Instructions to use User01110/LFM-2.5-350M-MathMini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use User01110/LFM-2.5-350M-MathMini with Transformers:

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

pipe = pipeline("text-generation", model="User01110/LFM-2.5-350M-MathMini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("User01110/LFM-2.5-350M-MathMini")
model = AutoModelForMultimodalLM.from_pretrained("User01110/LFM-2.5-350M-MathMini")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use User01110/LFM-2.5-350M-MathMini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "User01110/LFM-2.5-350M-MathMini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "User01110/LFM-2.5-350M-MathMini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/User01110/LFM-2.5-350M-MathMini

SGLang

How to use User01110/LFM-2.5-350M-MathMini 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 "User01110/LFM-2.5-350M-MathMini" \
    --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": "User01110/LFM-2.5-350M-MathMini",
		"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 "User01110/LFM-2.5-350M-MathMini" \
        --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": "User01110/LFM-2.5-350M-MathMini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use User01110/LFM-2.5-350M-MathMini with Docker Model Runner:
```
docker model run hf.co/User01110/LFM-2.5-350M-MathMini
```

LFM-2.5-350M-MathMini / README.md

User01110

Update README.md

12d4fdc verified 5 days ago

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	---
	library_name: transformers
	pipeline_tag: text-generation
	base_model: LiquidAI/LFM2.5-350M
	tags:
	- causal-lm
	- sft
	- math
	- chatml
	- transformers
	---

	# Math Curated SFT

	This is a full-model SFT checkpoint trained from `LiquidAI/LFM2.5-350M` on
	`User01110/math-curated-dataset`.

	## Training

	- Method: TRL `SFTTrainer`
	- Dataset split: `train`
	- Training rows: 39040
	- Epochs: 1
	- Max sequence length: 1024
	- Target style: full generated response
	- Format: the base tokenizer chat template via `tokenizer.apply_chat_template`
	- System prompt: `You are a math-focused assistant. Solve the user's math problem and follow the training format: Understanding Query, Drafting Answer, Refining The Answer, and Final Response.`

	## Format

	Each row is formatted with:

	```python
	messages = [
	{"role": "system", "content": SYSTEM_PROMPT},
	{"role": "user", "content": prompt},
	]
	prompt_text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	)
	training_text = prompt_text + response + (tokenizer.eos_token or "")
	```

	## Important limitation

	This model is trained on generated math-style data. Responses may contain
	incorrect arithmetic or flawed reasoning, and should not be treated as reliable
	mathematical answers without independent verification.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "User01110/LFM-2.5-350M-MathMini"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id)

	messages = [
	{"role": "system", "content": "You are a math-focused assistant. Solve the user's math problem and follow the training format: Understanding Query, Drafting Answer, Refining The Answer, and Final Response."},
	{"role": "user", "content": "John has 22 apples, he eats 10 of them, how many apples does john have now?"},
	]
	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	)
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(
	**inputs,
	max_new_tokens=512,
	do_sample=False,
	repetition_penalty=1.1,
	pad_token_id=tokenizer.pad_token_id,
	eos_token_id=tokenizer.eos_token_id,
	)
	print(tokenizer.decode(outputs[0], skip_special_tokens=False))
	```