jojo-ai-mst
/

thai-opt350m-instruct

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- thai
+- opt
+- generative ai
+- SEA
+- southeast-asian
+license: mit
+language:
+- th
 ---
+![thai-opt350m-instruct-logo](thai-opt350m.png)
+# thai-opt350m-instruct
+ a generative language model for thai language based on opt350m
+![Thai Open Pretrained Transformer 350M - Generative](/thai-opt350m.png)
+**Thai-OPT350M-Instruct** is a fine-tuned pretrained transformer for **thai language** based on facebook/opt-350m.
+Dataset for thai-opt350m-instruct
+- yadapruk/thai-instructions-rallio
+## Base Model
+- Facebook Open Pretrained Transformer
+## Languages
+- mainly support Thai Language
+- a few English, Chinese, Arabic
+## Training
+- epochs - 12
+- training loss - 0.809200
+# Model Page
+[https://huggingface.co/jojo-ai-mst/thai-opt350m-instruct](https://huggingface.co/jojo-ai-mst/thai-opt350m-instruct)
+## Prompt Format
+```
+User: อะไรคือวิธีที่ดีที่สุดในการทําความสะอาดพรม Assistant:
+```
+# How to use
+```python
+model = AutoModelForCausalLM.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")
+tokenizer = AutoTokenizer.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")
+def generate_text(prompt, max_length=200, temperature=0.8, top_k=50):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").cuda() # remove .cuda() if only cpu
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        temperature=temperature,
+        top_k=top_k,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True
+    )
+    for result in output:
+      generated_text = tokenizer.decode(result, skip_special_tokens=True)
+      print(generated_text)
+generate_text("User: อะไรคือวิธีที่ดีที่สุดในการทําความสะอาดพรม Assistant:")
+```
+# Date of Release
+22/03/2024
+# License
+MIT
+# Author
+[Min Si Thu](https://www.linkedin.com/in/min-si-thu/)
+# Notes
+This ai model is a movement of [MyanmarGPT-Movement](https://github.com/MyanmarGPT-Movement).