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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("playforgecoding/LFM2-1.2B-Extract-facts")
model = AutoModelForCausalLM.from_pretrained("playforgecoding/LFM2-1.2B-Extract-facts", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "playforgecoding/LFM2-1.2B-Extract-facts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "playforgecoding/LFM2-1.2B-Extract-facts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use playforgecoding/LFM2-1.2B-Extract-facts with Docker Model Runner:
```
docker model run hf.co/playforgecoding/LFM2-1.2B-Extract-facts
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LFM2-1.2B-Extract, fine-tuned to list checkable facts in lesson passages

The merged weights (bf16, safetensors). For the browser or Node, use the ONNX export at playforgecoding/LFM2-1.2B-Extract-facts-ONNX.

Experimental. It is not used by Spelling Creator: on held-out lessons it still lists a few facts the rules leave out, and two of them turned into false alarms in a fact check (see Results).

What it does

Reads one passage of a lesson and lists the facts in it that can be checked against Wikidata: a number, a date or a named thing stated about one specific, named, real thing ("Mount Everest rises 8,849 METRES", "CANBERRA is the capital"). Each claim has the exact words it rests on (quote), what it is about (subject, kind), which property it states, and the value and unit or the stated name. It never says whether a fact is right; that is done in code against Wikidata.

It is a fine-tune of LiquidAI/LFM2-1.2B-Extract for Spelling Creator, a lesson builder for Spelling to Communicate (S2C), where lessons are read aloud to nonspeaking spellers. Spelling Creator's fact check asks a hosted model for the claims today; a model like this one would let the whole check run on the device.

Training

LoRA (rank 16, learning rate 2e-4, 5 epochs, 3072-token context) on the spelling-creator-fact-claims dataset: 347 passages and 750 claims, from 10 hub lessons and 116 sections written for coverage, 96 of the passages with nothing to list. The labels are Claude Opus's, made with the same prompt Spelling Creator's server sends its own provider. Only the reply carries loss. Two hub lessons (24 passages, 20 claims) are held out. The scripts are in the Spelling Creator repo under packages/core/scripts/fact-eval/.

Results

The int8 ONNX export on the 24 held-out passages, beside the same training on the 350M model:

metric	350M	this model
replies in the schema	24/24	24/24
labelled claims found	50%	55%
listed claims that match a label	42%	46%
quotes not in the passage	2	2
empty list for a passage with nothing to list	8/13	10/13
subject named right, of the claims found	50%	64%
value, unit and qualifier right, of those	90 to 100%	100%

Run through the real checker against Wikidata, its claims give 2 false alarms (a right fact reported as contradicted by Wikidata) in those 24 passages, and miss 7 of the 14 findings the labels give. Both false alarms are facts the rules leave out: the year Pompeii was rediscovered, checked as when it began, and how long sunlight takes to reach Earth, checked as Earth's orbital period. About 10 seconds a passage on an M4's CPU. The write-up is at spellingcreator.org/docs/monorepo/fact-claim-model.

Prompt

The system prompt is the rules (which facts count, the properties with a short description each, how to choose between near ones, what each field means) and the JSON Schema of the reply; the user turn is Lesson: <title>, a blank line and the passage. Greedy decoding. The exact text is passageClaimsSystemPrompt in packages/core/src/factClaims.js, and every training example carries it.

Licence

Derived from LFM2-1.2B-Extract and released under the same LFM Open License v1.0 (see LICENSE).

Downloads last month: 28

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for playforgecoding/LFM2-1.2B-Extract-facts

Base model

LiquidAI/LFM2-1.2B

Finetuned

LiquidAI/LFM2-1.2B-Extract

Finetuned

(3)

this model

Quantizations

1 model

playforgecoding
/

LFM2-1.2B-Extract-facts