Instructions to use playforgecoding/LFM2-350M-Extract-facts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use playforgecoding/LFM2-350M-Extract-facts with Transformers:

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

pipe = pipeline("text-generation", model="playforgecoding/LFM2-350M-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/LFM2-350M-Extract-facts")
model = AutoModelForCausalLM.from_pretrained("playforgecoding/LFM2-350M-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/LFM2-350M-Extract-facts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "playforgecoding/LFM2-350M-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/LFM2-350M-Extract-facts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/playforgecoding/LFM2-350M-Extract-facts

SGLang

How to use playforgecoding/LFM2-350M-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/LFM2-350M-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/LFM2-350M-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/LFM2-350M-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/LFM2-350M-Extract-facts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use playforgecoding/LFM2-350M-Extract-facts with Docker Model Runner:
```
docker model run hf.co/playforgecoding/LFM2-350M-Extract-facts
```

playforgecoding commited on 13 days ago

Commit

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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/LFM2-350M-Extract
+datasets:
+  - playforgecoding/spelling-creator-fact-claims
 tags:
+  - liquid
+  - lfm2
+  - edge
+  - extraction
+  - fact-checking
+  - spelling-creator
+  - trl
+  - sft
 ---
+# LFM2-350M-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/LFM2-350M-Extract-facts-ONNX](https://huggingface.co/playforgecoding/LFM2-350M-Extract-facts-ONNX).
+**Experimental.** It is not used by Spelling Creator: on held-out lessons it
+still names the subject of a fact wrong half the time, which turns 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/LFM2-350M-Extract](https://huggingface.co/LiquidAI/LFM2-350M-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'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](https://huggingface.co/datasets/playforgecoding/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:
+| metric                                        | stock 350M | this model |
+| --------------------------------------------- | ---------: | ---------: |
+| replies in the schema                         |      16/24 |      24/24 |
+| labelled claims found                         |         0% |        50% |
+| listed claims that match a label              |        n/a |        42% |
+| quotes not in the passage                     |        243 |          2 |
+| empty list for a passage with nothing to list |       0/13 |       8/13 |
+| subject named right, of the claims found      |        n/a |        50% |
+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,
+both from a wrong subject or a date the rules say to leave out, and miss 9 of
+the 14 findings the labels give. The value, unit and qualifier of the claims
+it does find are almost always right; naming the subject is what it cannot do
+at this size. The write-up is at
+[spellingcreator.org/docs/monorepo/fact-claim-model](https://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 LFM2-350M-Extract and released under the same LFM Open License
+v1.0 (see LICENSE).