---
language:
- th
- en
pipeline_tag: text-generation
license: llama3
---
**Llama-3-Typhoon-1.5X-70B-instruct-awq: Thai Large Language Model (Instruct) - AWQ 4bit quantized**

**Llama-3-Typhoon-1.5X-70B-instruct** is a 70 billion parameter instruct model designed for Thai 🇹🇭 language. It demonstrates competitive performance with GPT-4-0612, and is optimized for **application** use cases, **Retrieval-Augmented Generation (RAG), constrained generation**, and **reasoning** tasks.

Built on Typhoon 1.5 70B (not yet released) and Llama 3 70B Instruct. this model is a result of our experiment on **cross-lingual transfer**. It utilizes the [task-arithmetic model editing](https://arxiv.org/abs/2212.04089) technique, combining the Thai understanding capability of Typhoon with the human alignment performance of Llama 3 Instruct.

Remark: To acknowledge Meta's efforts in creating the foundation model and comply with the license, we explicitly include "llama-3" in the model name.

## **Model Description**

- **Model type**: A 70B instruct decoder-only model based on the Llama architecture
- **Requirement**: vllm (https://pypi.org/project/vllm/) 0.3.2 or newer.
- **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧
- **License**: [**Llama 3 Community License**](https://llama.meta.com/llama3/license/)

## **Performance**

We evaluated the model's performance in **Language & Knowledge Capabilities** and **Instruction Following Capabilities**.

- **Language & Knowledge Capabilities**:
    - Assessed using multiple-choice question-answering datasets such as ThaiExam and MMLU.
- **Instruction Following Capabilities**:
    - Evaluated based on beta users' feedback, focusing on two factors:
        - **Human Alignment & Reasoning**: Ability to generate responses that are clear and logically structured across multiple steps.
            - Evaluated using [MT-Bench](https://arxiv.org/abs/2306.05685) — How LLMs can align with human needs.
        - **Instruction-following**: Ability to adhere to specified constraints in the instructions.
            - Evaluated using [IFEval](https://arxiv.org/abs/2311.07911) — How LLMs can follow specified constraints, such as formatting and brevity.
- **Agentic Capabilities**:
    - Evaluated in agent use-cases using [Hugging Face's Transformer Agents](https://huggingface.co/blog/agents) and the associated [benchmark](https://huggingface.co/blog/open-source-llms-as-agents).

Remark: We developed the Thai (TH) pairs by translating the original datasets into Thai through machine and human methods.

### ThaiExam

| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | MMLU |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Typhoon-1.5X 70B | **0.565** | 0.68 | **0.778** | **0.517** | 0.56 | **0.620** | 0.7945 |
| gpt-4-0612 | 0.493 | **0.69** | 0.744 | 0.509 | **0.616** | 0.610 | **0.864**** |
| --- | --- | --- | --- | --- | --- | --- | --- |
| gpt-4o | 0.62 | 0.63 | 0.789 | 0.56 | 0.623 | 0.644 | 0.887** |

** We report the MMLU score that is reported in [GPT-4o Tech Report](https://openai.com/index/hello-gpt-4o/). 

### MT-Bench

| Model | MT-Bench Thai | MT-Bench English |
| --- | --- | --- |
| Typhoon-1.5X 70B | **8.029** | **8.797** |
| gpt-4-0612 | 7.801 | 8.671 |
| --- | --- | --- |
| gpt-4o | 8.514 | 9.184 |

### IFEval

| Model | IFEval Thai | IFEval English |
| --- | --- | --- |
| Typhoon-1.5X 70B | **0.645** | **0.810** |
| gpt-4-0612 | 0.612 | 0.793* |
| --- | --- | --- |
| gpt-4o | 0.737 | 0.871 |

* We report the number from IFEval paper.

### Agent

| Model | GAIA - Thai/English | GSM8K - Thai/English | HotpotQA - Thai/English |
| --- | --- | --- | --- |
| gpt-3.5-turbo-0125 | **18.42**/37.5 | 70/80 | 39.56/59 |
| Typhoon-1.5X 70B | 17.10/36.25 | 80/95 | 52.7/65.83 |
| gpt-4-0612 | 17.10/**38.75** | **90**/**100** | **56.41**/**76.25** |
| --- | --- | --- | --- |
| gpt-4o | 44.73/57.5 | 100/100 | 71.64/76.58 |

## Insight

We utilized **model editing** techniques and found that the most critical feature for generating accurate Thai answers is located in the backend (the upper layers of the transformer block). Accordingly, we incorporated a high ratio of Typhoon components in these backend layers to enhance our model’s performance.

## **Usage Example**

```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
quant_path = "scb10x/llama-3-typhoon-v1.5x-70b-instruct-awq"
llm = LLM(model=quant_path, quantization='awq', max_model_len=8192)
tokenizer = AutoTokenizer.from_pretrained(quant_path)

messages = [
    // messages here
]
prompts = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False
)
sampling_params = SamplingParams(repetition_penalty=1.05, top_p=0.6, temperature=0.9, max_tokens=1024, stop=['<|eot_id|>', '<|start_header_id|>', '<|end_header_id|>'])
outputs = llm.generate(prompts, sampling_params=sampling_params)
print(outputs[0].outputs)
```

## **Chat Template**

We use the Llama 3 chat template.

```python
{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}
```

## **Intended Uses & Limitations**

This model is experimental and might not be fully evaluated for all use cases. Developers should assess risks in the context of their specific applications.

## **Follow us**

[**https://twitter.com/opentyphoon**](https://twitter.com/opentyphoon)

## **Support**

[**https://discord.gg/CqyBscMFpg**](https://discord.gg/CqyBscMFpg)

## **SCB 10X Typhoon Team**

- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
- If you find Typhoon-1.5X useful for your work, please cite it using:

```
@article{pipatanakul2023typhoon,
    title={Typhoon: Thai Large Language Models}, 
    author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
    year={2023},
    journal={arXiv preprint arXiv:2312.13951},
    url={https://arxiv.org/abs/2312.13951}
}
```

## **Contact Us**

- General & Collaboration: [**kasima@scb10x.com**](mailto:kasima@scb10x.com), [**pathomporn@scb10x.com**](mailto:pathomporn@scb10x.com)
- Technical: [**kunat@scb10x.com**](mailto:kunat@scb10x.com)