README.md

---
language:
- th
- en
library_name: transformers
base_model:
- Qwen/Qwen2.5-7B-Instruct
- Qwen/Qwen2.5-7B
pipeline_tag: text-generation
model-index:
- name: Tsunami-0.5-7B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 74
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 36.14
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.15
      name: exact match
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 7.83
      name: acc_norm
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 12.21
      name: acc_norm
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.92
      name: accuracy
    source:
      url: >-
        https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5-7B-Instruct
      name: Open LLM Leaderboard
license: apache-2.0
---

<img src="./Tsunami.webp" alt="Tsunami Model" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

# Tsunami-0.5-7B-Instruct
**TSUNAMI**: Transformative Semantic Understanding and Natural Augmentation Model for Intelligence.

**TSUNAMI** full name was created by ChatGPT.

---

### infomation
**Tsunami-0.5-7B-Instruct** is Thai Large Language Model that fine-tuned from **Qwen2.5-7B** around **60,000** rows in Thai-specific domain.

---

### 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-0.5-7B-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)
```

---

### Author
 - Pollakrit Lorprasertkul | [email protected]

---

 - **Tsunami-0.5-7B-Instruct** is the version 0.5 that did not train on the whole dataset.
 - **Tsunami-1.0-7B-Instruct** is coming soon.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_Tsunami-th__Tsunami-0.5-7B-Instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |28.04|
|IFEval (0-Shot)    |74.00|
|BBH (3-Shot)       |36.14|
|MATH Lvl 5 (4-Shot)| 0.15|
|GPQA (0-shot)      | 7.83|
|MuSR (0-shot)      |12.21|
|MMLU-PRO (5-shot)  |37.92|