Instructions to use FrontiersMind/Lumma-0.6B-Extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Lumma-0.6B-Extract with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Lumma-0.6B-Extract", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Lumma-0.6B-Extract", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FrontiersMind/Lumma-0.6B-Extract with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontiersMind/Lumma-0.6B-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": "FrontiersMind/Lumma-0.6B-Extract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrontiersMind/Lumma-0.6B-Extract

SGLang

How to use FrontiersMind/Lumma-0.6B-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 "FrontiersMind/Lumma-0.6B-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": "FrontiersMind/Lumma-0.6B-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 "FrontiersMind/Lumma-0.6B-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": "FrontiersMind/Lumma-0.6B-Extract",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrontiersMind/Lumma-0.6B-Extract with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Lumma-0.6B-Extract
```

Lumma-0.6B-Extract / README.md

vishesh-t27

updated readme.md

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	base_model:
	- FrontiersMind/Lumma-0.6B-Base
	---

	# Lumma-0.6B-Extract

	Based on [Lumma-0.6B-Base](FrontiersMind/Lumma-0.6B-Base), Lumma-0.6B-Extract is a lightweight, single-turn specialized model designed to accurately extract structured information from text in one step, enabling efficient and reliable information extraction.


	### Benchmark results

	<img src="benchmark_results.jpg" width="1000"/>

	We benchmarked Lumma-0.6B-Extract on FrontiersMind's internal structured benchmark, measuring model's performances on ~500 documents of diverse types including invoices,travel itenaries, OCR Extracted text & Emails etc.
	We plan to open-source this benchmark in the coming weeks, along with a extensive leaderboard including most popular open-weight and closed-sourced APIs and a Python library allowing to easily measure model performances on structured extraction.


	## Use cases

	- Information extraction: Extract structured information such as names, dates, organizations, locations, products, attributes, and other fields from unstructured text.
	- Document processing: Convert unstructured text from documents, emails, reports, and messages into structured JSON.
	- Data processing pipelines: Transform unstructured text into structured data for downstream applications, databases, search systems, and analytics.
	- Real-time AI applications: Power applications that require fast and reliable information extraction with low latency.
	- Resource-constrained environments: Support efficient extraction on mobile devices, edge devices, embedded systems, and hardware with limited compute and memory resources.


	> [!NOTE]
	> 🚧 Coming Soon: We will soon be bringing multilingual Indic language support to Lumma-0.6B-Extract.

	## Structured Extraction

	Lumma-0.6B-Extract uses a JSON template to define the information that should be extracted from the input text.



	The extraction process consists of two main steps:

	1. Extraction template: Provide a JSON template describing the fields and information to extract.
	2. Information extraction: Provide the input text, and the model returns the requested information in the specified JSON structure.

	This approach allows users to define custom extraction schemas depending on their application.

	## Example

	Template:

	{
	"name": "string",
	"company": "string",
	"job_title": "string"
	}

	Input text:

	John Smith joined Acme Corporation as a Senior Software Engineer.

	Expected output:

	{
	"name": "John Smith",
	"company": "Acme Corporation",
	"job_title": "Senior Software Engineer"
	}

	## Usage

	import torch
	import json
	from transformers import AutoModelForCausalLM, AutoTokenizer

	MODEL_PATH = "FrontiersMind/Lumma-0.6B-Extract"

	tokenizer = AutoTokenizer.from_pretrained(
	MODEL_PATH,
	trust_remote_code=True
	)

	model = AutoModelForCausalLM.from_pretrained(
	MODEL_PATH,
	trust_remote_code=True,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	model.eval()


	input_text = "John Smith joined Acme Corporation as a Senior Software Engineer."

	template = {
	"name": "string",
	"company": "string",
	"job_title": "string",
	}

	prompt = tokenizer.apply_chat_template(
	input_text=input_text,
	template=template,
	tokenize=False,
	add_generation_prompt=True
	)


	inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)

	with torch.inference_mode():
	out = model.generate(
	**inputs,
	max_new_tokens=512,
	do_sample=False,
	eos_token_id=tokenizer.eos_token_id,
	pad_token_id=tokenizer.pad_token_id,
	)

	raw = tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=False)
	pred = raw.split("<\|endoftext\|>")[0].strip()
	print(json.dumps(json.loads(pred), indent=4))





	## License

	This model is released under the Apache License 2.0.

	## Feedback & Suggestions

	We’d love to hear your thoughts, feedback, and ideas!

	- Discord: https://discord.gg/ZGdjCdRt
	- Email: support@frontiersmind.ai
	- Official Website: https://www.frontiersmind.ai/
	- LinkedIn: https://www.linkedin.com/company/frontiersmind/
	- X (Twitter): https://x.com/FrontiersMind