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