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Model: Tsunami-th/Tsunami-1.0-14B-Instruct
Source: Original Platform

README.md

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+---
+language:
+- th
+- en
+license: apache-2.0
+library_name: transformers
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+- Qwen/Qwen2.5-14B
+pipeline_tag: text-generation
+---
+<img src="./Tsunami.webp" alt="Tsunami Model" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+# Tsunami-1.0-14B-Instruct
+**TSUNAMI**: Transformative Semantic Understanding and Natural Augmentation Model for Intelligence.
+
+**TSUNAMI** full name was created by ChatGPT.
+
+---
+
+### infomation
+**Tsunami-1.0-14B-Instruct** is Thai Large Language Model that fine-tuned from **Qwen2.5-14B** in Thai dataset.
+
+---
+
+### Author
+ - Pollakrit Lorprasertkul | game.pollakrit@gmail.com
+
+---
+
+### Performance Evaluation
+
+Below are the benchmark results of **Tsunami-1.0-14B-Instruct** compared to similar models in its class:
+
+| Model | Average | Thai Exam | M3Exam |
+| --- | --- | --- | --- |
+| Qwen2.5-14B-Instruct | 58.45 | 57.35 | 59.55 |
+| Meta-Llama-3.1-70B-Instruct | 59.38 | 58.23 | 60.52 |
+| llama-3-typhoon-v1.5x-70b-instruct | 59.34 | 58.76 | 59.92 |
+| openthaigpt1.5-14b-instruct | 60.41 | 58.41 | 62.41 |
+| **Tsunami-1.0-14B-Instruct** | **62.05** | **61.06** | **63.05** |
+
+---
+
+### Prompt Template
+
+This model uses `ChatML` prompt template:
+
+```
+<|im_start|>system
+{System}<|im_end|>
+<|im_start|>user
+{User}<|im_end|>
+<|im_start|>assistant
+{Assistant}
+````
+
+---
+
+### How to use
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_name = "Tsunami-th/Tsunami-1.0-14B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "สวัสดีครับ"}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+inputs = tokenizer(text, return_tensors="pt")
+inputs = inputs.to(model.device)
+with torch.no_grad():
+   output = model.generate(**inputs, max_new_tokens=512)
+response = tokenizer.decode(output[0, len(inputs['input_ids'][0]):], skip_special_tokens=True)
+```
+
+---