Update README.md

README.md

@@ -2,57 +2,25 @@
 license: apache-2.0
 language:
 - en
+- th
 pipeline_tag: text-generation
+datasets:
+- wannaphong/mark13
 ---
 
-# NumFa 3B 1 epoch-test
+# NumFaLM 3B
 
-NumFa 3B 1 epoch is the first epoch of NumFa 3B model.
+NumFaLM 3B is a bilingual language model trained on Thai and English. The architecture model is Llama model that pretraining from scratch. It was build to open source AI and research for bilingual language model and improve small language model. We released the training srcipt and train datasets to you can research the training and datasets.
 
-Base model: openllama3b
 
-**For testing only**
+- Training script: [https://github.com/wannaphong/EasyLM/tree/numfa_pretraining](https://github.com/wannaphong/EasyLM/tree/numfa_pretraining)
+- Train Datasets: [wannaphong/mark13](https://huggingface.co/datasets/wannaphong/mark13)
 
-## Model Details
 
-### Model Description
+We fork EasyLM and add training by HuggingFace datasets but HuggingFace was down many time in during the time we train the model, so we can trained just 1 epoch. The model trained 1 epoch.
 
-The model was trained by TPU.
+# Acknowledgements
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). We use TPU4-64 for training model about 4 days / 1 epoch.
 
-- **Developed by:** NumFa
-- **Model type:** text-generation
-- **Language(s) (NLP):** English
-- **License:** apache-2.0
-
-
-### Out-of-Scope Use
-
-Math, Coding, and other language
-
-
-## Bias, Risks, and Limitations
-
-The model can has a bias from dataset. Use at your own risks!
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-**Example**
-
-1.
-
-```python
-# !pip install accelerate sentencepiece transformers bitsandbytes
-import torch
-from transformers import pipeline
-
-pipe = pipeline("text-generation", model="numfa_3b_1epoch", torch_dtype=torch.bfloat16, device_map="auto")
-
-# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
-
-outputs = pipe("test is", max_new_tokens=300, do_sample=True, temperature=0.9, top_k=50, top_p=0.95, no_repeat_ngram_size=2,typical_p=1.)
-print(outputs[0]["generated_text"])
-```
+Thank you [TPU Research Cloud](https://sites.research.google/trc/about/) and [EasyLM project](https://github.com/young-geng/EasyLM)! We use EasyLM for pretraining model.