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

Libraries

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

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

pipe = pipeline("text-generation", model="playforgecoding/LFM2-1.2B-Extract-lesson")
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-lesson")
model = AutoModelForCausalLM.from_pretrained("playforgecoding/LFM2-1.2B-Extract-lesson", 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-lesson 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-lesson"
# 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-lesson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

LFM2-1.2B-Extract, fine-tuned for Spelling Creator lesson documents

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

What it does

Turns one section of a spelling lesson document, as a person might type it up or as the app's own Word export reads back as plain text, into the lesson's JSON: the passage paragraphs copied word for word, the spelling words, and every question with its printed answers and working-out. 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.

The stock Extract model handled this badly: it split paragraphs into sentences, invented answers for open questions, and often abandoned the schema. This fine-tune copies rather than invents.

Training

LoRA (rank 16, learning rate 2e-4, 3 epochs, 4096-token context) on 412 examples: the 13 published lessons on the Spelling Creator hub, each rendered in 7 document layouts (the app's Word export read as raw text, and six typed-up styles) and cut into sections, paired with the section's lesson JSON. 84 sections from the two newest lessons were held out. The data generator and the training notebook are in the Spelling Creator repo under packages/core/scripts/extract-eval/.

Results

On a sample of 20 held-out sections spread across both held-out lessons and all seven layouts:

metric	value
sections parsed as JSON	95%
spelling words exact	85%
question prompts found	95%
answers exact	98%
answers invented for open questions	1

For comparison, the stock model's answers were right about half the time on a tidy typed document and a quarter of the time on the Word export.

Question types are not reliable from this or any small model; Spelling Creator derives the type from the answers and the wording instead. The app's rule-based parser still beats this model on every regular layout, so the model is for documents the parser cannot read. The full write-up is at spellingcreator.org/docs/monorepo/document-import-experiment.

Prompt

The system prompt is Return data as a JSON object with the following schema: followed by the JSON Schema of a section (name, paragraphs, spellingWords, and questions, each with prompt, type, answers and steps), and the user turn is the section's text. Greedy decoding.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "playforgecoding/LFM2-1.2B-Extract-lesson"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": "Return data as a JSON object with the following schema:\n" + schema},
    {"role": "user", "content": section_text},
]
text = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
ids = tok(text, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
out = model.generate(ids, max_new_tokens=1500, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Licence

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

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

BF16

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

Base model

LiquidAI/LFM2-1.2B

Finetuned

LiquidAI/LFM2-1.2B-Extract

Finetuned

(2)

this model

Quantizations

1 model