README.md

---
model-index:
- name: notus-7b-v1-lora
  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
- preference
- ultrafeedback
- lora
license: mit
---

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/CuMO3IjJfymC94_5qd15T.png" alt="Image was artificially generated by Dalle-3 via ChatGPT Pro"/>
</div>

# 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, 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](https://github.com/huggingface/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**.

## 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`](https://huggingface.co/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`](https://huggingface.co/datasets/openbmb/UltraFeedback), named [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences).

## 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`

```python
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

```python
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"]
```