llama3.1-typhoon2

Llama3.1-Typhoon2-8B: Thai Large Language Model (Instruct)

Llama3.1-Typhoon2-8B-instruct is a instruct Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3.1-8B.

For technical-report. please see our arxiv. *To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3.1" in the model name.

Performance

Instruction-Following & Function Call Performance

Specific Domain Performance (Math & Coding)

Long Context Performance

Detail Performance

Model Description

• Model type: A 8B instruct decoder-only model based on Llama architecture.
• Requirement: transformers 4.45.0 or newer.
• Context length: 90k
• Primary Language(s): Thai 🇹🇭 and English 🇬🇧
• License: Llama 3.1 Community License

Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "scb10x/llama3.1-typhoon2-8b-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 male AI assistant named Typhoon created by SCB 10X to be helpful, harmless, and honest. Typhoon is happy to help with analysis, question answering, math, coding, creative writing, teaching, role-play, general discussion, and all sorts of other tasks. Typhoon responds directly to all human messages without unnecessary affirmations or filler phrases like “Certainly!”, “Of course!”, “Absolutely!”, “Great!”, “Sure!”, etc. Specifically, Typhoon avoids starting responses with the word “Certainly” in any way. Typhoon follows this information in all languages, and always responds to the user in the language they use or request. Typhoon is now being connected with a human. Write in fluid, conversational prose, Show genuine interest in understanding requests, Express appropriate emotions and empathy. Also showing information in term that is easy to understand and visualized."},
    {"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("")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Inference Server Hosting Example

pip install vllm
vllm serve scb10x/llama3.1-typhoon2-8b-instruct
# see more information at https://docs.vllm.ai/

Function-Call Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import ast
model_name = "scb10x/llama3.1-typhoon2-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, torch_dtype=torch.bfloat16, device_map='auto'
)

get_weather_api = {
    "name": "get_weather",
    "description": "Get the current weather for a location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, New York",
            },
            "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "The unit of temperature to return",
            },
        },
        "required": ["location"],
    },
}

search_api = {
    "name": "search",
    "description": "Search for information on the internet",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The search query, e.g. 'latest news on AI'",
            }
        },
        "required": ["query"],
    },
}

get_stock = {
    "name": "get_stock_price",
    "description": "Get the stock price",
    "parameters": {
        "type": "object",
        "properties": {
            "symbol": {
                "type": "string",
                "description": "The stock symbol, e.g. AAPL, GOOG",
            }
        },
        "required": ["symbol"],
    },
}
# Tool input are same format with OpenAI tools
openai_format_tools = [get_weather_api, search_api, get_stock]

messages = [
    {"role": "system", "content": "You are an expert in composing functions."},
    {"role": "user", "content": "ขอราคาหุ้น Tasla (TLS) และ Amazon (AMZ) ?"},
]

inputs =