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Model: typhoon-ai/typhoon2-qwen2.5-7b-instruct Source: Original Platform
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---
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pipeline_tag: text-generation
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license: apache-2.0
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---
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**Typhoon2-Qwen2.5-7B-Instruct**: Thai Large Language Model (Instruct)
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**Typhoon2-Qwen2.5-7B-Instruct** is a instruct Thai 🇹🇭 large language model with 7 billion parameters, and it is based on Qwen2.5-7B.
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For technical-report. please see our [arxiv](https://arxiv.org/abs/2412.13702).
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## **Performance**
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**Instruction-Following & Function Call Performance**
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<div align="center">
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<img src="https://storage.googleapis.com/typhoon-public/assets/typhoon2-text/qwen7b_general.png" alt="Typhoon2 Qwen 7B General Performance" width="100%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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</div>
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**Specific Domain Performance (Math & Coding)**
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<div align="center">
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<img src="https://storage.googleapis.com/typhoon-public/assets/typhoon2-text/qwen7b_specific.png" alt="TTyphoon2 Qwen 7B Specific Domain Performance" width="100%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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</div>
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**Long Context Performance**
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<div align="center">
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<img src="https://storage.googleapis.com/typhoon-public/assets/typhoon2-text/qwen7b_long.jpg" alt="Typhoon2 Qwen 7B Long Context Performance" width="100%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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</div>
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**Detail Performance**
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| Model | IFEval - TH | IFEval - EN | MT-Bench TH | MT-Bench EN | Thai Code-Switching(t=0.7) | Thai Code-Switching(t=1.0) | FC-TH | FC-EN | GSM8K-TH | GSM8K-EN | MATH-TH | MATH-EN | HumanEval-TH | HumanEval-EN | MBPP-TH | MBPP-EN |
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|----------------------------|-------------|-------------|-------------|-------------|--------------------------------|--------------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-------------|-------------|-----------|-----------|
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| **Typhoon2 Qwen2.5 7B Instruct** | **74.37%** | 73.34% | **6.186** | 8.094 | **99.2%** | **96.8%** | **74.24%** | **75.44%** | **79.07%** | **84.2%** | **55.42%** | 66.42% | 73.2% | 79.3% | 78.3% | **81.7%** |
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| **Qwen2.5 7B Instruct** | 68.47% | **76.82%** | 6.0 | **8.537** | 85.8% | 20.4% | 66.06% | 74.81% | 47.53% | 81.0% | 17.41% | **73.4%** | **77.4%** | **81.1%** | **80.4%** | 79.6% |
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| **Openthaigpt 1.5 7B** | 67.38% | 75.47% | 5.692 | 8.106 | 93.8% | 28% | 65.53% | 73.10% | 65.73% | 68.0% | 24.44% | 69.68% | 71.3% | 78.7% | 77.5% | 79.1% |
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## **Model Description**
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- **Model type**: A 7B instruct decoder-only model based on Qwen2 architecture.
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- **Requirement**: transformers 4.45.0 or newer.
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- **Context length**: 128k - Please refer to this section[] for detailed instructions on how to deploy Typhoon2-Qwen2.5 for handling long texts.
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- **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧
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- **License**: Apache-2.0
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## Usage Example
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "scb10x/typhoon2-qwen2.5-7b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"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."},
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{"role": "user", "content": "ขอสูตรไก่ย่าง"},
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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)
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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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## Inference Server Hosting Example
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```bash
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pip install vllm
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vllm serve scb10x/typhoon2-qwen2.5-7b-instruct
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# see more information at https://docs.vllm.ai/
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```
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## Function-Call Example
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import ast
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model_name = "scb10x/typhoon2-qwen2.5-7b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name, torch_dtype=torch.bfloat16, device_map="auto",
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)
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get_weather_api = {
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"name": "get_weather",
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"description": "Get the current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, New York",
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The unit of temperature to return",
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},
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},
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"required": ["location"],
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},
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}
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search_api = {
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"name": "search",
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"description": "Search for information on the internet",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The search query, e.g. 'latest news on AI'",
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}
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},
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"required": ["query"],
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},
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}
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get_stock = {
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"name": "get_stock_price",
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"description": "Get the stock price",
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"parameters": {
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"type": "object",
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"properties": {
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"symbol": {
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"type": "string",
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"description": "The stock symbol, e.g. AAPL, GOOG",
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}
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},
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"required": ["symbol"],
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},
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}
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# Tool input are same format with OpenAI tools
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openai_format_tools = [get_weather_api, search_api, get_stock]
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messages = [
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{"role": "system", "content": "You are an expert in composing functions."},
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{"role": "user", "content": "ขอราคาหุ้น Tasla (TLS) และ Amazon (AMZ) ?"},
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]
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inputs = tokenizer.apply_chat_template(
|
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messages, tools=openai_format_tools, add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.7,
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num_return_sequences=1,
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)
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response = outputs[0][inputs.shape[-1]:]
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print("Here Output:", tokenizer.decode(response, skip_special_tokens=True))
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# Decoding function utility
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def resolve_ast_by_type(value):
|
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if isinstance(value, ast.Constant):
|
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if value.value is Ellipsis:
|
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output = "..."
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else:
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output = value.value
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elif isinstance(value, ast.UnaryOp):
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output = -value.operand.value
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elif isinstance(value, ast.List):
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output = [resolve_ast_by_type(v) for v in value.elts]
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elif isinstance(value, ast.Dict):
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output = {
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resolve_ast_by_type(k): resolve_ast_by_type(v)
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for k, v in zip(value.keys, value.values)
|
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}
|
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elif isinstance(
|
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value, ast.NameConstant
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): # Added this condition to handle boolean values
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output = value.value
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elif isinstance(
|
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value, ast.BinOp
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): # Added this condition to handle function calls as arguments
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output = eval(ast.unparse(value))
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elif isinstance(value, ast.Name):
|
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output = value.id
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elif isinstance(value, ast.Call):
|
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if len(value.keywords) == 0:
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output = ast.unparse(value)
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else:
|
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output = resolve_ast_call(value)
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elif isinstance(value, ast.Tuple):
|
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output = tuple(resolve_ast_by_type(v) for v in value.elts)
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elif isinstance(value, ast.Lambda):
|
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output = eval(ast.unparse(value.body[0].value))
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elif isinstance(value, ast.Ellipsis):
|
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output = "..."
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elif isinstance(value, ast.Subscript):
|
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try:
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output = ast.unparse(value.body[0].value)
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except:
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output = ast.unparse(value.value) + "[" + ast.unparse(value.slice) + "]"
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else:
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raise Exception(f"Unsupported AST type: {type(value)}")
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return output
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def resolve_ast_call(elem):
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func_parts = []
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func_part = elem.func
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while isinstance(func_part, ast.Attribute):
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func_parts.append(func_part.attr)
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func_part = func_part.value
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if isinstance(func_part, ast.Name):
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func_parts.append(func_part.id)
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func_name = ".".join(reversed(func_parts))
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args_dict = {}
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for arg in elem.keywords:
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output = resolve_ast_by_type(arg.value)
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args_dict[arg.arg] = output
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return {func_name: args_dict}
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def ast_parse(input_str, language="Python"):
|
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if language == "Python":
|
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cleaned_input = input_str.strip("[]'")
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parsed = ast.parse(cleaned_input, mode="eval")
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extracted = []
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if isinstance(parsed.body, ast.Call):
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extracted.append(resolve_ast_call(parsed.body))
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else:
|
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for elem in parsed.body.elts:
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assert isinstance(elem, ast.Call)
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extracted.append(resolve_ast_call(elem))
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return extracted
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else:
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raise NotImplementedError(f"Unsupported language: {language}")
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|
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|
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def parse_nested_value(value):
|
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"""
|
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Parse a potentially nested value from the AST output.
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|
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Args:
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value: The value to parse, which could be a nested dictionary, which includes another function call, or a simple value.
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Returns:
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str: A string representation of the value, handling nested function calls and nested dictionary function arguments.
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"""
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if isinstance(value, dict):
|
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# Check if the dictionary represents a function call (i.e., the value is another dictionary or complex structure)
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if all(isinstance(v, dict) for v in value.values()):
|
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func_name = list(value.keys())[0]
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args = value[func_name]
|
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args_str = ", ".join(
|
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f"{k}={parse_nested_value(v)}" for k, v in args.items()
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)
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return f"{func_name}({args_str})"
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else:
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# If it's a simple dictionary, treat it as key-value pairs
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return (
|
||||
"{"
|
||||
+ ", ".join(f"'{k}': {parse_nested_value(v)}" for k, v in value.items())
|
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+ "}"
|
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)
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return repr(value)
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|
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def default_decode_ast_prompting(result, language="Python"):
|
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result = result.strip("`\n ")
|
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if not result.startswith("["):
|
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result = "[" + result
|
||||
if not result.endswith("]"):
|
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result = result + "]"
|
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decoded_output = ast_parse(result, language)
|
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return decoded_output
|
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|
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|
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fc_result = default_decode_ast_prompting(tokenizer.decode(response, skip_special_tokens=True))
|
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print(fc_result) # [{'Function': {'arguments': '{"symbol": "TLS"}', 'name': 'get_stock_price'}}, {'Function': {'arguments': '{"symbol": "AMZ"}', 'name': 'get_stock_price'}}]
|
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```
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|
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## Processing Long Texts
|
||||
|
||||
The current `config.json` is set for context length up to 32,768 tokens.
|
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To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
|
||||
|
||||
For supported frameworks, you could add the following to `config.json` to enable YaRN:
|
||||
```json
|
||||
{
|
||||
...,
|
||||
"rope_scaling": {
|
||||
"factor": 4.0,
|
||||
"original_max_position_embeddings": 32768,
|
||||
"type": "yarn"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
For deployment, we recommend using vLLM.
|
||||
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
|
||||
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
|
||||
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
|
||||
|
||||
## **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**
|
||||
|
||||
## **Citation**
|
||||
|
||||
- If you find Typhoon2 useful for your work, please cite it using:
|
||||
```
|
||||
@misc{typhoon2,
|
||||
title={Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models},
|
||||
author={Kunat Pipatanakul and Potsawee Manakul and Natapong Nitarach and Warit Sirichotedumrong and Surapon Nonesung and Teetouch Jaknamon and Parinthapat Pengpun and Pittawat Taveekitworachai and Adisai Na-Thalang and Sittipong Sripaisarnmongkol and Krisanapong Jirayoot and Kasima Tharnpipitchai},
|
||||
year={2024},
|
||||
eprint={2412.13702},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CL},
|
||||
url={https://arxiv.org/abs/2412.13702},
|
||||
}
|
||||
```
|
||||
25
added_tokens.json
Normal file
25
added_tokens.json
Normal file
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"</tool_call>": 151658,
|
||||
"<tool_call>": 151657,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|eot_id|>": 151665,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
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31
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31
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Normal file
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3
tokenizer.json
Normal file
3
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215
tokenizer_config.json
Normal file
215
tokenizer_config.json
Normal file
@@ -0,0 +1,215 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Typhoon, created by SCB 10X. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\" }}\n {{- \"You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose.\" }}\n {{- \"If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out.\" }}\n {{- \"You should only return the function call in tools call sections.\" }}\n {{- \"If you decide to invoke any of the function(s), you MUST put it in the format of [Function(arguments1={{params_name1: params_value1,params_name2: params_value2, ...}}, name1=function_name1), Function(arguments2={{params}}, name2=function_name2) , ...]\"}}\n {{- \"You SHOULD NOT include any other text in the response.\\nHere is a list of functions in JSON format that you can invoke.\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Typhoon, created by SCB 10X. You are a helpful assistant. Typhoon follows this information in all languages, and always responds to the user in the language they use or request.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|eot_id|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user