--- language: - de license: apache-2.0 tags: - dpo - alignment-handbook - awq - quantization ---
# 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|> <|user|> {prompt} <|assistant|> ``` It is also possible to use the model in a multi-turn setup ``` <|system|> <|user|> {prompt_1} <|assistant|> {answer_1} <|user|> {prompt_2} <|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