--- license: apache-2.0 base_model: LiquidAI/LFM2.5-350M tags: - reasoning - thinking - cot - liquid - lfm - full-finetune - agentic - mmlu-pro - aime - koarai language: - en - ru - zh pipeline_tag: text-generation library_name: transformers ---
KoarAI LFM2.5-350M Thinking Banner # 🐨 KoarAI / LFM2.5-350M-Thinking [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Model Revision](https://img.shields.io/badge/Model%20Code-0002-purple.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking) [![Fine-Tuning: 100% Full Weights](https://img.shields.io/badge/Fine--Tuning-100%25%20Full%20Weights-emerald.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking) [![Parameters](https://img.shields.io/badge/Parameters-350M-orange.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking) [![GGUF Quantized](https://img.shields.io/badge/GGUF-Available-blue.svg)](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking-GGUF)
## 📌 Release Note: Model Code `0002` (Weight Architecture Update) > [!IMPORTANT] > **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 ` ... ` 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 ` ... ` blocks. --- ## 💡 Native Thinking Mode The model natively reasons before outputting its final response: ```text <|im_start|>user Solve: 32 + 32 - 42<|im_end|> <|im_start|>assistant 1. Evaluate 32 + 32 = 64. 2. Subtract 42 from 64: 64 - 42 = 22. \boxed{22}<|im_end|> ``` --- ## ⚡ Quickstart ```python 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`**](https://huggingface.co/KoarAI/LFM2.5-350M-Thinking-GGUF) --- ## 🐨 Maintained by KoarAI Lab Released for the open-source AI community by **KoarAI**.