transformers safetensors mistral text-generation alignment-handbook generated_from_trainer dataset:HuggingFaceH4/ultrafeedback_binarized base_model:fblgit/juanako-7b-v1 base_model:quantized:fblgit/juanako-7b-v1 license:artistic-2.0 autotrain_compatible text-generation-inference 4-bit awq region:us
---
base_model: fblgit/juanako-7b-v1
datasets:
- HuggingFaceH4/ultrafeedback_binarized
license: artistic-2.0
tags:
- alignment-handbook
- generated_from_trainer
inference: false
model_creator: FBL
model_type: mistral
prompt_template: '<|im_start|>system

  {system_message}<|im_end|>

  <|im_start|>user

  {prompt}<|im_end|>

  <|im_start|>assistant

  '
quantized_by: TheBloke
model-index:
- name: juanako-7b-v1
  results: []
---
<!-- markdownlint-disable MD041 -->

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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->

# Juanako 7B V1 - AWQ
- Model creator: [FBL](https://huggingface.co/fblgit)
- Original model: [Juanako 7B V1](https://huggingface.co/fblgit/juanako-7b-v1)

<!-- description start -->
## Description

This repo contains AWQ model files for [FBL's Juanako 7B V1](https://huggingface.co/fblgit/juanako-7b-v1).

These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).


### 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:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

<!-- description end -->
<!-- repositories-available start -->
## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/juanako-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/juanako-7B-v1-GGUF)
* [FBL's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/fblgit/juanako-7b-v1)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: ChatML

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

```

<!-- prompt-template end -->


<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters

I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.

Models are released as sharded safetensors files.

| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/juanako-7B-v1-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB

<!-- README_AWQ.md-provided-files end -->

<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)

Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/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-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: `juanako-7B-v1-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!
<!-- README_AWQ.md-text-generation-webui end -->

<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM

Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).

- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.

For example:

```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/juanako-7B-v1-AWQ --quantization awq --dtype auto
```

- When using vLLM from Python code, again set `quantization=awq`.

For example:

```python
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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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/juanako-7B-v1-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}")
```
<!-- README_AWQ.md-use-from-vllm start -->

<!-- README_AWQ.md-use-from-tgi start -->
## 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:

```shell
--model-id TheBloke/juanako-7B-v1-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](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):

```shell
pip3 install huggingface-hub
```

```python
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)
```
<!-- README_AWQ.md-use-from-tgi end -->

<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers

### Install the necessary packages

- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.

```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```

Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.

If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:

```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```

If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:

```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```

### Transformers example code (requires Transformers 4.35.0 and later)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_name_or_path = "TheBloke/juanako-7B-v1-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)

# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=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
'''

# Convert prompt to tokens
tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

generation_params = {
    "do_sample": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "max_new_tokens": 512,
    "repetition_penalty": 1.1
}

# Generate streamed output, visible one token at a time
generation_output = model.generate(
    tokens,
    streamer=streamer,
    **generation_params
)

# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
    tokens,
    **generation_params
)

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

# Inference is also possible via Transformers' pipeline
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    **generation_params
)

pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)

```
<!-- README_AWQ.md-use-from-python end -->

<!-- README_AWQ.md-compatibility start -->
## Compatibility

The files provided are tested to work with:

- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.

<!-- README_AWQ.md-compatibility end -->

<!-- footer start -->
<!-- 200823 -->
## Discord

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

[TheBloke AI's Discord server](https://discord.gg/theblokeai)

## Thanks, and how to contribute

Thanks to the [chirper.ai](https://chirper.ai) team!

Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

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.

<!-- footer end -->

# Original model card: FBL's Juanako 7B V1


# juanako-7b-v1

This model is a fine-tuned version of [fblgit/zephyr-lora-dpo-b1](https://huggingface.co/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](mailto:info@fblnet.net) **

## 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](https://huggingface.co/alignment-handbook/zephyr-7b-sft-lora) 
* 1 epoch on DPO with transformers-UNA, the result is [fblgit/zephyr-lora-dpo-b1](https://huggingface.co/fblgit/zephyr-lora-dpo-b1) after merge using FastChat converter.
* finally 1 epoch on DPO with transformers-UNA to [fblgit/zephyr-lora-dpo-b1](https://huggingface.co/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|
