--- base_model: fblgit/juanako-7b-v1 datasets: - HuggingFaceH4/ultrafeedback_binarized inference: false license: artistic-2.0 model-index: - name: juanako-7b-v1 results: [] model_creator: FBL model_name: Juanako 7B V1 model_type: mistral prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke tags: - alignment-handbook - generated_from_trainer ---
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# Juanako 7B V1 - GPTQ - Model creator: [FBL](https://huggingface.co/fblgit) - Original model: [Juanako 7B V1](https://huggingface.co/fblgit/juanako-7b-v1) # Description This repo contains GPTQ model files for [FBL's Juanako 7B V1](https://huggingface.co/fblgit/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](https://massedcompute.com/). ## 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) ## 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](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) 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](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 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](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 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](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 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](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/juanako-7B-v1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 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: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `juanako-7B-v1-GPTQ`: ```shell 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: ```shell 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](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell 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: ```shell 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](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-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: ```shell --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): ```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}") ``` ## 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. ```shell 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: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python 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](https://github.com/turboderp/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](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. # 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|