Stablebeluga2

🚀 Stable Beluga 2

Stable Beluga 2是基於Llama2 70B模型微調的自迴歸語言模型，適用於文本生成任務。本倉庫對原模型進行了優化，在存儲和加載方面有更好的表現。

🚀 快速開始

使用以下代碼片段開始與 Stable Beluga 2 進行對話：

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 應使用以下提示格式：

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

### User:
Your prompt here

### Assistant:
The output of Stable Beluga 2

✨ 主要特性

本倉庫包含來自 stabilityai/StableBeluga2 倉庫的模型，並進行了以下更改：

1. 以 bfloat16 而非 float32 存儲權重：這使得文件大小縮小了2倍，同時會有輕微的質量損失，但與Petals默認使用的NF4量化所導致的損失相比，並不顯著。
2. 以小分片形式存儲權重：每個Transformer塊存儲在其自己的分片中（每個1.71 GB）。輸入和輸出嵌入以及相鄰的層歸一化也存儲在單獨的分片中（1.05 GB）。這樣，Petals客戶端和服務器無需下載除實際使用的層之外的任何多餘數據。
3. 使用 Safetensors 而非Pickle：這允許更快的加載速度和更低的內存要求。

📚 詳細文檔

模型描述

Stable Beluga 2 是在Llama2 70B模型基礎上，針對Orca風格數據集進行微調的模型。

模型詳情

訓練數據集

Stable Beluga 2 在內部的Orca風格數據集上進行訓練。

訓練過程

模型通過在上述數據集上進行監督微調進行學習，採用混合精度（BF16）訓練，並使用AdamW進行優化。以下是相關超參數：

倫理考量與侷限性

Beluga是一項新技術，使用時存在風險。到目前為止的測試僅使用英語進行，且無法涵蓋所有場景。因此，與所有大語言模型一樣，Beluga的潛在輸出無法提前預測，在某些情況下，模型可能會對用戶提示產生不準確、有偏見或其他令人反感的響應。因此，在部署Beluga的任何應用之前，開發人員應針對其特定應用對模型進行安全測試和調整。

引用方式

@misc{StableBelugaModels, 
      url={[https://huggingface.co/stabilityai/StableBeluga2](https://huggingface.co/stabilityai/StableBeluga2)}, 
      title={Stable Beluga models}, 
      author={Mahan, Dakota and Carlow, Ryan and Castricato, Louis and Cooper, Nathan and Laforte, Christian}
}

參考文獻

@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}
}

其他Beluga模型

• StableBeluga 1 - Delta
• StableBeluga 13B
• StableBeluga 7B