metadata
base_model: HuggingFaceH4/zephyr-7b-beta
datasets:
  - HuggingFaceH4/ultrachat_200k
  - HuggingFaceH4/ultrafeedback_binarized
inference: false
language:
  - en
license: mit
model-index:
  - name: zephyr-7b-beta
    results: []
model_creator: Hugging Face H4
model_name: Zephyr 7B Beta
model_type: mistral
prompt_template: |
  <|system|>
  </s>
  <|user|>
  {prompt}</s>
  <|assistant|>
quantized_by: TheBloke
tags:
  - generated_from_trainer
TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Zephyr 7B Beta - AWQ

Description

This repo contains AWQ model files for Hugging Face H4's Zephyr 7B Beta.

These files were quantised using hardware kindly provided by Massed Compute.

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.

It is supported by:

Repositories available

Prompt template: Zephyr 

<|system|>
</s>
<|user|>
{prompt}</s>
<|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.

Branch Bits GS AWQ Dataset Seq Len Size
main 4 128 wikitext 4096 4.15 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.

  1. Click the Model tab.
  2. Under Download custom model or LoRA, enter TheBloke/zephyr-7B-beta-AWQ.
  3. Click Download.
  4. The model will start downloading. Once it's finished it will say "Done".
  5. In the top left, click the refresh icon next to Model.
  6. In the Model dropdown, choose the model you just downloaded: zephyr-7B-beta-AWQ
  7. Select Loader: AutoAWQ.
  8. Click Load, and the model will load and is now ready for use.
  9. 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.
  10. 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 awq parameter.

For example:

python3 python -m vllm.entrypoints.api_server --model TheBloke/zephyr-7B-beta-AWQ --quantization awq
  • 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'''<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>
'''

prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/zephyr-7B-beta-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/zephyr-7B-beta-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'''<|system|>
</s>
<|user|>
{prompt}</s>
<|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)

Inference from Python code using AutoAWQ

Install the AutoAWQ package

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 .

AutoAWQ example code 

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_name_or_path = "TheBloke/zephyr-7B-beta-AWQ"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
                                          trust_remote_code=False, safetensors=True)

prompt = "Tell me about AI"
prompt_template=f'''<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>
'''

print("*** Running model.generate:")

token_input = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    token_input,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    max_new_tokens=512
)

# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("LLM output: ", text_output)

"""
# 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:

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

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.

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: Hugging Face H4's Zephyr 7B Beta

Zephyr Logo

Model Card for Zephyr 7B β

Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes. You can find more details in the technical report.

Model description

  • Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
  • Language(s) (NLP): Primarily English
  • License: MIT
  • Finetuned from model: mistralai/Mistral-7B-v0.1

Model Sources

Performance

At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks:

Model Size Alignment MT-Bench (score) AlpacaEval (win rate %)
StableLM-Tuned-α 7B dSFT 2.75 -
MPT-Chat 7B dSFT 5.42 -
Xwin-LMv0.1 7B dPPO 6.19 87.83
Mistral-Instructv0.1 7B - 6.84 -
Zephyr-7b-α 7B dDPO 6.88 -
Zephyr-7b-β 🪁 7B dDPO 7.34 90.60
Falcon-Instruct 40B dSFT 5.17 45.71
Guanaco 65B SFT 6.41 71.80
Llama2-Chat 70B RLHF 6.86 92.66
Vicuna v1.3 33B dSFT 7.12 88.99
WizardLM v1.0 70B dSFT 7.71 -
Xwin-LM v0.1 70B dPPO - 95.57
GPT-3.5-turbo - RLHF 7.94 89.37
Claude 2 - RLHF 8.06 91.36
GPT-4 - RLHF 8.99 95.28

In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:

image/png

However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.

Intended uses & limitations

The model was initially fine-tuned on a filtered and preprocessed of the UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with 🤗 TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities.

You can find the datasets used for training Zephyr-7B-β here

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!

Bias, Risks, and Limitations

Zephyr-7B-β has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (mistralai/Mistral-7B-v0.1), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.

Training and evaluation data

During DPO training, this model achieves the following results on the evaluation set:

  • Loss: 0.7496
  • Rewards/chosen: -4.5221
  • Rewards/rejected: -8.3184
  • Rewards/accuracies: 0.7812
  • Rewards/margins: 3.7963
  • Logps/rejected: -340.1541
  • Logps/chosen: -299.4561
  • Logits/rejected: -2.3081
  • Logits/chosen: -2.3531

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • train_batch_size: 2
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 16
  • total_train_batch_size: 32
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3.0

Training results

The table below shows the full set of DPO training metrics:

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6284 0.05 100 0.6098 0.0425 -0.1872 0.7344 0.2297 -258.8416 -253.8099 -2.7976 -2.8234
0.4908 0.1 200 0.5426 -0.0279 -0.6842 0.75 0.6563 -263.8124 -254.5145 -2.7719 -2.7960
0.5264 0.15 300 0.5324 0.0414 -0.9793 0.7656 1.0207 -266.7627 -253.8209 -2.7892 -2.8122
0.5536 0.21 400 0.4957 -0.0185 -1.5276 0.7969 1.5091 -272.2460 -254.4203 -2.8542 -2.8764
0.5362 0.26 500 0.5031 -0.2630 -1.5917 0.7812 1.3287 -272.8869 -256.8653 -2.8702 -2.8958
0.5966 0.31 600 0.5963 -0.2993 -1.6491 0.7812 1.3499 -273.4614 -257.2279 -2.8778 -2.8986
0.5014 0.36 700 0.5382 -0.2859 -1.4750 0.75 1.1891 -271.7204 -257.0942 -2.7659 -2.7869
0.5334 0.41 800 0.5677 -0.4289 -1.8968 0.7969 1.4679 -275.9378 -258.5242 -2.7053 -2.7265
0.5251 0.46 900 0.5772 -0.2116 -1.3107 0.7344 1.0991 -270.0768 -256.3507 -2.8463 -2.8662
0.5205 0.52 1000 0.5262 -0.3792 -1.8585 0.7188 1.4793 -275.5552 -258.0276 -2.7893 -2.7979
0.5094 0.57 1100 0.5433 -0.6279 -1.9368 0.7969 1.3089 -276.3377 -260.5136 -2.7453 -2.7536
0.5837 0.62 1200 0.5349 -0.3780 -1.9584 0.7656 1.5804 -276.5542 -258.0154 -2.7643 -2.7756
0.5214 0.67 1300 0.5732 -1.0055 -2.2306 0.7656 1.2251 -279.2761 -264.2903 -2.6986 -2.7113
0.6914 0.72 1400 0.5137 -0.6912 -2.1775 0.7969 1.4863 -278.7448 -261.1467 -2.7166 -2.7275
0.4655 0.77 1500 0.5090 -0.7987 -2.2930 0.7031 1.4943 -279.8999 -262.2220 -2.6651 -2.6838
0.5731 0.83 1600 0.5312 -0.8253 -2.3520 0.7812 1.5268 -280.4902 -262.4876 -2.6543 -2.6728
0.5233 0.88 1700 0.5206 -0.4573 -2.0951 0.7812 1.6377 -277.9205 -258.8084 -2.6870 -2.7097
0.5593 0.93 1800 0.5231 -0.5508 -2.2000 0.7969 1.6492 -278.9703 -259.7433 -2.6221 -2.6519
0.4967 0.98 1900 0.5290 -0.5340 -1.9570 0.8281 1.4230 -276.5395 -259.5749 -2.6564 -2.6878
0.0921 1.03 2000 0.5368 -1.1376 -3.1615 0.7812 2.0239 -288.5854 -265.6111 -2.6040 -2.6345
0.0733 1.08 2100 0.5453 -1.1045 -3.4451 0.7656 2.3406 -291.4208 -265.2799 -2.6289 -2.6595
0.0972 1.14 2200 0.5571 -1.6915 -3.9823 0.8125 2.2908 -296.7934 -271.1505 -2.6471 -2.6709
0.1058 1.19 2300 0.5789 -1.0621 -3.8941 0.7969 2.8319 -295.9106 -264.8563 -2.5527 -2.5798
0.2423 1.24 2400 0.5455 -1.1963 -3.5590 0.7812 2.3627 -292.5599 -266.1981 -2.5414 -2.5784
0.1177 1.29 2500 0.5889 -1.8141 -4.3942 0.7969 2.5801 -300.9120 -272.3761 -2.4802 -2.5189
0.1213 1.34 2600 0.5683 -1.4608 -3.8420 0.8125 2.3812 -295.3901 -268.8436 -2.4774 -2.5207
0.0889 1.39 2700 0.5890 -1.6007 -3.7337 0.7812 2.1330 -294.3068 -270.2423 -2.4123 -2.4522
0.0995 1.45 2800 0.6073 -1.5519 -3.8362 0.8281 2.2843 -295.3315 -269.7538 -2.4685 -2.5050
0.1145 1.5 2900 0.5790 -1.7939 -4.2876 0.8438 2.4937 -299.8461 -272.1744 -2.4272 -2.4674
0.0644 1.55 3000 0.5735 -1.7285 -4.2051 0.8125 2.4766 -299.0209 -271.5201 -2.4193 -2.4574
0.0798 1.6 3100 0.5537 -1.7226 -4.2850 0.8438 2.5624 -299.8200 -271.4610 -2.5367 -2.5696
0.1013 1.65 3200 0.5575 -1.5715 -3.9813 0.875 2.4098 -296.7825 -269.9498 -2.4926 -2.5267
0.1254 1.7 3300 0.5905 -1.6412 -4.4703 0.8594 2.8291 -301.6730 -270.6473 -2.5017 -2.5340
0.085 1.76 3400 0.6133 -1.9159 -4.6760 0.8438 2.7601 -303.7296 -273.3941 -2.4614 -2.4960
0.065 1.81 3500 0.6074 -1.8237 -4.3525 0.8594 2.5288 -300.4951 -272.4724 -2.4597 -2.5004
0.0755 1.86 3600 0.5836 -1.9252 -4.4005 0.8125 2.4753 -300.9748 -273.4872 -2.4327 -2.4716
0.0746 1.91 3700 0.5789 -1.9280 -4.4906 0.8125 2.5626 -301.8762 -273.5149 -2.4686 -2.5115
0.1348 1.96 3800 0.6015 -1.8658 -4.2428 0.8281 2.3769 -299.3976 -272.8936 -2.4943 -2.5393
0.0217 2.01 3900 0.6122 -2.3335 -4.9229 0.8281 2.5894 -306.1988 -277.5699 -2.4841 -2.5272
0.0219 2.07 4000 0.6522 -2.9890 -6.0164 0.8281 3.0274 -317.1334 -284.1248 -2.4105 -2.4545
0.0119 2.12 4100 0.6922 -3.4777 -6.6749 0.7969 3.1972 -323.7187 -289.0121 -2.4272 -2.4699
0.0153 2.17 4200 0.6993 -3.2406 -6.6775 0.7969 3.4369 -323.7453 -286.6413 -2.4047 -2.4465
0.011 2.22 4300 0.7178 -3.7991 -7.4397 0.7656 3.6406 -331.3667 -292.2260 -2.3843 -2.4290
0.0072 2.27 4400 0.6840 -3.3269 -6.8021 0.8125 3.4752 -324.9908 -287.5042 -2.4095 -2.4536
0.0197 2.32 4500 0.7013 -3.6890 -7.3014 0.8125 3.6124 -329.9841 -291.1250 -2.4118 -2.4543
0.0182 2.37 4600 0.7476 -3.8994 -7.5366 0.8281 3.6372 -332.3356 -293.2291 -2.4163 -2.4565
0.0125 2.43 4700 0.7199 -4.0560 -7.5765 0.8438 3.5204 -332.7345 -294.7952 -2.3699 -2.4100
0.0082 2.48 4800 0.7048 -3.6613 -7.1356 0.875 3.4743 -328.3255 -290.8477 -2.3925 -2.4303
0.0118 2.53 4900 0.6976 -3.7908 -7.3152 0.8125 3.5244 -330.1224 -292.1431 -2.3633 -2.4047
0.0118 2.58 5000 0.7198 -3.9049 -7.5557 0.8281 3.6508 -332.5271 -293.2844 -2.3764 -2.4194
0.006 2.63 5100 0.7506 -4.2118 -7.9149 0.8125 3.7032 -336.1194 -296.3530 -2.3407 -2.3860
0.0143 2.68 5200 0.7408 -4.2433 -7.9802 0.8125 3.7369 -336.7721 -296.6682 -2.3509 -2.3946
0.0057 2.74 5300 0.7552 -4.3392 -8.0831 0.7969 3.7439 -337.8013 -297.6275 -2.3388 -2.3842
0.0138 2.79 5400 0.7404 -4.2395 -7.9762 0.8125 3.7367 -336.7322 -296.6304 -2.3286 -2.3737
0.0079 2.84 5500 0.7525 -4.4466 -8.2196 0.7812 3.7731 -339.1662 -298.7007 -2.3200 -2.3641
0.0077 2.89 5600 0.7520 -4.5586 -8.3485 0.7969 3.7899 -340.4545 -299.8206 -2.3078 -2.3517
0.0094 2.94 5700 0.7527 -4.5542 -8.3509 0.7812 3.7967 -340.4790 -299.7773 -2.3062 -2.3510
0.0054 2.99 5800 0.7520 -4.5169 -8.3079 0.7812 3.7911 -340.0493 -299.4038 -2.3081 -2.3530

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.14.0

Citation

If you find Zephyr-7B-β is useful in your work, please cite it with:

@misc{tunstall2023zephyr,
      title={Zephyr: Direct Distillation of LM Alignment}, 
      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
      year={2023},
      eprint={2310.16944},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}