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--- |
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thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png |
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license: gemma |
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language: |
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- ja |
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- en |
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tags: |
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- gemma2 |
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- conversational |
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base_model: |
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- google/gemma-2-2b |
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- google/gemma-2-2b-it |
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- rinna/gemma-2-baku-2b |
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base_model_relation: merge |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/gemma-2-baku-2b-it-GGUF |
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This is quantized version of [rinna/gemma-2-baku-2b-it](https://huggingface.co/rinna/gemma-2-baku-2b-it) created using llama.cpp |
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# Original Model Card |
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# `Gemma 2 Baku 2B Instruct (rinna/gemma-2-baku-2b-it)` |
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# Overview |
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The model is an instruction-tuned variant of [rinna/gemma-2-baku-2b](https://huggingface.co/rinna/gemma-2-baku-2b), utilizing Chat Vector and Odds Ratio Preference Optimization (ORPO) for fine-tuning. It adheres to the gemma-2 chat format. |
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| Size | Continual Pre-Training | Instruction-Tuning | |
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| :- | :- | :- | |
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| 2B | Gemma 2 Baku 2B [[HF]](https://huggingface.co/rinna/gemma-2-baku-2b) | Gemma 2 Baku 2B Instruct [[HF]](https://huggingface.co/rinna/gemma-2-baku-2b-it) | |
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* **Model architecture** |
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A 26-layer, 2304-hidden-size transformer-based language model. Please refer to the [Gemma 2 Model Card](https://www.kaggle.com/models/google/gemma-2/) for detailed information on the model's architecture. |
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* **Training** |
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**Model merging.** The base model was endowed with instruction-following capabilities through a chat vector addition process. The chat vector was derived by subtracting the parameter vectors of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) from [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it), as follows. |
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~~~~text |
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rinna/gemma-2-baku-2b + 1.0 * (google/gemma-2-2b-it - google/gemma-2-2b) |
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~~~~ |
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During this process, the embedding layer was excluded during the subtraction and addition of parameter vectors. |
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**OPRO** was applied using a subset of the following dataset to further refine the performance of the merged model. |
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- rinna's internal dataset |
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* **Contributors** |
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- [Xinqi Chen](https://huggingface.co/Keely0419) |
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- [Toshiaki Wakatsuki](https://huggingface.co/t-w) |
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- [Kei Sawada](https://huggingface.co/keisawada) |
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--- |
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# Benchmarking |
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Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html). |
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--- |
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# How to use the model |
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~~~~python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_id = "rinna/gemma-2-baku-2b-it" |
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dtype = torch.bfloat16 |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="cuda", |
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torch_dtype=dtype, |
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attn_implementation="eager", |
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) |
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chat = [ |
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{ "role": "user", "content": "西田幾多郎とはどんな人物ですか？" }, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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input_ids, |
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max_new_tokens=512, |
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) |
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response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True) |
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print(response) |
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~~~~ |
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It is recommended to use eager attention when conducting batch inference under bfloat16 precision. |
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Currently, Gemma 2 yields NaN values for input sequences with padding when the default attention mechanism (torch.scaled_dot_product_attention) is employed in conjunction with bfloat16. |
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--- |
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# Tokenization |
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The model uses the original [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) tokenizer. |
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--- |
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# How to cite |
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```bibtex |
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@misc{rinna-gemma-2-baku-2b-it, |
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title = {rinna/gemma-2-baku-2b-it}, |
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author = {Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei}, |
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url = {https://huggingface.co/rinna/gemma-2-baku-2b-it} |
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} |
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@inproceedings{sawada2024release, |
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title = {Release of Pre-Trained Models for the {J}apanese Language}, |
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author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh}, |
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booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, |
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month = {5}, |
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year = {2024}, |
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pages = {13898--13905}, |
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url = {https://aclanthology.org/2024.lrec-main.1213}, |
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note = {\url{https://arxiv.org/abs/2404.01657}} |
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} |
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``` |
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--- |
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# References |
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```bibtex |
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@article{gemma-2-2024, |
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title = {Gemma 2}, |
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url = {https://www.kaggle.com/models/google/gemma-2}, |
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publisher = {Kaggle}, |
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author = {Gemma Team}, |
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year = {2024} |
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} |
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@article{huang2023chat, |
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title = {Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages}, |
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author = {Huang, Shih-Cheng and Li, Pin-Zu and Hsu, Yu-Chi and Chen, Kuang-Ming and Lin, Yu Tung and Hsiao, Shih-Kai and Tzong-Han Tsai, Richard and Lee, Hung-yi}, |
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year = {2023}, |
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url = {https://arxiv.org/abs/2310.04799} |
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} |
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@article{hong2024orpo, |
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title = {ORPO: Monolithic Preference Optimization without Reference Model}, |
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author = {Hong, Jiwoo and Lee, Noah and Thorne, James}, |
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year = {2024}, |
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url = {https://arxiv.org/abs/2403.07691} |
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} |
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``` |
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--- |
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# License |
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[Gemma Terms of Use](https://ai.google.dev/gemma/terms) |
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