Instructions to use KoarAI/LFM2.5-350M-Thinking-0002-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use KoarAI/LFM2.5-350M-Thinking-0002-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
Use Docker
docker model run hf.co/KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use KoarAI/LFM2.5-350M-Thinking-0002-GGUF 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-0002-GGUF" # 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-0002-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
- Ollama
How to use KoarAI/LFM2.5-350M-Thinking-0002-GGUF with Ollama:
ollama run hf.co/KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use KoarAI/LFM2.5-350M-Thinking-0002-GGUF with Docker Model Runner:
docker model run hf.co/KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
- Lemonade
How to use KoarAI/LFM2.5-350M-Thinking-0002-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KoarAI/LFM2.5-350M-Thinking-0002-GGUF:Q8_0
Run and chat with the model
lemonade run user.LFM2.5-350M-Thinking-0002-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
π Release Note: Model Code 0002 (Dedicated GGUF Release)
Model Code:
0002
This repository contains official GGUF quantizations of KoarAI/LFM2.5-350M-Thinking (Revision0002).
Model 0002 was trained with 100% Full Parameter Fine-Tuning across 9 full epochs using a multi-teacher reasoning blend (Qwen 3.8 Max, GLM 5.2, Kimi K3, DeepSeek-V4-Pro Agentic, MMLU-Pro, AIME 2026).π Versioning Policy: Starting with this release, distinct repositories are created for each revision (
...-0002-GGUF, etc.) to maintain reproducible checkpoints.
π¦ Quantized Files
| File | Precision | File Size | Description |
|---|---|---|---|
LFM2.5-350M-Thinking-0002-f16.gguf |
FP16 |
~678 MB | Full precision unquantized weights. Maximum fidelity. |
LFM2.5-350M-Thinking-0002-Q8_0.gguf |
Q8_0 |
~347 MB | 8-bit quantization. Extremely fast with near-identical perplexity to FP16. |
π Overview
KoarAI/LFM2.5-350M-Thinking-0002-GGUF brings native Chain-of-Thought (CoT) reasoning to edge devices, CPUs, and local runtimes like llama.cpp, Ollama, and LM Studio.
- Ultra-Lightweight: Runs smoothly even on low-spec laptops, Raspberry Pi, and mobile devices.
- Native
<think>CoT: Performs step-by-step reasoning before outputting final answers. - Base Architecture: Liquid Foundation Model (LFM2.5).
π‘ Native Thinking Example
<|im_start|>user
Solve: 32 + 32 - 42<|im_end|>
<|im_start|>assistant
<think>
1. Calculate 32 + 32 = 64.
2. Calculate 64 - 42 = 22.
</think>
\boxed{22}<|im_end|>
β‘ Quickstart
1. Run with llama.cpp
llama-cli -m LFM2.5-350M-Thinking-0002-Q8_0.gguf \
-p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nSolve: 32 + 32 - 42<|im_end|>\n<|im_start|>assistant\n<think>\n" \
-n 512 --temp 0.6
2. Run with Ollama
Create a Modelfile:
FROM ./LFM2.5-350M-Thinking-0002-Q8_0.gguf
TEMPLATE """{{- range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{- end }}<|im_start|>assistant
<think>
"""
PARAMETER temperature 0.6
PARAMETER stop <|im_end|>
Then create and run:
ollama create lfm2-350m-thinking-0002 -f Modelfile
ollama run lfm2-350m-thinking-0002
π Related Models
- Base FP16 Weights (HF):
KoarAI/LFM2.5-350M-Thinking - Revision 0001 GGUF Repo:
KoarAI/LFM2.5-350M-Thinking-GGUF
π¨ Maintained by KoarAI Lab
Released for the open-source AI community by KoarAI.
- Downloads last month
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8-bit
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Model tree for KoarAI/LFM2.5-350M-Thinking-0002-GGUF
Base model
LiquidAI/LFM2.5-350M-Base