AXCXEPT
/

EZO-Humanities-9B-gemma-2-it

@@ -1,199 +1,114 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+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
 ---
+# EZO model card
+**Resources and Technical Documentation**:
+* [Responsible Generative AI Toolkit][rai-toolkit]
+* [Gemma on Kaggle][kaggle-gemma]
+* [Gemma on Vertex Model Garden][vertex-mg-gemma]
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)
+**Authors**: Axcxept co., ltd.
+## Model Information
+### Description
+Gemma-2-9B-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、特にHumanitiesに特化させたモデルです。
+### Benchmark Results
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/pFHVX7j4nd__jKdhari8w.png)
+### Usage
+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。
+まず、
+`pip install -U transformers`
+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。
+### Chat Template
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+model_id = "google/gemma-2-9b-it"
+dtype = torch.bfloat16
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype=dtype,)
+chat = [
+    { "role": "user", "content": "Write a hello world program" },
+]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
+print(tokenizer.decode(outputs[0]))
+```
+### Template
+```
+<bos><start_of_turn>user
+Write a hello world program<end_of_turn>
+<start_of_turn>model
+XXXXXX<end_of_turn><eos>
+```
+### Citation
+```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}
+}
+```
+## Model Data
+Data used for model training and how the data was processed.
+### Training Dataset
+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。
+https://huggingface.co/datasets/legacy-datasets/wikipedia
+https://huggingface.co/datasets/HuggingFaceFW/fineweb
+### Data Preprocessing
+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。
+## Implementation Information
+[Pre-Instruction Training]
+https://huggingface.co/instruction-pretrain/instruction-synthesizer
+### Hardware
+A100 × 4 × 32H