metadata

license: cc-by-nc-4.0
datasets:
  - conceptofmind/cot_submix_original
  - conceptofmind/flan2021_submix_original
  - conceptofmind/t0_submix_original
  - conceptofmind/niv2_submix_original
language:
  - en
pipeline_tag: text-generation

FreeWilly

Model Description

StableBeluga_13B is a Llama2 13B model finetuned on an Orca style Dataset

Usage

Start chatting with StableBeluga_13B using the following code snippet:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("stabilityai/StableBeluga_13B", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("stabilityai/StableBeluga_13B", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
system_prompt = "### System:\nYou are Free Willy, 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))

FreeWilly 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 FreeWilly2

Model Details

Developed by: Stability AI
Model type: StableBeluga_13B is an auto-regressive language model fine-tuned on Llama2 13B.
Language(s): English
Library: HuggingFace Transformers
License: Fine-tuned checkpoints (FreeWilly2) is licensed under the Non-Commercial Creative Commons license (CC BY-NC-4.0)
Contact: For questions and comments about the model, please email lm@stability.ai

Training Dataset

StableBeluga_13B 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)

Use and Limitations

Intended Use

These models are intended for research only, in adherence with the CC BY-NC-4.0 license.

Limitations and bias

Although the aforementioned dataset helps to steer the base language models into "safer" distributions of text, not all biases and toxicity can be mitigated through fine-tuning. We ask that users be mindful of such potential issues that can arise in generated responses. Do not treat model outputs as substitutes for human judgment or as sources of truth. Please use it responsibly.

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}
}