初始化项目，由ModelHub XC社区提供模型
Model: typhoon-ai/llama-3-typhoon-v1.5-8b-instruct Source: Original Platform
This commit is contained in:
ModelHub XC
120
README.md
Normal file
120
README.md
Normal file
@@ -0,0 +1,120 @@
|
||||
---
|
||||
license: llama3
|
||||
language:
|
||||
- en
|
||||
- th
|
||||
pipeline_tag: text-generation
|
||||
tags:
|
||||
- instruct
|
||||
- chat
|
||||
---
|
||||
**Llama-3-Typhoon-1.5-8B: Thai Large Language Model (Instruct)**
|
||||
|
||||
**Llama-3-Typhoon-1.5-8B-instruct** is a *instruct* Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B.
|
||||
|
||||
|
||||
|
||||
For release post, please see our [blog](https://blog.opentyphoon.ai/typhoon-1-5-release-a9364cb8e8d7).
|
||||
*To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name.
|
||||
|
||||
## **Model Description**
|
||||
|
||||
- **Model type**: A 8B instruct decoder-only model based on 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**
|
||||
|
||||
| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam | MMLU |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| Typhoon-1.0 (Mistral) | 0.379 | 0.393 | 0.700 | 0.414 | 0.324 | 0.442 | 0.391 | 0.547 |
|
||||
| Typhoon-1.5 8B (Llama3) | ***0.446*** | ***0.431*** | ***0.722*** | ***0.526*** | ***0.407*** | ***0.506*** | ***0.460*** | ***0.614*** |
|
||||
| Sailor 7B | 0.372 | 0.379 | 0.678 | 0.405 | 0.396 | 0.446 | 0.411 | 0.553 |
|
||||
| SeaLLM 2.0 7B | 0.327 | 0.311 | 0.656 | 0.414 | 0.321 | 0.406 | 0.354 | 0.579 |
|
||||
| OpenThaiGPT 1.0.0 7B | 0.238 | 0.249 | 0.444 | 0.319 | 0.289 | 0.308 | 0.268 | 0.369 |
|
||||
| SambaLingo-Thai-Chat 7B | 0.251 | 0.241 | 0.522 | 0.302 | 0.262 | 0.316 | 0.309 | 0.388 |
|
||||
|
||||
|
||||
## Usage Example
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
|
||||
model_id = "scb10x/llama-3-typhoon-v1.5-8b-instruct"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto",
|
||||
)
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "You are a helpful assistant who're always speak Thai."},
|
||||
{"role": "user", "content": "ขอสูตรไก่ย่าง"},
|
||||
]
|
||||
|
||||
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=0.9,
|
||||
)
|
||||
response = outputs[0][input_ids.shape[-1]:]
|
||||
print(tokenizer.decode(response, skip_special_tokens=True))
|
||||
```
|
||||
|
||||
## Chat Template
|
||||
|
||||
We use llama3 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 an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
|
||||
|
||||
## **Follow us**
|
||||
|
||||
**https://twitter.com/opentyphoon**
|
||||
|
||||
## **Support**
|
||||
|
||||
**https://discord.gg/us5gAYmrxw**
|
||||
|
||||
## **SCB10X AI Team**
|
||||
|
||||
- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
|
||||
- If you find Typhoon-8B 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)**
|
||||
Reference in New Issue
Block a user