--- base_model: fblgit/LUNA-SOLARkrautLM-Instruct datasets: - argilla/distilabel-math-preference-dpo inference: false language: - en - de library_name: transformers license: cc-by-nc-4.0 model_creator: FBL model_name: Luna SOLARkrautLM Instruct model_type: solar pipeline_tag: text-generation prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke tags: - finetune - dpo - Instruct - augmentation - german ---
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# Luna SOLARkrautLM Instruct - AWQ - Model creator: [FBL](https://huggingface.co/fblgit) - Original model: [Luna SOLARkrautLM Instruct](https://huggingface.co/fblgit/LUNA-SOLARkrautLM-Instruct) ## Description This repo contains AWQ model files for [FBL's Luna SOLARkrautLM Instruct](https://huggingface.co/fblgit/LUNA-SOLARkrautLM-Instruct). 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. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [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 ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LUNA-SOLARkrautLM-Instruct-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LUNA-SOLARkrautLM-Instruct-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LUNA-SOLARkrautLM-Instruct-GGUF) * [FBL's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/fblgit/LUNA-SOLARkrautLM-Instruct) ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## 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/LUNA-SOLARkrautLM-Instruct-AWQ/tree/main) | 4 | 128 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 2048 | 5.96 GB ## 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/LUNA-SOLARkrautLM-Instruct-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: `LUNA-SOLARkrautLM-Instruct-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](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/LUNA-SOLARkrautLM-Instruct-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/LUNA-SOLARkrautLM-Instruct-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: ```shell --model-id TheBloke/LUNA-SOLARkrautLM-Instruct-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) ``` ## 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/LUNA-SOLARkrautLM-Instruct-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) ``` ## 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. ## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. # Original model card: FBL's Luna SOLARkrautLM Instruct ![Juanako.AI & SauerkrautLM Productions](https://vago-solutions.de/wp-content/uploads/2023/12/sauerkrautlm-solar.png "LUNA-SOLARkrautLM-Instruct") ## VAGO solutions LUNA-SOLARkrautLM-Instruct Introducing **LUNA-SOLARkrautLM-Instruct** – a UNA-Sauerkraut version of the powerful [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) ! Aligned with **DPO** and tamed with **UNA**. # Table of Contents 1. [Overview of all LUNA-SOLARkrautLM-Instruct models](#all-sauerkrautlm-solar-instruct-models) 2. [Model Details](#model-details) - [Prompt template](#prompt-template) - [Training Dataset](#training-dataset) - [Data Contamination Test](#data-contamination-test-results) 3. [Evaluation](#evaluation) 5. [Disclaimer](#disclaimer) 6. [Contact](#contact) 7. [Collaborations](#collaborations) 8. [Acknowledgement](#acknowledgement) ## Model Details **LUNA-SOLARkrautLM-Instruct** - **Model Type:** LUNA-SOLARkrautLM-Instruct is a UNA Model based on [fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0) and the powerful set of [SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct/) - **Language(s):** English, German - **License:** cc-by-nc-4.0 - **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de) [Juanako.AI - UNA](mailto:info@juanako.ai) ### Training Dataset: LUNA-SOLARkrautLM-Instruct was trained with mix of German data augmentation and translated data. Aligned through **DPO** with our **new German SauerkrautLM-DPO dataset** based on parts of the SFT SauerkrautLM dataset as chosen answers and [Sauerkraut-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) as rejected answers. Added with additional **translated Parts of the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)** (Our dataset do not contain any TruthfulQA prompts - check Data Contamination Test Results) and **[argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).** We found, that only a simple translation of training data can lead to unnatural German phrasings. Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data. We improved the German language skills on this model. Nevertheless, certain formulations may occur that are not entirely correct. ### Data Contamination Test Results Some models on the HuggingFace leaderboard had problems with wrong data getting mixed in. We checked our SauerkrautLM-DPO dataset with a special test [1] on this model as target model and upstage/SOLAR-10.7B-Instruct-v1.0 as reference model. The HuggingFace team used the same methods [2, 3]. Our results, with `result < 0.1, %:` being well below 0.9, indicate that our dataset is free from contamination. *The data contamination test results of HellaSwag and Winograde will be added once [1] supports them.* | Dataset | ARC | MMLU | TruthfulQA | GSM8K | |------------------------------|-------|-------|-------|-------| | **SauerkrautLM-DPO**| result < 0.1, %: 0.0 |result < 0.1, %: 0.09 | result < 0.1, %: 0.13 | result < 0.1, %: 0.16 | [1] https://github.com/swj0419/detect-pretrain-code-contamination [2] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06 [3] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230 ### Prompt Template: ``` <|im_start|>system Du bist LUNA-SOLARkrautLM, ein großes Sprachmodell, das höflich und kompetent antwortet.<|im_end|> <|im_start|>user Wie geht es dir?<|im_end|> <|im_start|>assistant ``` ``` ### User: Hello, how are you? ### Assistant: Hi there! I am an AI language model, so I don't have personal feelings or emotions in the traditional sense. However, I can assure you that my systems and processes are functioning well at this moment, allowing me to provide helpful responses for your queries. How may I assist you today? ``` ## Evaluation ``` hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto |Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |-----|-------|----------|-----:|-----------|-----:|---|-----:| |gsm8k|Yaml |get-answer| 5|exact_match|0.6467|± |0.0132| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 0, batch_size: auto (64) | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |--------------|-------|------|-----:|------|-----:|---|-----:| |truthfulqa_mc2|Yaml |none | 0|acc |0.7368|± |0.0149| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 25, batch_size: auto (32) | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |-------------|-------|------|-----:|--------|----:|---|-----:| |arc_challenge|Yaml |none | 25|acc |0.692|± |0.0135| | | |none | 25|acc_norm|0.715|± |0.0132| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 0, batch_size: auto (64) | Tasks |Version|Filter|n-shot|Metric| Value | |Stderr| |-----------|-------|------|-----:|------|------:|---|-----:| |paws_de |Yaml |none | 0|acc | 0.3965|± |0.0109| |wmt16-en-de|Yaml |none | 0|bleu | 3.5784|± |0.1325| | | |none | 0|ter |64.5707|± |0.4514| | | |none | 0|chrf |45.7068|± |0.3861| |xnli_de |Yaml |none | 0|acc | 0.4129|± |0.0099| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 10, batch_size: auto (32) | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |---------|-------|------|-----:|--------|-----:|---|-----:| |hellaswag|Yaml |none | 10|acc |0.7131|± |0.0045| | | |none | 10|acc_norm|0.8815|± |0.0032| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto (64) | Tasks |Version|Filter|n-shot|Metric| Value | |Stderr| |-----------|-------|------|-----:|------|------:|---|-----:| |wmt16-de-en|Yaml |none | 5|bleu |14.9310|± |0.8014| | | |none | 5|ter |46.3206|± |0.4087| | | |none | 5|chrf |60.8637|± |0.4436| |wmt16-en-de|Yaml |none | 5|bleu | 6.2016|± |0.2918| | | |none | 5|ter |63.9997|± |0.4591| | | |none | 5|chrf |51.1399|± |0.3978| |xnli_de |Yaml |none | 5|acc | 0.4703|± |0.0100| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct,dtype=float16), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto (16) | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |---------------------------------------|-------|------|-----:|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.6461|± |0.1215| | - humanities |N/A |none | 5|acc |0.5960|± |0.1200| | - formal_logic |Yaml |none | 5|acc |0.4683|± |0.0446| | - high_school_european_history |Yaml |none | 5|acc |0.8121|± |0.0305| | - high_school_us_history |Yaml |none | 5|acc |0.8480|± |0.0252| | - high_school_world_history |Yaml |none | 5|acc |0.8312|± |0.0244| | - international_law |Yaml |none | 5|acc |0.7851|± |0.0375| | - jurisprudence |Yaml |none | 5|acc |0.7685|± |0.0408| | - logical_fallacies |Yaml |none | 5|acc |0.7423|± |0.0344| | - moral_disputes |Yaml |none | 5|acc |0.7283|± |0.0239| | - moral_scenarios |Yaml |none | 5|acc |0.3899|± |0.0163| | - philosophy |Yaml |none | 5|acc |0.7074|± |0.0258| | - prehistory |Yaml |none | 5|acc |0.7716|± |0.0234| | - professional_law |Yaml |none | 5|acc |0.4824|± |0.0128| | - world_religions |Yaml |none | 5|acc |0.7661|± |0.0325| | - other |N/A |none | 5|acc |0.7097|± |0.0900| | - business_ethics |Yaml |none | 5|acc |0.7700|± |0.0423| | - clinical_knowledge |Yaml |none | 5|acc |0.6792|± |0.0287| | - college_medicine |Yaml |none | 5|acc |0.6647|± |0.0360| | - global_facts |Yaml |none | 5|acc |0.3600|± |0.0482| | - human_aging |Yaml |none | 5|acc |0.6861|± |0.0311| | - management |Yaml |none | 5|acc |0.8350|± |0.0368| | - marketing |Yaml |none | 5|acc |0.8504|± |0.0234| | - medical_genetics |Yaml |none | 5|acc |0.6700|± |0.0473| | - miscellaneous |Yaml |none | 5|acc |0.7893|± |0.0146| | - nutrition |Yaml |none | 5|acc |0.7549|± |0.0246| | - professional_accounting |Yaml |none | 5|acc |0.5213|± |0.0298| | - professional_medicine |Yaml |none | 5|acc |0.7353|± |0.0268| | - virology |Yaml |none | 5|acc |0.5783|± |0.0384| | - social_sciences |N/A |none | 5|acc |0.7501|± |0.0684| | - econometrics |Yaml |none | 5|acc |0.5175|± |0.0470| | - high_school_geography |Yaml |none | 5|acc |0.8485|± |0.0255| | - high_school_government_and_politics|Yaml |none | 5|acc |0.8912|± |0.0225| | - high_school_macroeconomics |Yaml |none | 5|acc |0.6615|± |0.0240| | - high_school_microeconomics |Yaml |none | 5|acc |0.7311|± |0.0288| | - high_school_psychology |Yaml |none | 5|acc |0.8385|± |0.0158| | - human_sexuality |Yaml |none | 5|acc |0.7023|± |0.0401| | - professional_psychology |Yaml |none | 5|acc |0.6683|± |0.0190| | - public_relations |Yaml |none | 5|acc |0.6909|± |0.0443| | - security_studies |Yaml |none | 5|acc |0.7633|± |0.0272| | - sociology |Yaml |none | 5|acc |0.8358|± |0.0262| | - us_foreign_policy |Yaml |none | 5|acc |0.8800|± |0.0327| | - stem |N/A |none | 5|acc |0.5569|± |0.1360| | - abstract_algebra |Yaml |none | 5|acc |0.3800|± |0.0488| | - anatomy |Yaml |none | 5|acc |0.6148|± |0.0420| | - astronomy |Yaml |none | 5|acc |0.7237|± |0.0364| | - college_biology |Yaml |none | 5|acc |0.7708|± |0.0351| | - college_chemistry |Yaml |none | 5|acc |0.4600|± |0.0501| | - college_computer_science |Yaml |none | 5|acc |0.5400|± |0.0501| | - college_mathematics |Yaml |none | 5|acc |0.2700|± |0.0446| | - college_physics |Yaml |none | 5|acc |0.3333|± |0.0469| | - computer_security |Yaml |none | 5|acc |0.7300|± |0.0446| | - conceptual_physics |Yaml |none | 5|acc |0.6213|± |0.0317| | - electrical_engineering |Yaml |none | 5|acc |0.6276|± |0.0403| | - elementary_mathematics |Yaml |none | 5|acc |0.4788|± |0.0257| | - high_school_biology |Yaml |none | 5|acc |0.8065|± |0.0225| | - high_school_chemistry |Yaml |none | 5|acc |0.5123|± |0.0352| | - high_school_computer_science |Yaml |none | 5|acc |0.7000|± |0.0461| | - high_school_mathematics |Yaml |none | 5|acc |0.3889|± |0.0297| | - high_school_physics |Yaml |none | 5|acc |0.3576|± |0.0391| | - high_school_statistics |Yaml |none | 5|acc |0.5926|± |0.0335| | - machine_learning |Yaml |none | 5|acc |0.4554|± |0.0473| | Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |------------------|-------|------|-----:|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.6461|± |0.1215| | - humanities |N/A |none | 5|acc |0.5960|± |0.1200| | - other |N/A |none | 5|acc |0.7097|± |0.0900| | - social_sciences|N/A |none | 5|acc |0.7501|± |0.0684| | - stem |N/A |none | 5|acc |0.5569|± |0.1360| ``` ### MT-Bench ``` ########## Average ########## score model gpt-4 8.990625 gpt-3.5-turbo 7.943750 claude-instant-v1 7.905660 claude-v1 7.900000 UNA-SOLAR-10.7B-Instruct-v1.0 7.521875 LUNA-SOLARkrautLM-Instruct 7.462500 vicuna-33b-v1.3 7.121875 wizardlm-30b 7.009375 Llama-2-70b-chat 6.856250 Llama-2-13b-chat 6.650000 guanaco-33b 6.528125 tulu-30b 6.434375 guanaco-65b 6.409375 oasst-sft-7-llama-30b 6.409375 palm-2-chat-bison-001 6.400000 mpt-30b-chat 6.393750 vicuna-13b-v1.3 6.387500 wizardlm-13b 6.353125 Llama-2-7b-chat 6.268750 vicuna-7b-v1.3 5.996875 baize-v2-13b 5.750000 nous-hermes-13b 5.553459 mpt-7b-chat 5.459119 gpt4all-13b-snoozy 5.452830 koala-13b 5.350000 mpt-30b-instruct 5.218750 falcon-40b-instruct 5.168750 h2ogpt-oasst-open-llama-13b 4.625000 alpaca-13b 4.531250 chatglm-6b 4.500000 oasst-sft-4-pythia-12b 4.318750 rwkv-4-raven-14b 3.984375 dolly-v2-12b 3.275000 fastchat-t5-3b 3.040625 stablelm-tuned-alpha-7b 2.753125 llama-13b 2.606250 ``` ## Disclaimer We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out. However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided. Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models.   ## Contact If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.   ## Collaborations We are also keenly seeking support and investment for our startup, [VAGO Solutions](https://huggingface.co/VAGOsolutions), where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us. [Juanako.AI](https://huggingface.co/fblgit) is also seeking support and investment for our startup, we also are open for collaborating with other labs to make awesome models like this one. ## Acknowledgement Big Hug to [VAGO Solutions](https://huggingface.co/VAGOsolutions), we merely used our UNA transformers library on their code and dataset, nothing else. This won't be possible without them, thanks! Many thanks to [argilla](https://huggingface.co/datasets/argilla) and [Huggingface](https://huggingface.co) for providing such valuable datasets to the Open-Source community. And of course a big thanks to [upstage](https://huggingface.co/upstage) for providing the open source community with their latest technology!