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RichardErkhov/
stabilityai_-_StableBeluga-7B-gguf

Name: stabilityai_-_StableBeluga-7B-gguf
Availability: InStock

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StableBeluga-7B - GGUF

Model creator: https://huggingface.co/stabilityai/
Original model: https://huggingface.co/stabilityai/StableBeluga-7B/

Name	Quant method	Size
StableBeluga-7B.Q2_K.gguf	Q2_K	2.36GB
StableBeluga-7B.IQ3_XS.gguf	IQ3_XS	2.6GB
StableBeluga-7B.IQ3_S.gguf	IQ3_S	2.75GB
StableBeluga-7B.Q3_K_S.gguf	Q3_K_S	2.75GB
StableBeluga-7B.IQ3_M.gguf	IQ3_M	2.9GB
StableBeluga-7B.Q3_K.gguf	Q3_K	3.07GB
StableBeluga-7B.Q3_K_M.gguf	Q3_K_M	3.07GB
StableBeluga-7B.Q3_K_L.gguf	Q3_K_L	3.35GB
StableBeluga-7B.IQ4_XS.gguf	IQ4_XS	3.4GB
StableBeluga-7B.Q4_0.gguf	Q4_0	3.56GB
StableBeluga-7B.IQ4_NL.gguf	IQ4_NL	3.58GB
StableBeluga-7B.Q4_K_S.gguf	Q4_K_S	3.59GB
StableBeluga-7B.Q4_K.gguf	Q4_K	3.8GB
StableBeluga-7B.Q4_K_M.gguf	Q4_K_M	3.8GB
StableBeluga-7B.Q4_1.gguf	Q4_1	3.95GB
StableBeluga-7B.Q5_0.gguf	Q5_0	4.33GB
StableBeluga-7B.Q5_K_S.gguf	Q5_K_S	4.33GB
StableBeluga-7B.Q5_K.gguf	Q5_K	4.45GB
StableBeluga-7B.Q5_K_M.gguf	Q5_K_M	4.45GB
StableBeluga-7B.Q5_1.gguf	Q5_1	4.72GB
StableBeluga-7B.Q6_K.gguf	Q6_K	5.15GB

Original model description:

datasets:

conceptofmind/cot_submix_original
conceptofmind/flan2021_submix_original
conceptofmind/t0_submix_original
conceptofmind/niv2_submix_original language:
en pipeline_tag: text-generation

Stable Beluga 7B

Use Stable Chat (Research Preview) to test Stability AI's best language models for free

Model Description

Stable Beluga 7B is a Llama2 7B model finetuned on an Orca style Dataset

Usage

Start chatting with Stable Beluga 7B using the following code snippet:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("stabilityai/StableBeluga-7B", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("stabilityai/StableBeluga-7B", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
system_prompt = "### System:\nYou are StableBeluga, 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 7B 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 7B

Model Details

Developed by: Stability AI
Model type: Stable Beluga 7B is an auto-regressive language model fine-tuned on Llama2 7B.
Language(s): English
Library: HuggingFace Transformers
License: Fine-tuned checkpoints (Stable Beluga 7B) is licensed under the STABLE BELUGA NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT
Contact: For questions and comments about the model, please email lm@stability.ai

Training Dataset

Stable Beluga 7B 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

bibtext

@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}
}