Instructions to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lianghsun/Llama-3.2-Taiwan-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lianghsun/Llama-3.2-Taiwan-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("lianghsun/Llama-3.2-Taiwan-3B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lianghsun/Llama-3.2-Taiwan-3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lianghsun/Llama-3.2-Taiwan-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct

SGLang

How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lianghsun/Llama-3.2-Taiwan-3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lianghsun/Llama-3.2-Taiwan-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lianghsun/Llama-3.2-Taiwan-3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lianghsun/Llama-3.2-Taiwan-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lianghsun/Llama-3.2-Taiwan-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct
```

lianghsun commited on May 4

Commit

3dce296

verified ·

1 Parent(s): cc8d33f

revert: restore previous Llama-3.2-Taiwan-3B-Instruct model card

Browse files

Files changed (1) hide show

README.md +6 -4

README.md CHANGED Viewed

@@ -981,13 +981,15 @@ metrics:
 - accuracy
 ---
-# Model Card for Llama-3.2-Taiwan-3B-Instruct
 <!-- Provide a quick summary of what the model is/does. -->
-**Llama-3.2-Taiwan-3B-Instruct** 採用 [lianghsun/Llama-3.2-Taiwan-3B](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) 為[基礎模型（foundation model）](https://en.wikipedia.org/wiki/Foundation_model)，使用大量繁體中文對話集和多國語言對話集進行模型[指令微調（instruction fine-tuning）](https://www.ibm.com/topics/instruction-tuning)和多輪迭代[直接偏好優化（direct preference optimization, DPO）](https://arxiv.org/abs/2305.18290)，旨在訓練出具有中華民國台灣知識及風格的[小語言模型（small language model, SLM）](https://www.ibm.com/think/topics/small-language-models)之對話模型。
-> ⚠️ 規格重點：
-> 本模型為 **3B 參數**、純文本單模態，已完成指令微調並做多輪 DPO，可直接對話使用。
 <details>
   <summary><b>Model Change Log</b></summary>

 - accuracy
 ---
+# Model Card for lianghsun/Llama-3.2-Taiwan-3B-Instruct
 <!-- Provide a quick summary of what the model is/does. -->
+<a href="https://discord.gg/fj6WbHMvfs" target="_blank">[👋 歡迎加入 Discord 討論 🎉]</a>
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/618dc56cbc345ca7bf95f3cd/v_cfMxTtVE6_eh0rzcy5L.png)
+*圖像生成來自 [OpenArt](https://openart.ai/home)：An anime-style 🦙 standing proudly atop the summit of Taiwan’s [Yushan (Jade Mountain)](https://zh.wikipedia.org/wiki/%E7%8E%89%E5%B1%B1), gazing forward.*
+採用 [lianghsun/Llama-3.2-Taiwan-3B](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) 為[基礎模型（foundation model）](https://en.wikipedia.org/wiki/Foundation_model)，使用大量[中華民國台灣](https://zh.wikipedia.org/zh-tw/%E8%87%BA%E7%81%A3)的繁體中文對話集和多國語言對話集進行模型[指令微調（instruction fine-tuning）](https://www.ibm.com/topics/instruction-tuning)和多輪迭代[直接偏好優化（direct preference optimization, DPO）](https://arxiv.org/abs/2305.18290)，旨在訓練出具有中華民國台灣知識及風格的[小語言模型（small langugae model, SLM）](https://www.ibm.com/think/topics/small-language-models)之對話模型。
 <details>
   <summary><b>Model Change Log</b></summary>