argilla/notus-7b-v1-lora

Model Card for Notus 7B v1 (LoRA)

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, when we started building distilabel, we invested time understanding and deep-diving into the UltraFeedback dataset. Using Argilla, we've found data issues in the original UltraFeedback dataset, leading to high-scores for bad responses (more details in the training data section). After curating several hundreds of data points, we decided to binarize the dataset using the preference ratings, instead of the original critique overall_score, and verified the new dataset with Argilla.

Using preference ratings, instead of critiques scores, led to a new dataset where the chosen response is different in ~50% of the cases. Using this new dataset with DPO we fine-tuned Notus, a 7B model, that surpasses Zephyr-7B-beta and Claude 2 on AlpacaEval.

Important note: While we opted for the average of multi-aspect ratings, while we fix the original dataset, a very interesting open question remains: once critique data is fixed, what works better? using the critique scores or the preference ratings? We're very excited to do this comparison in the coming weeks, stay tuned!

This model wouldn't have been possible without the amazing Alignment Handbook, OpenBMB for releasing the Ultrafeedback dataset, 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.

Why Notus?: Notus name comes from the ancient Greek god Notus, as a wink to Zephyr, which comes from the ancient Greek god Zephyrus; with the difference that Notus is the god of the south wind, and Zephyr the god of the west wind. More information at https://en.wikipedia.org/wiki/Anemoi.

Model Details

Model Description

• Developed by: Argilla, Inc. (based on HuggingFace H4 and MistralAI previous efforts and amazing work)
• Shared by: Argilla, Inc.
• Model type: GPT-like 7B model DPO fine-tuned using LoRA
• Language(s) (NLP): Mainly English
• License: Apache 2.0 (same as Zephyr 7B SFT and Mistral 7B v0.1)
• Finetuned from model: alignment-handbook/zephyr-7b-sft-full

Model Sources [optional]

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

Training Details

Training Hardware

We used a VM with 8 x A100 40GB hosted in GCP.

Training Data

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

TL;DR

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](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta):

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

Usage

Note that the LoRA adapter is already merged into the model.

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

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-lora", 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"]