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, type 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.

Spelling Creator reads typed lessons with rules first, and only hands this model the sections the rules cannot read: questions typed with no question marks or numbers, an answer left glued to its question, a numbered list that lost its line breaks. This is the second fine-tune, trained on exactly those.

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 the spelling-creator-document-import dataset: 521 sections from 10 lessons published on the Spelling Creator hub, each rendered in 9 layouts and paired with the section's lesson JSON. Seven layouts the app's rules read (its Word export as raw text, and six typed-up styles), and two they cannot, from which only the sections the app would really send to the model are kept, laid out exactly as it sends them. The two newest lessons (108 sections) are held out. The data generator and the training notebook are in the Spelling Creator repo under packages/core/scripts/extract-eval/.

Results

The int8 ONNX export, on every section of both held-out lessons (12 per layout):

layout	parsed	answers	composite
no question marks or numbers	100%	97%	96%
numbered list on one line	92%	91%	89%
Word export as raw text	100%	99%	96%
numbered, bracketed answers	100%	100%	97%
Q and A lines	100%	100%	94%
bare capitals, no headings	100%	99%	96%
bullets, square brackets	92%	92%	89%
numbered, colon	100%	96%	96%
working-out on its own line	92%	92%	88%

On the first two, the layouts the app actually sends it, Spelling Creator's rules score 66 and 40 percent. Every row under 94 percent is one section in twelve whose reply is not valid JSON (a key in the wrong place, a bare string where a question belonged, a reply that repeated itself), which the app shows as unread. On the sections that parse, every layout is 96 to 100 percent. The model's own question types are right 87 to 97 percent of the time, above the app's rule for deriving them, so the app keeps them. 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 a short guide to the question types; 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: 39

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

(3)

this model

Quantizations

1 model

playforgecoding
/

LFM2-1.2B-Extract-lesson