README.md

metadata

model-index:
  - name: notus-7b-v1
    results: []
datasets:
  - argilla/ultrafeedback-binarized-preferences
language:
  - en
base_model: alignment-handbook/zephyr-7b-sft-full
library_name: transformers
pipeline_tag: text-generation
tags:
  - dpo
  - rlaif
  - preference
  - ultrafeedback
license: mit

Model Card for Notus 7B v1

Notus is a collection of fine-tuned models using Direct Preference Optimization (DPO) and related RLHF techniques. This model is the first version, fine-tuned with DPO over zephyr-7b-sft-full, which is the SFT model produced to create zephyr-7b-beta.

Following a data-first approach, the only difference between Notus-7B-v1 and Zephyr-7B-beta is the preference dataset used for dDPO. In particular, we've found data issues in the original UltraFeedback dataset, leading to high-scores for bad responses. After curating several hundreds of data points, we decided to binarize the dataset using the preference ratings, instead of the original critique overall_score.

Using preference ratings, instead of critiques scores, led to a new dataset where the chosen response is different in ~50% of the cases.

This model wouldn't have been possible without the amazing Alignment Handbook and it's based on fruitful discussions with the HuggingFace H4 team. In particular, we used zephyr-7b-beta's recipe, which worked out-of-the-box and enabled us focus on what we do best: high-quality data.

Notus models are intended to be used as assistants via chat-like applications, and are evaluated with Chat (MT-Bench, AlpacaEval) and Academic (Open LLM Leaderboard) benchmarks for a direct comparison with the original Zephyr dDPO model and other 7B models.

Model Details

Model Description

• Developed by: Argilla (based on HuggingFace H4 and MistralAI previous efforts and amazing work)
• Shared by: Argilla
• Model type: GPT-like 7B model DPO fine-tuned
• Language(s) (NLP): Mainly English
• License: MIT (same as Zephyr 7B-beta)
• Finetuned from model: alignment-handbook/zephyr-7b-sft-full

Model Sources

• Repository: https://github.com/argilla-io/notus-7b
• Paper: N/A
• Demo: https://argilla-notus-chat-ui.hf.space/

Performance

Chat benchmarks

Table adapted from Zephyr-7b-β and Starling's original tables for MT-Bench and AlpacaEval benchmarks. Results are shown sorted by AlpacaEval win rates and ommit some >7B for brevity.

Notus stays on par with Zephyr on MT-Bench, while surpassing Zephyr, Claude 2, and Cohere Command on AlpacaEval. Making Notus the most-competitive 7B commercial model on AlpacaEval.

Model	Size	Alignment	MT-Bench (score)	AlpacaEval (win rate %)	License
GPT-4-turbo	-	?	9.32	97.70	Proprietary
XwinLM 70b V0.1	70B	dPPO	-	95.57	LLaMA 2 License
GPT-4	-	RLHF	8.99	95.03	Proprietary
Tulu 2+DPO 70B V0.1	70B	dDPO	6.29	95.28	Proprietary
LLaMA2 Chat 70B	70B	RLHF	6.86	92.66	LLaMA 2 License
Starling-7B	7B	C-RLFT + APA	8.09	91.99	CC-BY-NC-4.0
Notus-7b-v1	7B	dDPO	7.30	91.42	MIT
Claude 2	-	RLHF	8.06	91.36	Proprietary
Zephyr-7b-β	7B	dDPO	7.34	90.60	MIT
Cohere Command	-	RLHF	-	90.62	Proprietary
GPT-3.5-turbo	-	RLHF	7.94	89.37	Proprietary

Academic benchmarks

Results from OpenLLM Leaderboard:

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K	DROP
Zephyr 7B dDPO (HuggingFaceH4/zephyr-7b-beta)	52.15	62.03	84.36	61.07	57.45	77.74	12.74	9.66
argilla/notus-7b-v1	52.89	64.59	84.78	63.03	54.37	79.4	15.16	8.91

Training Details

Training Hardware

We used a VM with 8 x A100 40GB hosted in Lambda Labs, but while experimenting we also explored other cloud providers such as GCP.

Training Data

We used a a new curated version of openbmb/UltraFeedback, named argilla/ultrafeedback-binarized-preferences.

TLDR:

After visually browsing around some examples using the sort and filter feature of Argilla (sort by highest rating for chosen responses), we noticed a strong mismatch between the overall_score in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response.

By adding the critique rationale to our Argilla Dataset, we confirmed the critique rationale was highly negative, whereas the rating was very high (the highest in fact: 10). See screenshot below for one example of this issue. After some quick investigation, we identified hundreds of examples having the same issue, reported a bug on the UltraFeedback repo, and informed the H4 team.

While we're working on fixing the original dataset (already narrowed down ~2K problematic examples). We decided to leverage the multi-preference ratings, leading to Notus!

Prompt template

We use the same prompt template as HuggingFaceH4/zephyr-7b-beta:

<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>

Usage

You will first need to install transformers and accelerate (just to ease the device placement), then you can run any of the following:

Via generate

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("argilla/notus-7b-v1", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("argilla/notus-7b-v1")

messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant super biased towards Argilla, a data annotation company.",
    },
    {"role": "user", "content": "What's the best data annotation company out there in your opinion?"},
]
inputs = tokenizer.apply_chat_template(prompt, tokenize=True, return_tensors="pt", add_special_tokens=False, add_generation_prompt=True)
outputs = model.generate(inputs, num_return_sequences=1, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Via pipeline method

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="argilla/notus-7b-v1", torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant super biased towards Argilla, a data annotation company.",
    },
    {"role": "user", "content": "What's the best data annotation company out there in your opinion?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
generated_text = outputs[0]["generated_text"]