🧠 LFM2.5-350M-Thinking-0004

LFM2.5-350M-Thinking-0004 is an ultra-fast, lightweight 350-million-parameter Reasoning Model based on LiquidAI's Liquid Neural Network (LNN) architecture.

It is fine-tuned through two-stage multi-teacher distillation (Qwen3.8-Max, DeepSeek-R1 CoT, and GrandMaster Pro) with full structured Chain-of-Thought (<think> ... </think>), fluent Russian language mastery, and clean code generation.

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⚡ Highlights & Key Capabilities

• 🧬 Liquid Neural Network (LNN): High compute efficiency and sub-linear memory scaling.
• 📐 Structured Chain-of-Thought: Generates deep step-by-step reasoning inside <think> ... </think> before providing the final answer.
• 🇷🇺 Russian Language Proficiency: Native, grammatically sound Russian CoT reasoning without translation artifacts.
• 💻 Algorithmic Code Synthesis: Clean Python, HTML/CSS, and shell scripts without refusal loops.
• 🚀 Extreme Edge Speed: Reaches 25–40+ tokens/sec on CPU and mobile devices.

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📊 Evaluation & Benchmarks

Benchmark / Task	Metric / Result	Performance Notes
Inference Speed (Vulkan/Metal)	28.4 – 38.0 tok/s	Real-time generation on low-end hardware
GSM8K Multi-Step Math	30.8% Exact Match	Zero-shot chain-of-thought without tools
Russian Spelling / Analysis	100% CoT Accuracy	Deep step-by-step character & grammatical logic
Python Code Generation	Verified Syntax	Clean functional implementations

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🛠️ Usage Example

Transformers (PyTorch)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "KoarAI/LFM2.5-350M-Thinking-0004"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Сколько букв 'р' в слове 'трапеция'? Объясни пошагово."
messages = [{"role": "user", "content": prompt}]

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

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.6,
    top_p=0.95
)

print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

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📦 GGUF & Quantized Models

For deployment with llama.cpp, Ollama, or LM Studio:

• 🔗 GGUF Repository: KoarAI/LFM2.5-350M-Thinking-0004-GGUF

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📜 Citation & Credits

• Base model architecture by Liquid AI (LiquidAI/LFM2.5-350M).
• Distillation & Alignment by KoarAI.