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README.md

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+
+---
+
+thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
+license: gemma
+language:
+- ja
+- en
+tags:
+- gemma2
+- conversational
+base_model:
+- google/gemma-2-2b
+- google/gemma-2-2b-it
+- rinna/gemma-2-baku-2b
+base_model_relation: merge
+pipeline_tag: text-generation
+library_name: transformers
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/gemma-2-baku-2b-it-GGUF
+This is quantized version of [rinna/gemma-2-baku-2b-it](https://huggingface.co/rinna/gemma-2-baku-2b-it) created using llama.cpp
+
+# Original Model Card
+
+
+
+# `Gemma 2 Baku 2B Instruct (rinna/gemma-2-baku-2b-it)`
+
+![rinna-icon](./rinna.png)
+
+# Overview
+
+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.
+
+| Size | Continual Pre-Training | Instruction-Tuning |
+| :-   | :-                     | :-                 |
+| 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) |
+
+* **Model architecture**
+
+    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.
+
+* **Training**
+
+    **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. 
+
+    ~~~~text
+    rinna/gemma-2-baku-2b + 1.0 * (google/gemma-2-2b-it - google/gemma-2-2b)
+    ~~~~
+
+    During this process, the embedding layer was excluded during the subtraction and addition of parameter vectors.
+    
+    **OPRO** was applied using a subset of the following dataset to further refine the performance of the merged model.
+
+    - rinna's internal dataset
+  
+* **Contributors**
+
+    - [Xinqi Chen](https://huggingface.co/Keely0419)
+    - [Toshiaki Wakatsuki](https://huggingface.co/t-w)
+    - [Kei Sawada](https://huggingface.co/keisawada)
+
+---
+
+# Benchmarking
+
+Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
+
+---
+
+# How to use the model
+
+~~~~python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "rinna/gemma-2-baku-2b-it"
+dtype = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype=dtype,
+    attn_implementation="eager",
+)
+
+chat = [
+    { "role": "user", "content": "西田幾多郎とはどんな人物ですか？" },
+]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+)
+
+response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
+print(response)
+~~~~
+
+It is recommended to use eager attention when conducting batch inference under bfloat16 precision. 
+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.
+
+---
+
+# Tokenization
+The model uses the original [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) tokenizer.
+
+---
+
+# How to cite
+```bibtex
+@misc{rinna-gemma-2-baku-2b-it,
+    title = {rinna/gemma-2-baku-2b-it},
+    author = {Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei},
+    url = {https://huggingface.co/rinna/gemma-2-baku-2b-it}
+}
+
+@inproceedings{sawada2024release,
+    title = {Release of Pre-Trained Models for the {J}apanese Language},
+    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
+    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
+    month = {5},
+    year = {2024},
+    pages = {13898--13905},
+    url = {https://aclanthology.org/2024.lrec-main.1213},
+    note = {\url{https://arxiv.org/abs/2404.01657}}
+}
+```
+---
+
+# References
+```bibtex
+@article{gemma-2-2024,
+    title = {Gemma 2},
+    url = {https://www.kaggle.com/models/google/gemma-2},
+    publisher = {Kaggle},
+    author = {Gemma Team},
+    year = {2024}
+}
+
+@article{huang2023chat,
+    title = {Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages},
+    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},
+    year = {2023},
+    url = {https://arxiv.org/abs/2310.04799}
+}
+
+@article{hong2024orpo,
+  title = {ORPO: Monolithic Preference Optimization without Reference Model},
+  author = {Hong, Jiwoo and Lee, Noah and Thorne, James},
+  year = {2024},
+  url = {https://arxiv.org/abs/2403.07691}
+}
+```
+---
+
+# License
+[Gemma Terms of Use](https://ai.google.dev/gemma/terms)