TheBloke/juanako-7B-v1-GPTQ

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Want to contribute? TheBloke's Patreon page

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

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Juanako 7B V1 - GPTQ

• Model creator: FBL
• Original model: Juanako 7B V1

Description

This repo contains GPTQ model files for FBL's Juanako 7B V1.

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

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

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: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Known compatible clients / servers

These GPTQ models are known to work in the following inference servers/webuis.

• text-generation-webui
• KoboldAI United
• LoLLMS Web UI
• Hugging Face Text Generation Inference (TGI)

This may not be a complete list; if you know of others, please let me know!

Provided files, and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch. See below for instructions on fetching from different branches.

Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.

Explanation of GPTQ parameters

• Bits: The bit size of the quantised model.
• GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
• Act Order: True or False. Also known as desc_act. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
• Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
• GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
• Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
• ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.

Branch	Bits	GS	Act Order	Damp %	GPTQ Dataset	Seq Len	Size	ExLlama	Desc
main	4	128	Yes	0.1	VMware Open Instruct	4096	4.16 GB	Yes	4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy.
gptq-4bit-32g-actorder_True	4	32	Yes	0.1	VMware Open Instruct	4096	4.57 GB	Yes	4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage.
gptq-8bit--1g-actorder_True	8	None	Yes	0.1	VMware Open Instruct	4096	7.52 GB	No	8-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-8bit-128g-actorder_True	8	128	Yes	0.1	VMware Open Instruct	4096	7.68 GB	No	8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy.
gptq-8bit-32g-actorder_True	8	32	Yes	0.1	VMware Open Instruct	4096	8.17 GB	No	8-bit, with group size 32g and Act Order for maximum inference quality.
gptq-4bit-64g-actorder_True	4	64	Yes	0.1	VMware Open Instruct	4096	4.29 GB	Yes	4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy.

How to download, including from branches

In text-generation-webui

To download from the main branch, enter TheBloke/juanako-7B-v1-GPTQ in the "Download model" box.

To download from another branch, add :branchname to the end of the download name, eg TheBloke/juanako-7B-v1-GPTQ:gptq-4bit-32g-actorder_True

From the command line

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called juanako-7B-v1-GPTQ:

mkdir juanako-7B-v1-GPTQ
huggingface-cli download TheBloke/juanako-7B-v1-GPTQ --local-dir juanako-7B-v1-GPTQ --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir juanako-7B-v1-GPTQ
huggingface-cli download TheBloke/juanako-7B-v1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir juanako-7B-v1-GPTQ --local-dir-use-symlinks False

More advanced huggingface-cli download usage

If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.

The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

mkdir juanako-7B-v1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/juanako-7B-v1-GPTQ --local-dir juanako-7B-v1-GPTQ --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

With git (not recommended)

To clone a specific branch with git, use a command like this:

git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ

Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface-hub, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git folder as a blob.)

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/juanako-7B-v1-GPTQ.

   • To download from a specific branch, enter for example TheBloke/juanako-7B-v1-GPTQ:gptq-4bit-32g-actorder_True
   • see Provided Files above for the list of branches for each option.

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: juanako-7B-v1-GPTQ

7. The model will automatically load, and is now ready for use!

8. 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.

   • Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file quantize_config.json.

9. Once you're ready, click the Text Generation tab and enter a prompt to get started!

Serving this model from Text Generation Inference (TGI)

It's recommended to 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/juanako-7B-v1-GPTQ --port 3000 --quantize gptq --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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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}")

Python code example: inference from this GPTQ model

Install the necessary packages

Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/

If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:

pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .

Example Python code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/juanako-7B-v1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' 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 Transformers. For non-Mistral models, AutoGPTQ can also be used directly.

ExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.

For a list of clients/servers, please see "Known compatible clients / servers", above.

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.

• Patreon: https://patreon.com/TheBlokeAI
• Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: FBL's Juanako 7B V1

juanako-7b-v1

This model is a fine-tuned version of fblgit/zephyr-lora-dpo-b1 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

• Loss: 0.4594
• Rewards/chosen: -1.1095
• Rewards/rejected: -2.3132
• Rewards/accuracies: 0.7964
• Rewards/margins: 1.2037
• Logps/rejected: -220.0052
• Logps/chosen: -217.5506
• Logits/rejected: -2.5535
• Logits/chosen: -2.7973

** Please feel free to run more tests and commit the results. Also if you are interested to participate in UNA's paper research or GPU sponsorship **

Model description

It seems to outperforms the original Zephyr in most of the tasks.

I trained Juanako with the same datasets and trainer from alignment-handbook/zephyr-7b-sft-lora

• 1 epoch on DPO with transformers-UNA, the result is fblgit/zephyr-lora-dpo-b1 after merge using FastChat converter.
• finally 1 epoch on DPO with transformers-UNA to fblgit/zephyr-lora-dpo-b1.

Some other experiments were performed as well to test transformers-UNA capabilities on diverse scenarios and models.

This is a complete version of the model, the result of converting LoRa's

Intended uses & limitations

Research purposes.

Training and evaluation data

alignment-handbook DPO with UNA on top of the SFT lora.

Evaluation lm-evaluation-harness

GSM8K

hf (pretrained=/root/juanako-7b-v1-beta,load_in_4bit=False,dtype=float16), limit: None, num_fewshot: 3, batch_size: 4

Tasks	Version	Filter	Metric	Value		Stderr
gsm8k	Yaml	get-answer	exact_match	0.4556	±	0.0137

0-Shot

hf (pretrained=fblgit/juanako-7b-v1,load_in_4bit=False,dtype=float16), limit: None, num_fewshot: 0, batch_size: 8

Tasks	Version	Filter	Metric	Value		Stderr
arc_challenge	Yaml	none	acc	0.5691	±	0.0145
		none	acc_norm	0.6041	±	0.0143
arc_easy	Yaml	none	acc	0.8363	±	0.0076
		none	acc_norm	0.8161	±	0.0079
hellaswag	Yaml	none	acc	0.6554	±	0.0047
		none	acc_norm	0.8411	±	0.0036
boolq	Yaml	none	acc	0.8355	±	0.0065
lambada	N/A	none	perplexity	3.3607	±	0.1398
		none	acc	0.7309	±	0.0137
piqa	Yaml	none	acc	0.8194	±	0.0090
		none	acc_norm	0.8335	±	0.0087
sciq	Yaml	none	acc	0.9480	±	0.0070
		none	acc_norm	0.8960	±	0.0097
truthfulqa	N/A	none	bleu_max	26.0803	±	0.6528
- truthfulqa_mc1	Yaml	none	acc	0.4198	±	0.0173
- truthfulqa_mc2	Yaml	none	acc	0.5847	±	0.0153
winogrande	Yaml	none	acc	0.7609	±	0.0120

1-Shot

hf (pretrained=fblgit/juanako-7b-v1,load_in_4bit=False,dtype=float16), limit: None, num_fewshot: 1, batch_size: 8

Tasks	Version	Filter	Metric	Value		Stderr
arc_challenge	Yaml	none	acc	0.6084	±	0.0143
		none	acc_norm	0.6357	±	0.0141
arc_easy	Yaml	none	acc	0.8645	±	0.0070
		none	acc_norm	0.8645	±	0.0070
hellaswag	Yaml	none	acc	0.6475	±	0.0048
		none	acc_norm	0.8372	±	0.0037
boolq	Yaml	none	acc	0.8609	±	0.0061
lambada	N/A	none	perplexity	3.5484	±	0.1034
		none	acc	0.7207	±	0.0107
piqa	Yaml	none	acc	0.8259	±	0.0088
		none	acc_norm	0.8384	±	0.0086
sciq	Yaml	none	acc	0.9730	±	0.0051
		none	acc_norm	0.9740	±	0.0050
truthfulqa	N/A	none	bleu_max	18.9814	±	0.4805
		none	acc	0.4856	±	0.0521
- truthfulqa_mc1	Yaml	none	acc	0.4333	±	0.0173
- truthfulqa_mc2	Yaml	none	acc	0.5903	±	0.0153
winogrande	Yaml	none	acc	0.7609	±	0.0120

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 12
• gradient_accumulation_steps: 16
• total_train_batch_size: 192
• total_eval_batch_size: 12
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.4966	0.15	50	0.4893	-1.1759	-2.2914	0.7485	1.1155	-219.7872	-218.2148	-2.5450	-2.7884
0.4522	0.31	100	0.4808	-0.8099	-1.8893	0.7784	1.0794	-215.7659	-214.5544	-2.5644	-2.8095
0.5048	0.46	150	0.4706	-1.0526	-2.1412	0.7725	1.0887	-218.2852	-216.9814	-2.5638	-2.8089
0.4853	0.62	200	0.4640	-1.0787	-2.2821	0.7725	1.2034	-219.6941	-217.2426	-2.5460	-2.7891
0.4639	0.77	250	0.4636	-1.2348	-2.4583	0.8084	1.2235	-221.4559	-218.8034	-2.5533	-2.7970
0.4634	0.93	300	0.4601	-1.1370	-2.3243	0.7964	1.1873	-220.1163	-217.8257	-2.5540	-2.7977
-	1.00	300	0.4594	-1.1095	-2.3132	0.7964	1.2037	-220.0052	-217.5506	-2.5535	-2.7973

Framework versions

• Transformers 4.35.0-UNA
• Pytorch 2.1.0
• Datasets 2.14.6
• Tokenizers 0.14.1

MMLU Results

1-Shot

hf (pretrained=fblgit/juanako-7b-v1,load_in_4bit=False,dtype=float16), limit: None, num_fewshot: 1, batch_size: 1

Tasks	Version	Filter	Metric	Value		Stderr
mmlu	N/A	none	acc	0.6085	±	0.1321
- humanities	N/A	none	acc	0.5405	±	0.1478
- formal_logic	Yaml	none	acc	0.4206	±	0.0442
- high_school_european_history	Yaml	none	acc	0.7576	±	0.0335
- high_school_us_history	Yaml	none	acc	0.8186	±	0.0270
- high_school_world_history	Yaml	none	acc	0.7890	±	0.0266
- international_law	Yaml	none	acc	0.7438	±	0.0398
- jurisprudence	Yaml	none	acc	0.8056	±	0.0383
- logical_fallacies	Yaml	none	acc	0.7791	±	0.0326
- moral_disputes	Yaml	none	acc	0.7023	±	0.0246
- moral_scenarios	Yaml	none	acc	0.2145	±	0.0137
- philosophy	Yaml	none	acc	0.7074	±	0.0258
- prehistory	Yaml	none	acc	0.7377	±	0.0245
- professional_law	Yaml	none	acc	0.4361	±	0.0127
- world_religions	Yaml	none	acc	0.8421	±	0.0280
- other	N/A	none	acc	0.6894	±	0.1091
- business_ethics	Yaml	none	acc	0.5600	±	0.0499
- clinical_knowledge	Yaml	none	acc	0.6981	±	0.0283
- college_medicine	Yaml	none	acc	0.6185	±	0.0370
- global_facts	Yaml	none	acc	0.3300	±	0.0473
- human_aging	Yaml	none	acc	0.6726	±	0.0315
- management	Yaml	none	acc	0.8058	±	0.0392
- marketing	Yaml	none	acc	0.8419	±	0.0239
- medical_genetics	Yaml	none	acc	0.7200	±	0.0451
- miscellaneous	Yaml	none	acc	0.8033	±	0.0142
- nutrition	Yaml	none	acc	0.7288	±	0.0255
- professional_accounting	Yaml	none	acc	0.4929	±	0.0298
- professional_medicine	Yaml	none	acc	0.6801	±	0.0283
- virology	Yaml	none	acc	0.5000	±	0.0389
- social_sciences	N/A	none	acc	0.7195	±	0.0676
- econometrics	Yaml	none	acc	0.5000	±	0.0470
- high_school_geography	Yaml	none	acc	0.7879	±	0.0291
- high_school_government_and_politics	Yaml	none	acc	0.8601	±	0.0250
- high_school_macroeconomics	Yaml	none	acc	0.6231	±	0.0246
- high_school_microeconomics	Yaml	none	acc	0.6471	±	0.0310
- high_school_psychology	Yaml	none	acc	0.8000	±	0.0171
- human_sexuality	Yaml	none	acc	0.7557	±	0.0377
- professional_psychology	Yaml	none	acc	0.6552	±	0.0192
- public_relations	Yaml	none	acc	0.6636	±	0.0453
- security_studies	Yaml	none	acc	0.7184	±	0.0288
- sociology	Yaml	none	acc	0.8358	±	0.0262
- us_foreign_policy	Yaml	none	acc	0.8500	±	0.0359
- stem	N/A	none	acc	0.5217	±	0.1149
- abstract_algebra	Yaml	none	acc	0.3000	±	0.0461
- anatomy	Yaml	none	acc	0.6222	±	0.0419
- astronomy	Yaml	none	acc	0.6711	±	0.0382
- college_biology	Yaml	none	acc	0.7361	±	0.0369
- college_chemistry	Yaml	none	acc	0.4400	±	0.0499
- college_computer_science	Yaml	none	acc	0.5000	±	0.0503
- college_mathematics	Yaml	none	acc	0.3100	±	0.0465
- college_physics	Yaml	none	acc	0.4902	±	0.0497
- computer_security	Yaml	none	acc	0.7100	±	0.0456
- conceptual_physics	Yaml	none	acc	0.5362	±	0.0326
- electrical_engineering	Yaml	none	acc	0.5862	±	0.0410
- elementary_mathematics	Yaml	none	acc	0.4365	±	0.0255
- high_school_biology	Yaml	none	acc	0.7129	±	0.0257
- high_school_chemistry	Yaml	none	acc	0.5074	±	0.0352
- high_school_computer_science	Yaml	none	acc	0.6500	±	0.0479
- high_school_mathematics	Yaml	none	acc	0.3259	±	0.0286
- high_school_physics	Yaml	none	acc	0.3709	±	0.0394
- high_school_statistics	Yaml	none	acc	0.5139	±	0.0341
- machine_learning	Yaml	none	acc	0.5089	±	0.0475

Groups	Version	Filter	Metric	Value		Stderr
mmlu	N/A	none	acc	0.6085	±	0.1321
- humanities	N/A	none	acc	0.5405	±	0.1478
- other	N/A	none	acc	0.6894	±	0.1091
- social_sciences	N/A	none	acc	0.7195	±	0.0676
- stem	N/A	none	acc	0.5217	±	0.1149