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KoarAI/LFM2.5-350M-Thinking-0004
LFM2.5-350M-Thinking-0004 is a text generation model from KoarAI. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
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Downloads · 30 days
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.safetensors707 MB · 99%
From the Hugging Face model README
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.
<think> ... </think> before providing the final answer.| 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 |
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))
For deployment with llama.cpp, Ollama, or LM Studio:
LiquidAI/LFM2.5-350M).