Update README.md

README.md

@@ -1,3 +1,63 @@
 ---
-license: gemma
+license: [apache-2.0, gemma]
+datasets:
+- traintogpb/aihub-koen-translation-integrated-base-10m
+language:
+- ko
+- en
+pipeline_tag: translation
+tags:
+- gemma
 ---
+# Gemago Model Card
+
+**Original Gemma Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
+
+**Model Page On Github**: [Gemago](https://github.com/deveworld/Gemago)
+
+**Resources and Technical Documentation**:
+* [Blog(Korean)](https://blog.worldsw.dev/tag/gemago/)
+* [Original Google's Gemma-2B](https://huggingface.co/google/gemma-2b)
+* [Training Code @ Github: Gemma-EasyLM (Orginial by Beomi)](https://github.com/deveworld/Gemma-EasyLM/tree/2b)
+
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
+
+**Authors**: Orginal Google, Fine-tuned by DevWorld
+
+## Model Information
+
+Translate English/Korean to Korean/English.
+
+### Description
+
+Gemago is a lightweight English-and-Korean translation model based on Gemma.
+
+### Context Length
+Models are trained on a context length of 8192 tokens, which is equivalent to Gemma.
+
+### Usage
+
+Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U keras keras-nlp`, then copy the snippet from the section that is relevant for your usecase.
+
+#### Running the model with transformers
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deveworld/Gemago/blob/main/Gemago_2b_Infer.ipynb)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("devworld/gemago-2b")
+model = AutoModelForCausalLM.from_pretrained("devworld/gemago-2b")
+
+def gen(text, max_length):
+    input_ids = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(**input_ids, max_length=max_length)
+    return tokenizer.decode(outputs[0])
+
+def e2k(e):
+    input_text = f"English:\n{e}\n\nKorean:\n"
+    return gen(input_text, 1024)
+
+def k2e(k):
+    input_text = f"Korean:\n{k}\n\nEnglish:\n"
+    return gen(input_text, 1024)
+```