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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KoarAI/LFM2.5-350M-Thinking")
model = AutoModelForCausalLM.from_pretrained("KoarAI/LFM2.5-350M-Thinking", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KoarAI/LFM2.5-350M-Thinking with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

LFM2.5-350M-Thinking / README.md

KoarAI

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	---
	license: apache-2.0
	base_model: LiquidAI/LFM2.5-350M
	tags:
	- reasoning
	- thinking
	- cot
	- liquid
	- lfm
	- full-finetune
	- agentic
	- mmlu-pro
	- aime
	- koarai
	language:
	- en
	- ru
	- zh
	pipeline_tag: text-generation
	library_name: transformers
	---

	<div align="center">

	<img src="https://huggingface.co/KoarAI/LFM2.5-350M-Thinking/resolve/main/banner.png" alt="KoarAI LFM2.5-350M Thinking Banner" width="100%" style="border-radius: 12px; box-shadow: 0 4px 20px rgba(0,0,0,0.3);"/>

	# 🐨 KoarAI / LFM2.5-350M-Thinking

	[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Model Revision](https://img.shields.io/badge/Model%20Code-0002-purple.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking)
	[![Fine-Tuning: 100% Full Weights](https://img.shields.io/badge/Fine--Tuning-100%25%20Full%20Weights-emerald.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking)
	[![Parameters](https://img.shields.io/badge/Parameters-350M-orange.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking)
	[![GGUF Quantized](https://img.shields.io/badge/GGUF-Available-blue.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking-GGUF)

	</div>

	## 📌 Release Note: Model Code `0002` (Weight Architecture Update)

	> [!IMPORTANT]
	> Model Code: `0002`
	> This version underwent a comprehensive 100% Full Parameter Fine-Tuning across 9 epochs with a cosine learning rate scheduler. It integrates an expanded multi-teacher dataset (Reasoning CoT + DeepSeek-V4-Pro Agentic + MMLU-Pro + AIME 2026 Mathematics) and strict syntactic normalization for `<think> ... </think>` blocks.
	>
	> 🚀 KoarAI Release & Versioning Policy: Starting from the upcoming release (`0003` and beyond), rather than overwriting existing models, each new iteration will be released into its own dedicated repository (e.g., `KoarAI/LFM2.5-350M-Thinking-v3`, `KoarAI/LFM2.5-350M-Thinking-RU`, etc.).

	---

	## 🌟 Overview

	`KoarAI/LFM2.5-350M-Thinking` (Code: 0002) is an ultra-compact, high-efficiency language model featuring native Chain-of-Thought (CoT) reasoning capabilities.

	Built upon the state-of-the-art Liquid Foundation Model architecture (LiquidAI/LFM2.5-350M), this model was trained using 100% Full Parameter Fine-Tuning on a balanced blend of distilled reasoning traces from frontier models:
	- `Qwen 3.8 Max`
	- `GLM 5.2`
	- `Kimi K3`
	- `DeepSeek-V4-Pro 0813 Agentic`
	- `MMLU-Pro & AIME 2026 Mathematics`

	Despite having only 350 Million parameters, the model demonstrates strong multi-step logic, mathematical deduction, and structured problem-solving inside native `<think> ... </think>` blocks.

	---

	## 💡 Native Thinking Mode

	The model natively reasons before outputting its final response:

	```text
	<\|im_start\|>user
	Solve: 32 + 32 - 42<\|im_end\|>
	<\|im_start\|>assistant
	<think>
	1. Evaluate 32 + 32 = 64.
	2. Subtract 42 from 64: 64 - 42 = 22.
	</think>
	\boxed{22}<\|im_end\|>
	```

	---

	## ⚡ Quickstart

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "KoarAI/LFM2.5-350M-Thinking"

	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.float16,
	device_map="auto",
	trust_remote_code=True
	)

	messages = [
	{"role": "user", "content": "How many 'r' in strawberry?"}
	]

	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	outputs = model.generate(
	**inputs,
	max_new_tokens=512,
	temperature=0.6,
	top_p=0.9,
	do_sample=True,
	pad_token_id=tokenizer.eos_token_id
	)

	print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=False))
	```

	---

	## 📦 GGUF & Quantization

	Official quantized GGUF versions (`FP16`, `Q8_0`, `Q5_K_M`, `Q4_K_M`, `Q4_0`) for llama.cpp, Ollama, and LM Studio are available at:
	👉 [`KoarAI/LFM2.5-350M-Thinking-GGUF`](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking-GGUF)

	---

	## 🐨 Maintained by KoarAI Lab
	Released for the open-source AI community by KoarAI.