transformers safetensors mistral text-generation dpo alignment-handbook awq quantization conversational de arxiv:2401.10580 license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible 4-bit region:us
---
language:
- de
license: apache-2.0
tags:
- dpo
- alignment-handbook
- awq
- quantization
---
<div align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/6474c16e7d131daf633db8ad/-mL8PSG00X2lEw1lb8E1Q.png>
</div>

# AWQ-Version of Phoenix

| Bits | GS | AWQ Dataset | Seq Len |
| ---- | -- | ----------- | ------- |
| 4 | 128 | c4 | 4096 |

# Model Card for Phoenix

**Phoenix** is a model trained using Direct Preference Optimization (DPO) for the german language. Its training procedure follows the process of the alignment-handbook from Huggingface. 
In contrast to zephyr and notus this model has been trained using german instruction and dpo data. In detail, a german translation of HuggingFaceH4/ultrachat_200k
and HuggingFaceH4/ultrafeedback_binarized were created in addition to a series of allready available instruction datasets. The LLM haoranxu/ALMA-13B was used for this. 
While the mistral model performs really well, it is not really suitable for the german language. Therefore we have used the fantastic LeoLM/leo-mistral-hessianai-7b.
Thanks to the new type of training, Phoenix is not only able to compete with the Mistral model from LeoLM but also **beats the Llama-70b-chat model in 2 mt-bench categories**.
This model **wouldn't have been possible without the amazing work of Huggingface, LeoLM, openbnb, argilla, the Alma-Team and many others of the AI community**.
i would like to personally thank all AI researchers who make the training of such models possible

## MT-Bench-DE Scores
Phoenix beats the LeoLM-Mistral model in all categories except for coding and humanities.
Additionally it also Beats LeoLM/Llama-2-70b-chat in roleplay and reasoning which shows the power of DPO.

```
{
    "first_turn": 6.39375,
    "second_turn": 5.1625,
    "categories": {
        "writing": 7.45,
        "roleplay": 7.9,
        "reasoning": 4.3,
        "math": 3.25,
        "coding": 2.5,
        "extraction": 5.9,
        "stem": 7.125,
        "humanities": 7.8
    },
    "average": 5.778124999999999
}
```

## Other Evaluations

Florian Leurer compared Phoenix to other LLMs. Check it out here:

['Evaluation of German LLMs'](https://www.linkedin.com/posts/florian-leuerer-927479194_vermutlich-relativ-unbeobachtet-ist-gestern-activity-7151475428019388418-sAKR?utm_source=share&utm_medium=member_desktop)


## Model Details

### Model Description

- **Developed by:** Matthias Uhlig (based on HuggingFace H4, Argillla and MistralAI previous efforts and amazing work)
- **Shared by:** Matthias Uhlig
- **Model type:** GPT-like 7B model DPO fine-tuned
- **Language(s) (NLP):** German
- **License:** Apache 2.0 (same as alignment-handbook/zephyr-7b-dpo-full)
- **Finetuned from model:** [`LeoLM/leo-mistral-hessianai-7b`](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b)

### Model Sources

- **Repository:** -
- **Paper:** [`PHOENIX: Open-Source Language Adaption for Direct Preference Optimization`](https://arxiv.org/abs/2401.10580)
- **Demo:** -

## Training Details

### Training Hardware

We used a VM with 8 x A100 80GB hosted in Runpods.io.

### Training Data

We used a new translated version of [`HuggingFaceH4/ultrachat_200k`](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), and [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences).

The data used for training will be made public after additional quality inspection.

## 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|>
```

It is also possible to use the model in a multi-turn setup
```
<|system|>
</s>
<|user|>
{prompt_1}</s>
<|assistant|>
{answer_1}</s>
<|user|>
{prompt_2}</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`
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("DRXD1000/Phoenix-AWQ", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("DRXD1000/Phoenix-AWQ")
prompt = [
    {
        "role": "system",
        "content": "", #Not recommended. Phoenix does not react well on system prompts
    },
    {"role": "user", "content": "Erkläre mir was KI ist"},
]
inputs = tokenizer.apply_chat_template(prompt,  return_tensors="pt").to("cuda")
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)
```

## Ethical Considerations and Limitations
 
As with all LLMs, the potential outputs of `DRXD1000/Phoenix` cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of `DRXD1000/Phoenix`, developers should
perform safety testing and tuning tailored to their specific applications of the model.
Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

#### SFT Training
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1

#### DPO Training
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Citation
```
@misc{uhlig2024phoenix,
      title={PHOENIX: Open-Source Language Adaption for Direct Preference Optimization}, 
      author={Matthias Uhlig and Sigurd Schacht and Sudarshan Kamath Barkur},
      year={2024},
      eprint={2401.10580},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1