metadata

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

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

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

Luna SOLARkrautLM Instruct - AWQ

Model creator: FBL
Original model: Luna SOLARkrautLM Instruct

Description

This repo contains AWQ model files for FBL's Luna SOLARkrautLM Instruct.

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

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

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 - using Loader: AutoAWQ
vLLM - version 0.2.2 or later for support for all model types.
Hugging Face Text Generation Inference (TGI)
Transformers version 4.35.0 and later, from any code or client that supports Transformers
AutoAWQ - for use from Python code

Repositories available

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	4	128	German Quad	2048	5.96 GB

How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui.

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

Click the Model tab.
Under Download custom model or LoRA, enter TheBloke/LUNA-SOLARkrautLM-Instruct-AWQ.
Click Download.
The model will start downloading. Once it's finished it will say "Done".
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded: LUNA-SOLARkrautLM-Instruct-AWQ
Select Loader: AutoAWQ.
Click Load, and the model will load and is now ready for use.
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.
Once you're ready, click the Text Generation tab and enter a prompt to get started!

Multi-user inference server: vLLM

Documentation on installing and using vLLM can be found here.

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

For example:

python3 -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:

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:

--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 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)

Inference from Python code using Transformers

Install the necessary packages

Requires: Transformers 4.35.0 or later.
Requires: AutoAWQ 0.1.6 or later.

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:

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 using the pre-built wheels, install it from source instead:

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

Transformers example code (requires Transformers 4.35.0 and later)

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 using Loader: AutoAWQ.
vLLM version 0.2.0 and later.
Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.
Transformers version 4.35.0 and later.
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

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: 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

VAGO solutions LUNA-SOLARkrautLM-Instruct

Introducing LUNA-SOLARkrautLM-Instruct – a UNA-Sauerkraut version of the powerful upstage/SOLAR-10.7B-Instruct-v1.0 ! Aligned with DPO and tamed with UNA.

Overview of all LUNA-SOLARkrautLM-Instruct models
Model Details
Evaluation
Disclaimer
Contact
Collaborations
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 and the powerful set of SauerkrautLM-SOLAR-Instruct
Language(s): English, German
License: cc-by-nc-4.0
Contact: Website David Golchinfar Juanako.AI - UNA

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 as rejected answers. Added with additional translated Parts of the HuggingFaceH4/ultrafeedback_binarized (Our dataset do not contain any TruthfulQA prompts - check Data Contamination Test Results) and 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. We are also grateful for your feedback and suggestions.

Collaborations

We are also keenly seeking support and investment for our startup, VAGO Solutions, 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 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, 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 and Huggingface for providing such valuable datasets to the Open-Source community. And of course a big thanks to upstage for providing the open source community with their latest technology!

TheBloke
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LUNA-SOLARkrautLM-Instruct-AWQ