Instructions to use LiquidAI/LFM2-1.2B-Extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-1.2B-Extract with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2-1.2B-Extract")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-1.2B-Extract")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-1.2B-Extract")
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

vLLM

How to use LiquidAI/LFM2-1.2B-Extract with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-1.2B-Extract"
# 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/LFM2-1.2B-Extract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-1.2B-Extract

SGLang

How to use LiquidAI/LFM2-1.2B-Extract 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/LFM2-1.2B-Extract" \
    --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/LFM2-1.2B-Extract",
		"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/LFM2-1.2B-Extract" \
        --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/LFM2-1.2B-Extract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2-1.2B-Extract with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-1.2B-Extract
```

Paulescu commited on Mar 30

Commit

e68bdd9

1 Parent(s): 1ff3a10

Fix link bar, add Discord and br tag, update Contact section

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -29,11 +29,12 @@ base_model: LiquidAI/LFM2-1.2B
   />
 </div>
 <div style="display: flex; justify-content: center; gap: 0.5em;">
-  <a href="https://playground.liquid.ai/chat">
-<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a></a>
 </div>
 </center>
 # LFM2-1.2B-Extract
 Based on [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B), LFM2-1.2B-Extract is designed to **extract important information from a wide variety of unstructured documents** (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
@@ -111,7 +112,8 @@ You can use the following Colab notebooks for easy inference and fine-tuning:
 ## 📬 Contact
-If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
 ## Citation

   />
 </div>
 <div style="display: flex; justify-content: center; gap: 0.5em;">
+<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
 </div>
 </center>
+<br>
 # LFM2-1.2B-Extract
 Based on [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B), LFM2-1.2B-Extract is designed to **extract important information from a wide variety of unstructured documents** (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
 ## 📬 Contact
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
 ## Citation