Create README.md

README.md

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+---
+language:
+- th
+- en
+pipeline_tag: text-generation
+license: llama3
+---
+**Llama-3-Typhoon-1.5X-8B-instruct: Thai Large Language Model (Instruct)**
+
+**Llama-3-Typhoon-1.5X-8B-instruct** is an 8 billion parameter instruct model designed for Thai 🇹🇭 language. It demonstrates competitive performance with GPT-3.5-turbo, and is optimized for **production** environments, **Retrieval-Augmented Generation (RAG), constrained generation**, and **reasoning** tasks.
+
+Built on Typhoon 1.5 8B and Llama 3 8B 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**: An 8B instruct decoder-only model based on the Llama architecture.
+- **Requirement**: Transformers 4.38.0 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 our beta users' feedback, focusing on two factors:
+        - **Human Alignment & Reasoning**: Ability to generate responses that are understandable and reasoned across multiple steps.
+            - Evaluated using [MT-Bench](https://arxiv.org/abs/2306.05685) — How LLMs can answer embedded knowledge to align with human needs.
+        - **Instruction-following**: Ability to adhere to specified constraints in the instruction
+            - Evaluated using [IFEval](https://arxiv.org/abs/2311.07911) — How LLMs can follow specified constraints, such as formatting and brevity.
+
+Remark: We developed the TH pair by translating the original datasets into Thai and conducting a human verification on them.
+
+### ThaiExam
+
+| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | MMLU |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| Typhoon-1.5 8B | 0.446 | 0.431 | 0.722 | 0.526 | 0.407 | 0.5028 | 0.6136 |
+| Typhoon-1.5X 8B | 0.478 | 0.379 | 0.722 | 0.5 | 0.435 | 0.5028 | 0.6369 |
+| gpt-3.5-turbo-0125 | 0.358 | 0.279 | 0.678 | 0.345 | 0.318 | 0.3956 | 0.700** |
+
+### MT-Bench
+
+| Model | MT-Bench Thai | MT-Bench English |
+| --- | --- | --- |
+| Typhoon-1.5 8B | 6.402 | 7.275 |
+| Typhoon-1.5X 8B | 6.902 | 7.9 |
+| gpt-3.5-turbo-0125 | 6.186 | 8.181 |
+
+### IFEval
+
+| Model | IFEval Thai | IFEval English |
+| --- | --- | --- |
+| Typhoon-1.5 8B | 0.548 | 0.676 |
+| Typhoon-1.5X 8B | 0.548 | 0.691 |
+| gpt-3.5-turbo-0125 | 0.479 | 0.659 |
+
+## Insight
+
+Utilized model editing technique. We found that the most critical feature for generating Thai answers is located in the backend (the upper layers of the transformer block). Accordingly, we incorporated a high ratio of Typhoon in these backend layers.
+
+## **Usage Example**
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "scb10x/llama-3-typhoon-v1.5x-8b-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [...] # add message here
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.4,
+    top_p=0.95,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```
+
+## **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, 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)