metadata

datasets:
  - conceptofmind/cot_submix_original
  - conceptofmind/flan2021_submix_original
  - conceptofmind/t0_submix_original
  - conceptofmind/niv2_submix_original
inference: false
language:
  - en
license: other
model_creator: Stability AI
model_link: https://huggingface.co/stabilityai/StableBeluga2
model_name: StableBeluga 2
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke

Want to contribute? TheBloke's Patreon page

StableBeluga 2 - GGML

Model creator: Stability AI
Original model: StableBeluga 2

Description

This repo contains GGML format model files for Stability AI's StableBeluga 2.

These 70B Llama 2 GGML files currently only support CPU inference. They are known to work with:

llama.cpp, commit e76d630 and later. CUDA GPU acceleration is supported. No Metal acceleration for macOS yet.
text-generation-webui, the most widely used web UI. Supports CUDA GPU acceleration for 70B models.
LM Studio, a fully featured local GUI with GPU acceleration. 70B GPU acceleration works on Windows, but not yet on macOS.
KoboldCpp, version 1.37 and later. A powerful GGML web UI, especially good for story telling.
llama-cpp-python, version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server.

Repositories available

Prompt template: Orca-Hashes

### System:
This is a system prompt, please behave and help the user.

### User:
{prompt}

### Assistant:

Compatibility

Requires llama.cpp commit `e76d630` or later.

Or one of the other tools and libraries listed above.

To use in llama.cpp, you must add -gqa 8 argument.

For other UIs and libraries, please check the docs.

Explanation of the new k-quant methods

Click to see details

The new methods available are:

GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
stablebeluga2-70b.ggmlv3.q2_K.bin	q2_K	2	28.59 GB	31.09 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
stablebeluga2-70b.ggmlv3.q3_K_L.bin	q3_K_L	3	36.15 GB	38.65 GB	New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
stablebeluga2-70b.ggmlv3.q3_K_M.bin	q3_K_M	3	33.04 GB	35.54 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
stablebeluga2-70b.ggmlv3.q3_K_S.bin	q3_K_S	3	29.75 GB	32.25 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
stablebeluga2-70b.ggmlv3.q4_0.bin	q4_0	4	38.87 GB	41.37 GB	Original quant method, 4-bit.
stablebeluga2-70b.ggmlv3.q4_1.bin	q4_1	4	43.17 GB	45.67 GB	Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
stablebeluga2-70b.ggmlv3.q4_K_M.bin	q4_K_M	4	41.38 GB	43.88 GB	New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
stablebeluga2-70b.ggmlv3.q4_K_S.bin	q4_K_S	4	38.87 GB	41.37 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
stablebeluga2-70b.ggmlv3.q5_0.bin	q5_0	5	47.46 GB	49.96 GB	Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
stablebeluga2-70b.ggmlv3.q5_K_M.bin	q5_K_M	5	48.75 GB	51.25 GB	New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
stablebeluga2-70b.ggmlv3.q5_K_S.bin	q5_K_S	5	47.46 GB	49.96 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
stablebeluga2-70b.ggmlv3.q5_1.bin	q5_1	5	51.76 GB	54.26 GB	Original quant method, 5-bit. Higher accuracy, slower inference than q5_0.
stablebeluga2-70b.ggmlv3.q6_K.bin	q6_K	6	56.59 GB	59.09 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
stablebeluga2-70b.ggmlv3.q8_0.bin	q8_0	8	73.23 GB	75.73 GB	Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

q5_1, q6_K and q8_0 files require expansion from archive

Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the q6_K and q8_0 files as multi-part ZIP files. They are not compressed, they are just for storing a .bin file in two parts.

q5_1

Please download:

stablebeluga2-70b.ggmlv3.q5_1.zip
stablebeluga2-70b.ggmlv3.q5_1.z01

q6_K

Please download:

stablebeluga2-70b.ggmlv3.q6_K.zip
stablebeluga2-70b.ggmlv3.q6_K.z01

q8_0

Please download:

stablebeluga2-70b.ggmlv3.q8_0.zip
stablebeluga2-70b.ggmlv3.q8_0.z01

Then extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use 7zip - the basic unzip tool did not work. Example:

sudo apt update -y && sudo apt install 7zip
7zz x stablebeluga2-70b.ggmlv3.q6_K.zip

Once the .bin is extracted you can delete the .zip and .z01 files.

How to run in `llama.cpp`

I use the following command line; adjust for your tastes and needs:

./main -t 10 -gqa 8 -m stablebeluga2.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nThis is a system prompt, please behave and help the user.\n\n### User:\nWrite a story about llamas\n\n### Assistant:"

Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.

Remember the -gqa 8 argument, required for Llama 70B models.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

How to run in `text-generation-webui`

Further instructions here: text-generation-webui/docs/llama.cpp-models.md.

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!

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: Luke from CarbonQuill, Aemon Algiz.

Patreon special mentions: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse

Thank you to all my generous patrons and donaters!

Original model card: Stability AI's StableBeluga 2

Stable Beluga 2

Model Description

Stable Beluga 2 is a Llama2 70B model finetuned on an Orca style Dataset

Usage

Start chatting with Stable Beluga 2 using the following code snippet:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("stabilityai/StableBeluga2", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("stabilityai/StableBeluga2", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
system_prompt = "### System:\nYou are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n"

message = "Write me a poem please"
prompt = f"{system_prompt}### User: {message}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Stable Beluga 2 should be used with this prompt format:

### System:
This is a system prompt, please behave and help the user.

### User:
Your prompt here

### Assistant:
The output of Stable Beluga 2

Model Details

Developed by: Stability AI
Model type: Stable Beluga 2 is an auto-regressive language model fine-tuned on Llama2 70B.
Language(s): English
Library: HuggingFace Transformers
License: Fine-tuned checkpoints (Stable Beluga 2) is licensed under the STABLE BELUGA NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT
Contact: For questions and comments about the model, please email [email protected]

Training Dataset

Stable Beluga 2 is trained on our internal Orca-style dataset

Training Procedure

Models are learned via supervised fine-tuning on the aforementioned datasets, trained in mixed-precision (BF16), and optimized with AdamW. We outline the following hyperparameters:

Dataset	Batch Size	Learning Rate	Learning Rate Decay	Warm-up	Weight Decay	Betas
Orca pt1 packed	256	3e-5	Cosine to 3e-6	100	1e-6	(0.9, 0.95)
Orca pt2 unpacked	512	3e-5	Cosine to 3e-6	100	1e-6	(0.9, 0.95)

Ethical Considerations and Limitations

Beluga is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Beluga's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Beluga, developers should perform safety testing and tuning tailored to their specific applications of the model.

Citations

@misc{touvron2023llama,
      title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
      author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
      year={2023},
      eprint={2307.09288},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

StableBeluga 2 - GGML

Description

Repositories available

Prompt template: Orca-Hashes

Compatibility

Requires llama.cpp commit e76d630 or later.

Explanation of the new k-quant methods

Provided files

q5_1, q6_K and q8_0 files require expansion from archive

q5_1

q6_K

q8_0

How to run in llama.cpp

How to run in text-generation-webui

Discord

Thanks, and how to contribute.

Original model card: Stability AI's StableBeluga 2

Stable Beluga 2

Model Description

Usage

Model Details

Training Dataset

Training Procedure

Ethical Considerations and Limitations

Citations

Requires llama.cpp commit `e76d630` or later.

How to run in `llama.cpp`

How to run in `text-generation-webui`