From d705984fbef3c7af8aa7f908fa45ef13eb06c2ce Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 25 Jun 2026 11:07:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: rtzr/ko-gemma-2-9b-it Source: Original Platform --- .gitattributes | 36 + README.md | 325 ++++++ config.json | 33 + generation_config.json | 8 + model-00001-of-00010.safetensors | 3 + model-00002-of-00010.safetensors | 3 + model-00003-of-00010.safetensors | 3 + model-00004-of-00010.safetensors | 3 + model-00005-of-00010.safetensors | 3 + model-00006-of-00010.safetensors | 3 + model-00007-of-00010.safetensors | 3 + model-00008-of-00010.safetensors | 3 + model-00009-of-00010.safetensors | 3 + model-00010-of-00010.safetensors | 3 + model.safetensors.index.json | 471 ++++++++ special_tokens_map.json | 34 + tokenizer.json | 3 + tokenizer.model | 3 + tokenizer_config.json | 1759 ++++++++++++++++++++++++++++++ 19 files changed, 2702 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00010.safetensors create mode 100644 model-00002-of-00010.safetensors create mode 100644 model-00003-of-00010.safetensors create mode 100644 model-00004-of-00010.safetensors create mode 100644 model-00005-of-00010.safetensors create mode 100644 model-00006-of-00010.safetensors create mode 100644 model-00007-of-00010.safetensors create mode 100644 model-00008-of-00010.safetensors create mode 100644 model-00009-of-00010.safetensors create mode 100644 model-00010-of-00010.safetensors create mode 100644 model.safetensors.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer.model create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..36db0ae --- /dev/null +++ b/README.md @@ -0,0 +1,325 @@ +--- +license: gemma +library_name: transformers +pipeline_tag: text-generation +extra_gated_heading: Access Gemma on Hugging Face +extra_gated_prompt: >- + To access Gemma on Hugging Face, you’re required to review and agree to + Google’s usage license. To do this, please ensure you’re logged in to Hugging + Face and click below. Requests are processed immediately. +extra_gated_button_content: Acknowledge license +tags: +- conversational +base_model: +- google/gemma-2-9b +language: +- ko +--- + + + +## Model Details + +### Ko-Gemma-2-9B-IT + +**[Ko-Gemma-2-9B-IT](https://huggingface.co/rtzr/ko-gemma-2-9b-it)** is a Korean-language conversational model that is part of the Gemma family of models. It is a text-to-text, decoder-only large language model, available in Korean. We fine-tuned this model on a carefully curated high-quality dataset using Supervised Fine-Tuning (SFT). And we use [Direct Preference Optimization](https://arxiv.org/abs/2305.18290) training specifically for Human Feedback. The datasets include: + +- [Orca-Math](https://huggingface.co/datasets/kuotient/orca-math-korean-dpo-pairs) +- [dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) + +Some of these datasets were partially used and translated for training. In particular, a lot of repetition occurred during the translation process, so preprocessing was performed based on N-gram. + +#### *Inputs and outputs* + +- **Input:** Text string, such as a question, a prompt, or a document to be summarized. +- **Output:** Generated Korean-language text in response to the input, such as an answer to a question, or a summary of a document. + +### Google Gemma 2 + +Gemma is a family of lightweight, state-of-the-art open models from Google, +built from the same research and technology used to create the Gemini models. +They are text-to-text, decoder-only large language models, available in English, +with open weights for both pre-trained variants and instruction-tuned variants. +Gemma models are well-suited for a variety of text generation tasks, including +question answering, summarization, and reasoning. Their relatively small size +makes it possible to deploy them in environments with limited resources such as +a laptop, desktop or your own cloud infrastructure, democratizing access to +state of the art AI models and helping foster innovation for everyone. + +## Benchmark Scores + +We evaluated it internally using [LogicKor](https://github.com/instructkr/LogicKor) code. While the public LogicKor code is assessed as GPT-4, our internal evaluation was conducted as GPT-4o. Public scores will be added as they are released. The scores below include only 0-shot evaluations. + +| Model | Math | Reasoning | Writing | Coding | Understanding | Grammar | Single ALL | Multi ALL | Overall | +|:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:|:----:| +| [rtzr/ko-gemma-2-9b-it](https://huggingface.co/rtzr/ko-gemma-2-9b-it) | 8.71 / 8.00 | 9.14 / 8.00 | 9.43 / 9.29 | 9.00 / 9.43 | 9.57 / 9.86 | 7.14 / 5.00 | 8.83 | 8.26 | 8.55 | +| [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) | 8.57 / 7.71 | 8.86 / 7.00 | 9.29 / 9.29 | 9.29 / 9.57 | 8.57 / 8.29 | 6.86 / 3.86 | 8.57 | 7.62 | 8.10 | +| [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) | 6.43 / 5.71 | 6.86 / 5.14 | 9.14 / 8.57 | 8.29 / 8.14 | 8.43 / 9.29 | 5.71 / 5.29 | 7.48 | 7.02 | 7.25 | +| [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) | 5.57 / 4.29 | 8.14 / 5.14 | 8.29 / 6.29 | 6.43 / 7.86 | 9.29 / 8.57 | 6.57 / 3.71 | 7.38 | 5.98 | 6.68 | +| [allganize/Llama-3-Alpha-Ko-8B-Instruct](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct) | 4.57 / 3.00 | 6.86 / 6.43 | 7.43 / 6.71 | 8.43 / 8.43| 7.71 / 8.71 | 6.71 / 4.43 | 6.95 | 6.29 | 6.62 | + +## Usage + +### Install Dependencies + +You must install transformers >= 4.42.3 for gemma2 models. + +```bash +pip install transformers==4.42.3 accelerate +``` + +### Python code with Pipeline + +```python +import transformers +import torch + + +model_id = "rtzr/ko-gemma-2-9b-it" + +pipeline = transformers.pipeline( + "text-generation", + model=model_id, + model_kwargs={"torch_dtype": torch.bfloat16}, + device_map="auto", +) + +pipeline.model.eval() +instruction = "서울의 유명한 관광 코스를 만들어줄래?" + +messages = [ + {"role": "user", "content": f"{instruction}"} +] + +prompt = pipeline.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +terminators = [ + pipeline.tokenizer.eos_token_id, + pipeline.tokenizer.convert_tokens_to_ids("") +] + +outputs = pipeline( + prompt, + max_new_tokens=2048, + eos_token_id=terminators, + do_sample=True, + temperature=0.6, + top_p=0.9, +) + +print(outputs[0]["generated_text"][len(prompt):]) +``` + +```markdown +서울은 역사, 문화, 현대성이 조화를 이룬 매력적인 도시입니다. 여기서 즐길 수 있는 다양한 관광지와 명소를 소개합니다. 다음은 서울의 유명한 관광 코스 3가지입니다. + +**1. 역사와 문화를 둘러싼 한국관광코스** + +1. **경복궁**: 조선 시대의 웅장한 왕궁을 만끽할 수 있는 곳입니다. 특히 매년 봄에 열리는 '춘추연회'는 경복궁의 아름다움을 더욱 돋보이게 합니다. +2. **북촌 한옥마을**: 고풍스러운 한옥이 모여있는 곳으로, 전통 문화 체험이 가능합니다. '북촌 한옥마을 문화체험관'에서는 한복 체험부터 종이만화, 한글 쓰기 등 다양한 프로그램이 준비되어 있습니다. +3. **인사동**: 서점, 미술관, 한식당이 많은 곳입니다. 특히 '인사동 문화관'에서는 서울의 역사와 문화를 이해하는 데 도움이 되는 전시를 볼 수 있습니다. +4. **광화문** 및 **명동**: 현대적인 쇼핑과 레스토랑이 즐비한 곳입니다. 광화문은 특히 젊은이들이 많은 곳으로, 스트리트 패션을 관찰하거나 밤거리에서 활기를 느낄 수 있습니다. + +**2. 도시의 모습을 바라보는 뷰투어 코스** + +1. **남산타워**: 서울의 상징적인 건물로, 꼭대기에서 펼쳐지는 360도의 경치가 압니다. 특히 밤이 되면 조명이 어우러져 더욱 아름다워집니다. +2. **서울타워**: 남산타워와 비슷한 위치로, 높이가 더 높기 때문에 더 넓은 전망을 볼 수 있습니다. 서울타워 내부에는 다양한 전시관과 레스토랑도 있습니다. +3. **북악산**: 서울의 중심부에 위치한 산으로, 서울의 경치를 조금 다른 관점에서 볼 수 있습니다. 특히 북악산 정상인 북악사에서도 좋은 전망을 볼 수 있습니다. +4. **서울숲**: 녹지 공간으로, 도시의 혼잡함에서 벗어날 수 있는 곳입니다. 또한, 서울숲 내부에는 '서울숲 아트프레젠트'라는 공간이 있어 예술과 자연을 함께 체험할 수 있습니다. + +**3. 현대 문화를 만나는 코스** + +1. **삼성동**: 현대 미술관이 많은 곳으로, '삼성 미술관', '아모리카나스 갤러리' 등이 있습니다. 또한, '코엑스'나 '아포카로포스' 등의 명소도 가까운 곳에 있습니다. +2. **이태원**: 외국인들이 많은 곳으로, 다양한 외국 음식을 즐길 수 있는 곳입니다. 또한, '이태원 글로컬문화센터'에서는 세계 각국의 문화 체험이 가능합니다. +3. **홍대**: 젊은이들의 문화가 넘치는 곳입니다. '홍대 롤링홀'은 특히 많은 사람들이 방문하는 곳입니다. 또한, '홍대 서점거리'에서는 독서와 문화를 만날 수 있습니다. +4. **강남**: 서울의 현대적 모습을 잘 보여주는 곳입니다. '강남역'을 중심으로 많은 고급 쇼핑몰과 레스토랑이 있습니다. + +이러한 코스를 통해 서울의 다양한 모습을 한 번에 만나볼 수 있을 거예요. 각자의 취향에 맞춰 코스를 조절하시면 좋겠습니다. 즐거운 여행 되세요! +``` + +### Python code with AutoModel + +```python +import os +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM + + +model_id = "rtzr/ko-gemma-2-9b-it" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.bfloat16, + device_map="auto", +) + +model.eval() +instruction = "서울의 유명한 관광 코스를 만들어줄래?" + +messages = [ + {"role": "user", "content": f"{instruction}"} +] + +input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" +).to(model.device) + +terminators = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("") +] + +outputs = model.generate( + input_ids, + max_new_tokens=2048, + eos_token_id=terminators, + do_sample=True, + temperature=0.6, + top_p=0.9, +) + +print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)) +``` + +```markdown +서울 관광 코스를 제안해드릴게요. 하루 종일 즐겁게 여행할 수 있는 루트로 구성했습니다. + +### 1. 서울역사관 및 북촌한옥마을(오전) + +- 서울역사관: 서울의 역사와 문화를 체험할 수 있는 곳입니다. 다양한 전시물과 상설전시를 통해 서울의 변화를 살펴볼 수 있습니다. +- 북촌한옥마을: 서울의 한옥을 보존하고 관리하는 곳입니다. 조선 시대의 분위기를 느낄 수 있으며, 한옥에서 문화 콘텐츠도 제공하는 곳도 많습니다. + +### 2. 북악산 입장과 북악산 등산(오전) + +- 북악산은 서울의 북쪽에 위치한 산으로, 서울 한복판에서도 자연을 만날 수 있는 곳입니다. 북악산 입구에서 등산을 시작하여, 북악산 정상까지 올라가면 서울의 전경을 볼 수 있습니다. + +### 3. 종로 명동 쇼핑과 맛집 투어(낮) + +- 명동: 다양한 쇼핑몰과 매장이 있는 곳입니다. 명동 쇼핑타운, 미스터트위스터, 미스터리마켓 등을 방문해보세요. +- 맛집 투어: 명동에는 다양한 지역 음식을 먹을 수 있는 곳이 많습니다. 떡볶이, 순대, 닭강정 등을 맛볼 수 있는 곳을 추천드립니다. + +### 4. 서울시립미술관과 덕수궁(오후) + +- 서울시립미술관: 현대미술을 전시하는 곳입니다. 특별전이 열린다면 방문해 볼 수 있습니다. +- 덕수궁: 조선시대의 궁궐입니다. 특히 봄에는 벚꽃이 아름답게 만발합니다. + +### 5. 남산타워와 남산공원 산책(오후) + +- 남산타워: 남산에 있는 관람대입니다. 남산타워에 올라가면 서울의 360도 전경을 볼 수 있습니다. +- 남산공원: 남산에 있는 공원입니다. 다양한 테마 공원과 조경이 잘 된 곳입니다. 남산공원을 산책하며 휴식을 취할 수 있습니다. + +### 6. 명동 또는 이태원에서의 저녁 식사와 문화 활동(저녁) + +- 명동: 다양한 전통적인 한국 음식을 먹을 수 있는 곳입니다. 또한, 명동은 밤에도 활기차게 활발한 문화 생활을 할 수 있는 곳입니다. +- 이태원: 외국인 관광객들이 많이 찾는 곳으로, 다양한 세계 음식을 먹을 수 있으며, 클럽이나 바가 많은 문화적 활동이 가능한 곳입니다. + +이 코스는 하루 종일 활발하게 여행을 할 수 있도록 계획했습니다. 각 지역에 따라 이동 시간을 고려하시고, 개장 시간과 전시 일정 등을 미리 확인하시는 것이 좋습니다. 즐거운 여행 되세요! +``` + +### Quantized Versions through bitsandbytes + +- *Using 8-bit precision* +- *Using 4-bit precision* + +```python +# pip install bitsandbytes +from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig + + +model_id = "rtzr/ko-gemma-2-9b-it" +quantization_config_8bit = BitsAndBytesConfig(load_in_8bit=True) +# quantization_config_4bit = BitsAndBytesConfig(load_in_4bit=True) + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.bfloat16, + device_map="auto", + quantization_config=quantization_config_8bit, + # quantization_config=quantization_config_4bit, + low_cpu_mem_usage=True, +) + +model.eval() +instruction = "서울의 유명한 관광 코스를 만들어줄래?" + +messages = [ + {"role": "user", "content": f"{instruction}"} +] + +input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" +).to(model.device) + +terminators = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("") +] + +outputs = model.generate( + input_ids, + max_new_tokens=2048, + eos_token_id=terminators, + do_sample=True, + temperature=0.6, + top_p=0.9, +) + +print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)) +``` + +### VLLM Usage + +When we use `vllm==0.5.1`, the gemma2 model cannot be loaded yet and the following [issue](https://github.com/vllm-project/vllm/issues/6237) occurs. So it is recommended to use `vllm/vllm-openai:latest` docker or [`vllm==0.5.0.post1`](https://github.com/vllm-project/vllm/releases/tag/v0.5.0.post1). + +```bash +#!/bin/bash + +VLLM_ATTENTION_BACKEND=FLASHINFER +MODEL_NAME="rtzr/ko-gemma-2-9b-it" + +MODEL_PATH="YOUR_PATH/${MODEL_NAME}" +docker run --rm --gpus all \ + -p 8000:8000 \ + --shm-size=12gb --ulimit memlock=-1 --ulimit stack=67108864 \ + -e VLLM_ATTENTION_BACKEND=${VLLM_ATTENTION_BACKEND} \ + -v $MODEL_PATH:/vllm-workspace/${MODEL_NAME} \ + vllm/vllm-openai:latest \ + --model ${MODEL_NAME} --dtype auto \ + --gpu-memory-utilization 0.8 +``` + +## License + +Gemma 2 License: + +## Citation + +```none +@article{RTZR, + title={ko-gemma-2-9b-it}, + author={Return Zero Team}, + year={2024}, + url={https://huggingface.co/rtzr/ko-gemma-2-9b-it} +} +``` + +```none +@article{gemma_2024, + title={Gemma}, + url={https://www.kaggle.com/m/3301}, + DOI={10.34740/KAGGLE/M/3301}, + publisher={Kaggle}, + author={Gemma Team}, + year={2024} +} 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