richarderkhov/hodachi_-_ezo-common-t2-2b-gemma-2-it-gguf overview

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    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nEZO-Common-T2-2B-gemma-2-it - GGUF\n- Model creator: https://huggingface.co/HODACHI/\n- Original model: https://huggingface.co/HODACHI/EZO-Common-T2-2B-gemma-2-it/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [EZO-Common-T2-2B-gemma-2-it.Q2_K.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q2_K.gguf) | Q2_K | 1.15GB |\n| [EZO-Common-T2-2B-gemma-2-it.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.IQ3_XS.gguf) | IQ3_XS | 1.22GB |\n| [EZO-Common-T2-2B-gemma-2-it.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.IQ3_S.gguf) | IQ3_S | 1.27GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q3_K_S.gguf) | Q3_K_S | 1.27GB |\n| [EZO-Common-T2-2B-gemma-2-it.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.IQ3_M.gguf) | IQ3_M | 1.3GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q3_K.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q3_K.gguf) | Q3_K | 1.36GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q3_K_M.gguf) | Q3_K_M | 1.36GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q3_K_L.gguf) | Q3_K_L | 1.44GB |\n| [EZO-Common-T2-2B-gemma-2-it.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.IQ4_XS.gguf) | IQ4_XS | 1.47GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q4_0.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q4_0.gguf) | Q4_0 | 1.52GB |\n| [EZO-Common-T2-2B-gemma-2-it.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.IQ4_NL.gguf) | IQ4_NL | 1.53GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q4_K_S.gguf) | Q4_K_S | 1.53GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q4_K.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q4_K.gguf) | Q4_K | 1.59GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q4_K_M.gguf) | Q4_K_M | 1.59GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q4_1.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q4_1.gguf) | Q4_1 | 1.64GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q5_0.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q5_0.gguf) | Q5_0 | 1.75GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q5_K_S.gguf) | Q5_K_S | 1.75GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q5_K.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q5_K.gguf) | Q5_K | 1.79GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q5_K_M.gguf) | Q5_K_M | 1.79GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q5_1.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q5_1.gguf) | Q5_1 | 1.87GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q6_K.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q6_K.gguf) | Q6_K | 2.0GB |\n| [EZO-Common-T2-2B-gemma-2-it.Q8_0.gguf](https://huggingface.co/RichardErkhov/HODACHI_-_EZO-Common-T2-2B-gemma-2-it-gguf/blob/main/EZO-Common-T2-2B-gemma-2-it.Q8_0.gguf) | Q8_0 | 2.59GB |\n\n\n\n\nOriginal model description:\n---\nlicense: gemma\nlibrary_name: transformers\npipeline_tag: text-generation\ntags:\n- conversational\n---\n\n# [EZO model card]\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/0OYFqT8kACowa9bY1EZF6.png)\n\n## [Model Information]\nThis model is based on Gemma-2-2B-it, enhanced with multiple tuning techniques to improve its general performance. While it excels in Japanese language tasks, it's designed to meet diverse needs globally.\n\nGemma-2-2B-itをベースとして、複数のチューニング手法を採用のうえ、汎用的に性能を向上させたモデルです。日本語タスクに優れつつ、世界中の多様なニーズに応える設計となっています。\n\n### [Benchmark Results]\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/N4HgxZTeAUQxCOfn8rlDd.png)\n\n**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-2b-it)\n\nThis model is based on Gemma-2-2B-it and is subject to the Gemma Terms of Use. For detailed information, please refer to the official Gemma license page.\n\nこのモデルはGemma-2-2B-itをベースにしており、Gemmaの利用規約に従います。詳細については、Gemmaの公式ライセンスページをご参照ください。\n\n### 推奨される使用ガイドライン / Recommended Usage Guidelines\n\n1. **商用利用**: 本モデルを商用目的で使用する場合、info@axcxept.com へのメール連絡を強く推奨します。これにより、モデルの応用や改善についての協力の機会が生まれる可能性があります。\n\n2. **クレジット表記**: 本モデルを使用または改変する際は、以下のようなクレジット表記を行うことを推奨します：\n   \"This project utilizes HODACHI/EZO-Common-T2-2B-gemma-2-it, a model based on gemma-2 and fine-tuned by Axcxept co., ltd.\"\n\n3. **フィードバック**: モデルの使用経験に関するフィードバックを歓迎します。info@axcxept.com までご連絡ください。\n\nこれらは推奨事項であり、法的要件ではありません。本モデルの使用は主に Gemma-2-2B-itをベースにしており、Gemmaの利用規約に準拠します。\n\n1. **Commercial Use**: If you plan to use this model for commercial purposes, we strongly encourage you to inform us via email at info@axcxept.com. This allows for potential collaboration on model applications and improvements.\n\n2. **Attribution**: When using or adapting this model, we recommend providing attribution as follows:\n   \"This project utilizes HODACHI/EZO-Common-T2-2B-gemma-2-it, a model based on gemma-2 and fine-tuned by Axcxept co., ltd.\"\n\n3. **Feedback**: We welcome any feedback on your experience with the model. Please feel free to email us at info@axcxept.com.\n\nPlease note that these are recommendations and not legal requirements. Your use of this model is primarily governed by the gemma License Agreement.\n\n### [Usage]\nHere are some code snippets to quickly get started with the model. First, run:\n`pip install -U transformers accelerate`\nThen, copy the snippet from the relevant section for your use case.\n\n以下に、モデルの実行を素早く開始するためのコードスニペットをいくつか紹介します。\nまず、\n`pip install -U transformers`\nを実行し、使用例に関連するセクションのスニペットをコピーしてください。\n\n### [Chat Template]\n```py\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nmodel_id = \"HODACHI/EZO-Common-T2-2B-gemma-2-it\"\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_id,\n    device_map=\"auto\",\n)\n\nmessages = [\n    {\"role\": \"user\", \"content\": f\"\"\"あなたは高度なAIです。特に指示がない限り、日本語で回答してください。\\n\\n山田太郎は、宇宙軍の曹長だった。\nこの文に現代として考えられない要素は含まれていますか？\"\"\"},\n]\ninput_ids = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_dict=True).to(\"cuda\")\n\noutputs = model.generate(**input_ids, max_new_tokens=512)\nprint(tokenizer.decode(outputs[0]))\n\n```\n\n### [Template]\n```\n<bos><start_of_turn>user\nWrite a hello world program<end_of_turn>\n<start_of_turn>model\nXXXXXX<end_of_turn><eos>\n```\n\n### [Model Data]\n#### Training Dataset]\nWe extracted high-quality data from Japanese Wikipedia and FineWeb to create instruction data. Our innovative training approach allows for performance improvements across various languages and domains, making the model suitable for global use despite its focus on Japanese data.\n\n日本語のWikiデータおよび、FineWebから良質なデータのみを抽出し、Instructionデータを作成しました。このモデルでは日本語に特化させていますが、世界中のどんなユースケースでも利用可能なアプローチです。\n\nhttps://huggingface.co/datasets/legacy-datasets/wikipedia\nhttps://huggingface.co/datasets/HuggingFaceFW/fineweb\n\n#### Data Preprocessing\nWe used a plain instruction tuning method to train the model on exemplary responses. This approach enhances the model's ability to understand and generate high-quality responses across various languages and contexts.\n\nプレインストラクトチューニング手法を用いて、模範的回答を学習させました。この手法により、モデルは様々な言語やコンテキストにおいて高品質な応答を理解し生成する能力が向上しています。\n\n#### Implementation Information\n[Pre-Instruction Training] \n\nhttps://huggingface.co/instruction-pretrain/instruction-synthesizer\n\n### [Hardware]\nA100 × 8(Running in 4h)\n\n### [We are.]\n[![Axcxept logo](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/8OKW86U986ywttvL2RcbG.png)](https://axcxept.com)\n\n\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "last_modified": "2024-08-04T02:21:52.000Z",
  "created_at": "2024-08-03T22:44:33.000Z",
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