Instructions to use LiquidAI/LFM2-350M-PII-Extract-JP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-350M-PII-Extract-JP with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2-350M-PII-Extract-JP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-350M-PII-Extract-JP")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-350M-PII-Extract-JP", 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 LiquidAI/LFM2-350M-PII-Extract-JP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-350M-PII-Extract-JP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2-350M-PII-Extract-JP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-350M-PII-Extract-JP

SGLang

How to use LiquidAI/LFM2-350M-PII-Extract-JP 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 "LiquidAI/LFM2-350M-PII-Extract-JP" \
    --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": "LiquidAI/LFM2-350M-PII-Extract-JP",
		"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 "LiquidAI/LFM2-350M-PII-Extract-JP" \
        --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": "LiquidAI/LFM2-350M-PII-Extract-JP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2-350M-PII-Extract-JP with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-350M-PII-Extract-JP
```

mlabonne commited on Oct 21, 2025

Commit

7f9ef1a

verified ·

1 Parent(s): 10d3da6

Update README.md

Browse files

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README.md +18 -0

README.md CHANGED Viewed

@@ -181,6 +181,16 @@ If the same entity appears multiple times with slight formatting variations, the
 - llama.cpp: [LFM2-350M-PII-Extract-JP-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP-GGUF)
 - LEAP: [LEAP model library](https://leap.liquid.ai/models?model=lfm2-350m-pii-extract-jp)
 ## 📬 Contact
 If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
@@ -282,6 +292,14 @@ Extract , <company_name>, <email_address>, <human_name>, <phone_number><|im_end|
 - llama.cpp: [LFM2-350M-PII-Extract-JP-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP-GGUF)
 - LEAP: [LEAP モデルライブラリ](https://leap.liquid.ai/models?model=lfm2-350m-pii-extract-jp)
 ---
 ## 📬 お問い合わせ

 - llama.cpp: [LFM2-350M-PII-Extract-JP-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP-GGUF)
 - LEAP: [LEAP model library](https://leap.liquid.ai/models?model=lfm2-350m-pii-extract-jp)
+You can use the following Colab notebooks for easy inference and fine-tuning:
+| Notebook | Description | Link |
+|-------|------|------|
+| Inference | Run the model with Hugging Face's transformers library. | <a href="https://colab.research.google.com/drive/1kIaBNZYZSZ9wzrl9Yot3W5ZKR1lf47k1?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| DPO (TRL) | Preference alignment with Direct Preference Optimization (DPO) using TRL. | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (Axolotl) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Axolotl. | <a href="https://colab.research.google.com/drive/155lr5-uYsOJmZfO6_QZPjbs8hA_v8S7t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (Unsloth) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Unsloth. | <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
 ## 📬 Contact
 If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
 - llama.cpp: [LFM2-350M-PII-Extract-JP-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP-GGUF)
 - LEAP: [LEAP モデルライブラリ](https://leap.liquid.ai/models?model=lfm2-350m-pii-extract-jp)
+| ノートブック | 説明 | リンク |
+|-------|------|------|
+| 推論 | Hugging Faceのtransformersライブラリを使用してモデルを実行します。 | <a href="https://colab.research.google.com/drive/1kIaBNZYZSZ9wzrl9Yot3W5ZKR1lf47k1?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (TRL) | TRLを使用したLoRAアダプターによる教師あり微調整(SFT)ノートブック。 | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| DPO (TRL) | TRLを使用した直接選好最適化(DPO)による選好アライメント。 | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (Axolotl) | Axolotlを使用したLoRAアダプターによる教師あり微調整(SFT)ノートブック。 | <a href="https://colab.research.google.com/drive/155lr5-uYsOJmZfO6_QZPjbs8hA_v8S7t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (Unsloth) | Unslothを使用したLoRAアダプターによる教師あり微調整(SFT)ノートブック。 | <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
 ---
 ## 📬 お問い合わせ