From 5fbc69aa818d996423958a08303bc4e3a0fe3e59 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 7 Jun 2026 10:10:17 +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: beomi/gemma-ko-2b Source: Original Platform --- .gitattributes | 36 +++++ README.md | 223 +++++++++++++++++++++++++++++++ config.json | 27 ++++ generation_config.json | 7 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 171 ++++++++++++++++++++++++ special_tokens_map.json | 30 +++++ tokenizer.json | 3 + tokenizer.model | 3 + tokenizer_config.json | 49 +++++++ 11 files changed, 555 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-00002.safetensors create mode 100644 model-00002-of-00002.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..93be774 --- /dev/null +++ b/README.md @@ -0,0 +1,223 @@ +--- +language: +- ko +- en +license: other +library_name: transformers +tags: +- pytorch +license_name: gemma-terms-of-use +license_link: https://ai.google.dev/gemma/terms +pipeline_tag: text-generation +--- + +# Gemma-Ko + +> Update @ 2024.03.26: First release of Gemma-Ko 2B model + +**Original Gemma Model Page**: [Gemma](https://ai.google.dev/gemma/docs) + +This model card corresponds to the 2B base version of the **Gemma-Ko** model. + +**Resources and Technical Documentation**: + +* [Original Google's Gemma-2B](https://huggingface.co/google/gemma-2b) +* [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM) + +**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) + +**Citation** + +```bibtex +@misc {gemma_ko_7b, + author = { {Junbum Lee, Taekyoon Choi} }, + title = { gemma-ko-7b }, + year = 2024, + url = { https://huggingface.co/beomi/gemma-ko-7b }, + doi = { 10.57967/hf/1859 }, + publisher = { Hugging Face } +} +``` + +**Model Developers**: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon) + +## Model Information + +Summary description and brief definition of inputs and outputs. + +### Description + +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, 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. + +### Usage + +Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. + +#### Running the model on a CPU + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM + +tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-2b") +model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-2b") + +input_text = "머신러닝과 딥러닝의 차이는" +input_ids = tokenizer(input_text, return_tensors="pt") + +outputs = model.generate(**input_ids) +print(tokenizer.decode(outputs[0])) +``` + + +#### Running the model on a single / multi GPU + +```python +# pip install accelerate +from transformers import AutoTokenizer, AutoModelForCausalLM + +tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-2b") +model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-2b", device_map="auto") + +input_text = "머신러닝과 딥러닝의 차이는" +input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") + +outputs = model.generate(**input_ids) +print(tokenizer.decode(outputs[0])) +``` + +#### Other optimizations + +* _Flash Attention 2_ + +First make sure to install `flash-attn` in your environment `pip install flash-attn` + +```diff +model = AutoModelForCausalLM.from_pretrained( + "beomi/gemma-ko-2b", + torch_dtype=torch.float16, ++ attn_implementation="flash_attention_2" +).to(0) +``` + +### Inputs and outputs + +* **Input:** Text string, such as a question, a prompt, or a document to be + summarized. +* **Output:** Generated Korean/English-language text in response to the input, such + as an answer to a question, or a summary of a document. + +## Implementation Information + +Details about the model internals. + +### Software + +Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM). + + +## Evaluation + +Model evaluation metrics and results. + +### Benchmark Results + +TBD + +## Usage and Limitations + +These models have certain limitations that users should be aware of. + +### Intended Usage + +Open Large Language Models (LLMs) have a wide range of applications across +various industries and domains. The following list of potential uses is not +comprehensive. The purpose of this list is to provide contextual information +about the possible use-cases that the model creators considered as part of model +training and development. + +* Content Creation and Communication + * Text Generation: These models can be used to generate creative text formats + such as poems, scripts, code, marketing copy, and email drafts. +* Research and Education + * Natural Language Processing (NLP) Research: These models can serve as a + foundation for researchers to experiment with NLP techniques, develop + algorithms, and contribute to the advancement of the field. + * Language Learning Tools: Support interactive language learning experiences, + aiding in grammar correction or providing writing practice. + * Knowledge Exploration: Assist researchers in exploring large bodies of text + by generating summaries or answering questions about specific topics. + +### Limitations + +* Training Data + * The quality and diversity of the training data significantly influence the + model's capabilities. Biases or gaps in the training data can lead to + limitations in the model's responses. + * The scope of the training dataset determines the subject areas the model can + handle effectively. +* Context and Task Complexity + * LLMs are better at tasks that can be framed with clear prompts and + instructions. Open-ended or highly complex tasks might be challenging. + * A model's performance can be influenced by the amount of context provided + (longer context generally leads to better outputs, up to a certain point). +* Language Ambiguity and Nuance + * Natural language is inherently complex. LLMs might struggle to grasp subtle + nuances, sarcasm, or figurative language. +* Factual Accuracy + * LLMs generate responses based on information they learned from their + training datasets, but they are not knowledge bases. They may generate + incorrect or outdated factual statements. +* Common Sense + * LLMs rely on statistical patterns in language. They might lack the ability + to apply common sense reasoning in certain situations. + +### Ethical Considerations and Risks + +The development of large language models (LLMs) raises several ethical concerns. +In creating an open model, we have carefully considered the following: + +* Bias and Fairness + * LLMs trained on large-scale, real-world text data can reflect socio-cultural + biases embedded in the training material. These models underwent careful + scrutiny, input data pre-processing described and posterior evaluations + reported in this card. +* Misinformation and Misuse + * LLMs can be misused to generate text that is false, misleading, or harmful. + * Guidelines are provided for responsible use with the model, see the + [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). +* Transparency and Accountability: + * This model card summarizes details on the models' architecture, + capabilities, limitations, and evaluation processes. + * A responsibly developed open model offers the opportunity to share + innovation by making LLM technology accessible to developers and researchers + across the AI ecosystem. + +Risks identified and mitigations: + +* Perpetuation of biases: It's encouraged to perform continuous monitoring + (using evaluation metrics, human review) and the exploration of de-biasing + techniques during model training, fine-tuning, and other use cases. +* Generation of harmful content: Mechanisms and guidelines for content safety + are essential. Developers are encouraged to exercise caution and implement + appropriate content safety safeguards based on their specific product policies + and application use cases. +* Misuse for malicious purposes: Technical limitations and developer and + end-user education can help mitigate against malicious applications of LLMs. + Educational resources and reporting mechanisms for users to flag misuse are + provided. Prohibited uses of Gemma models are outlined in the + [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). +* Privacy violations: Models were trained on data filtered for removal of PII + (Personally Identifiable Information). Developers are encouraged to adhere to + privacy regulations with privacy-preserving techniques. + +## Acknowledgement + +The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..96cf5a9 --- /dev/null +++ b/config.json @@ -0,0 +1,27 @@ +{ + "_name_or_path": "Taekyoon/gemma-ko-2b-dev", + "architectures": [ + "GemmaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 2, + "eos_token_id": 1, + "head_dim": 256, + "hidden_act": "gelu", + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 16384, + "max_position_embeddings": 8192, + "model_type": "gemma", + "num_attention_heads": 8, + "num_hidden_layers": 18, + "num_key_value_heads": 1, + "pad_token_id": 0, + "rms_norm_eps": 1e-06, + "rope_theta": 10000.0, + "torch_dtype": "bfloat16", + "transformers_version": "4.38.2", + "use_cache": true, + 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