Instructions to use LiquidAI/LFM2.5-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-350M with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-350M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-350M")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-350M")
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 LiquidAI/LFM2.5-350M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-350M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-350M

SGLang

How to use LiquidAI/LFM2.5-350M 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 "LiquidAI/LFM2.5-350M" \
    --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": "LiquidAI/LFM2.5-350M",
		"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 "LiquidAI/LFM2.5-350M" \
        --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": "LiquidAI/LFM2.5-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-350M with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-350M
```

Add IFStruct v1.0 evaluation result

by SaylorTwift HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

1 day ago

No description provided.

Add IFStruct v1.0 evaluation result0df06923

SaylorTwift

1 day ago

Hugging Face CLI

Add IFStruct v1.0 evaluation result for LiquidAI/LFM2.5-350M

Summary

This PR adds an IFStruct v1.0 evaluation result extracted from the Liquid AI IFStruct v1.0 blog to the .eval_results/ directory, following the Hugging Face Hub evaluation-results specification.

Benchmark Added

Benchmark	Score	Hub Dataset	Task ID	Notes
IFStruct v1.0	44.90	`LiquidAI/ifstruct-v1.0`	`ifstruct_v1`	+ IFStruct RL

This is the LFM2.5-350M model after IFStruct RL fine-tuning, as reported in the blog's leaderboard. (The base LFM2.5-350M scores 21.10 on the same benchmark.)

Source

Blog: https://www.liquid.ai/blog/ifstruct-v1.0
Model card: https://huggingface.co/LiquidAI/LFM2.5-350M

Files Added

.eval_results/LFM2.5-350M.yaml

Verification

These results were extracted from the official benchmark table published in the IFStruct v1.0 blog. No verified token is provided as these were not run via HF Jobs with inspect-ai.

mlabonne changed pull request status to merged about 24 hours ago

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