KoarAI/LFM2.5-350M-Thinking
KoarAI/LFM2.5-350M-Thinking is an ultra-compact 350 million parameter language model from KoarAI, built on the Liquid Foundation Model (LFM2.5-350M) architecture. This model is specifically fine-tuned for native Chain-of-Thought (CoT) reasoning, demonstrating strong multi-step logic and mathematical deduction. It excels at structured problem-solving within dedicated blocks, making it suitable for tasks requiring explicit reasoning processes.
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KoarAI/LFM2.5-350M-Thinking: Compact Reasoning Model
This model, designated as Code: 0002, is an ultra-compact, high-efficiency language model developed by KoarAI. It features native Chain-of-Thought (CoT) reasoning capabilities and is built upon the Liquid Foundation Model architecture (LiquidAI/LFM2.5-350M).
Key Capabilities & Training
- Native Reasoning: The model is designed to reason internally before generating a final response, utilizing
<think> ... </think>blocks for explicit multi-step logic and mathematical deduction. - Compact Size: Despite having only 350 Million parameters, it demonstrates strong structured problem-solving abilities.
- Full Parameter Fine-Tuning: It underwent 100% full parameter fine-tuning over 9 epochs, integrating an expanded multi-teacher dataset.
- Distilled Reasoning Traces: Training data included distilled reasoning traces from frontier models such as
Qwen 3.8 Max,GLM 5.2,Kimi K3,DeepSeek-V4-Pro 0813 Agentic, and specialized datasets likeMMLU-ProandAIME 2026 Mathematics.
Use Cases
This model is particularly well-suited for applications requiring:
- Explicit Reasoning: Tasks where the intermediate steps of problem-solving are important or need to be auditable.
- Mathematical Deduction: Solving arithmetic and more complex mathematical problems with step-by-step logic.
- Structured Problem-Solving: Scenarios demanding a structured approach to arrive at a solution.
Quantized GGUF versions are available for llama.cpp, Ollama, and LM Studio at KoarAI/LFM2.5-350M-Thinking-GGUF.