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
```

LFM2-1.2B-Extract-lesson / README.md

playforgecoding

Card for the second fine-tune

c67081f verified 14 days ago

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---
license: other
license_name: lfm1.0
license_link: LICENSE
language:
  - en
pipeline_tag: text-generation
library_name: transformers
base_model: LiquidAI/LFM2-1.2B-Extract
datasets:
  - playforgecoding/spelling-creator-document-import
tags:
  - liquid
  - lfm2
  - edge
  - extraction
  - spelling-creator
  - trl
  - sft
---
# 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](https://huggingface.co/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](https://huggingface.co/LiquidAI/LFM2-1.2B-Extract)
for [Spelling Creator](https://github.com/Spelling-Creator/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](https://huggingface.co/datasets/playforgecoding/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](https://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

```python
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).

	---
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	base_model: LiquidAI/LFM2-1.2B-Extract
	datasets:
	- playforgecoding/spelling-creator-document-import
	tags:
	- liquid
	- lfm2
	- edge
	- extraction
	- spelling-creator
	- trl
	- sft
	---

	# 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](https://huggingface.co/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](https://huggingface.co/LiquidAI/LFM2-1.2B-Extract)
	for [Spelling Creator](https://github.com/Spelling-Creator/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](https://huggingface.co/datasets/playforgecoding/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](https://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

	```python
	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).