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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🐨 KoarAI / LFM2.5-350M-Thinking

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

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:

<|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

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

🐨 Maintained by KoarAI Lab

Released for the open-source AI community by KoarAI.

Downloads last month: -

Safetensors

Model size

0.4B params

Tensor type

F16

Model tree for KoarAI/LFM2.5-350M-Thinking

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

(62)

this model

Quantizations

2 models

Collection including KoarAI/LFM2.5-350M-Thinking

Test models / Outdated

Collection

Test models or first models that can be inaccurate • 3 items • Updated about 9 hours ago • 1