---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- text-generation
- transformer
- instruction-tuned
- reasoning
- web-search
- rag
- small-language-model
- edge-ai
- on-device
- cpu-inference
- ollama
- gguf
- causal-lm
- conversational
pipeline_tag: text-generation
library_name: gguf
model-index:
- name: AILO-152M-v2
  results: []
---

# AILO-152M-v2  Tiny LLM with Chat, Reasoning & Web Search ⚡

> **A 152M-parameter language model that runs on almost anything**  laptops, old PCs, even a Raspberry Pi  yet does instruction-following chat, step-by-step reasoning, and **web search** for fresh facts.

**AILO** (Artificial Intelligence Language Operator) is a compact, fast, from-scratch transformer. v2 turns the original base model into a real assistant: it answers questions, thinks before answering, and can use **live web results** to answer about things it was never trained on.

```bash
ollama run Alieno/ailo-152m-v2
```

| | |
|---|---|
| 🧠 **Parameters** | 151.9M |
| ⚡ **Speed** | up to **384 tok/s** (GPU), runs on **CPU & edge** |
| 📦 **Size** | 97 MB (q4_k_m) – 305 MB (f16) |
| 🌐 **Web search** | yes (context-following) |
| 💭 **Reasoning** | yes (`<think>`) |
| 🪶 **Min RAM** | ~300 MB |

---

## ✨ Why AILO-152M-v2?

- **Runs anywhere**  97 MB quantized, ~300 MB RAM. Old laptops, mini-PCs, Raspberry Pi, phones.
- **Fast**  fastest in its class (see benchmarks). Real-time chat even on modest hardware.
- **Web-aware**  trained for *context-following*, so it answers from fresh search results instead of stale memory.
- **Distilled from a bigger model**  answers learned from **Gemma 3 4B** (knowledge distillation): richer, better-structured replies than its size suggests.
- **Honest small model**  strong at concise factual Q&A and conversation; pairs with tools for exact math.
- **Open & local**  no cloud, full privacy, drop-in for Ollama.

**Great for:** edge/on-device AI, offline assistants, learning how LLMs work, fast prototyping, low-power servers, privacy-first chatbots.

---

## 🚀 Quick start

### Ollama (recommended)
```bash
ollama run Alieno/ailo-152m-v2
>>> What is the capital of Italy?
The capital city of Italy is Rome.
```

Tags: `:latest` / `:q8_0` (best quality, 156 MB) · `:q4_k_m` (smallest, 97 MB) · `:f16` (full precision, 305 MB)

### API
```bash
curl http://localhost:11434/api/chat -d '{
  "model": "Alieno/ailo-152m-v2",
  "messages": [{"role": "user", "content": "Explain what gravity is."}]
}'
```

---

## 🏆 Benchmarks

Evaluated via Ollama `/api/chat` on factual QA, reasoning and coherence vs comparable and **larger** models:

| Model | Params | Factual | Reasoning | Coherence | Speed (tok/s) |
|-------|--------|---------|-----------|-----------|---------------|
| **AILO-152M-v2** | **152M** | **7/8** | 1–2/5 | **100%** | **384** 🥇 |
| SmolLM2 | 135M | 8/8 | 1/5 | 98% | 403 |
| Qwen2.5 | 500M | 8/8 | 3–4/5 | 96% | 213 |
| TinyLlama | 1.1B | 8/8 | 1–2/5 | 97% | 260 |

- 🥇 **Top coherence** (100%  virtually no repetition) and among the **fastest**.
- Competitive on factual accuracy with models its size and **larger**.
- Trails only bigger instruction-tuned models on multi-step reasoning  expected for the smallest, from-scratch model here.

> Measured on an NVIDIA RTX 5060 Ti. Reasoning has run-to-run variance on an 8/5-question micro-suite.

---

## 🖥️ Hardware & performance

AILO-152M is tiny, so it runs **far beyond high-end GPUs**  including old and low-power hardware. Approximate generation speed (q4_k_m, ~97 MB):

| Hardware | Type | Est. speed (tok/s) | Notes |
|----------|------|--------------------|-------|
| RTX 5060 Ti / 4070+ | Modern GPU | **350–450** | ✅ measured: 384 (q8_0) |
| RTX 3060 / 2070 | Mid GPU | ~250–350 | smooth real-time |
| GTX 1660 / 1060 | **Older GPU** | ~150–220 | still real-time |
| GTX 1050 / MX150 | **Old laptop GPU** | ~90–140 | very usable |
| Ryzen 7 / Core i7 (recent) | Modern CPU | ~45–80 | no GPU needed |
| Core i5 ~2015 | **Old CPU** | ~18–30 | usable for chat |
| Raspberry Pi 5 | **SBC / edge** | ~10–16 | runs offline |
| Raspberry Pi 4 | **Low-power SBC** | ~5–9 | runs offline |
| Recent smartphone | **Mobile** | ~15–35 | via llama.cpp/Termux |

*Estimates except the measured RTX 5060 Ti; real numbers vary with quantization, RAM bandwidth and build flags. The takeaway: **AILO runs even where larger models can't load at all.***

**Minimum requirements:** ~300 MB RAM (q4_k_m), any x86-64 / ARM CPU. No GPU required.

---

## 💬 Chat format

Trained on this template (tags are plain GPT-2 BPE sequences  no vocab extension):
```
<|user|>
{question}
<|assistant|>
<think>{optional reasoning}</think>
{answer}<|end|>
```

---

## 🌐 Web search (fresh facts)

AILO v2 is trained for **context-following with override**: give it search results and it answers from them  **even when they contradict its training-time knowledge**, so it can use *up-to-date* facts. When no context is given, it falls back to its own (true) knowledge.

A ready pipeline is included (`ailo_web.py`): DuckDuckGo → instant-answer + **semantic re-ranking** (MiniLM) with language/relevance filters → short clean context (fits the 512-token window) → AILO answers.

```bash
python ailo_web.py "What is the tallest mountain in the world?"
# -> "Mount Everest, at 8,848 meters."
```

*This is how a 152M model can answer about events it never saw in training.*

---

## 💭 Reasoning (thinking)

The model declares the **`thinking` capability**: set `"think": true` and the reasoning trace is returned in `message.thinking`, separate from the answer (shown in a dedicated box in the Ollama desktop app). Best on reasoning-style prompts; for exact math, pair with a calculator tool.

---

## 🐍 Python (Transformers)
```python
from huggingface_hub import hf_hub_download
import torch, tiktoken, sys
repo = "xxrickyxx/ailo-152m-v2"
for f in ["config.json","configuration_ailo.py","modeling_ailo.py","pytorch_model.bin"]:
    hf_hub_download(repo_id=repo, filename=f, local_dir="ailo_v2")
sys.path.insert(0, "ailo_v2")
from modeling_ailo import AILOForCausalLM
from configuration_ailo import AILOConfig
model = AILOForCausalLM(AILOConfig())
model.load_state_dict(torch.load("ailo_v2/pytorch_model.bin", map_location="cpu"), strict=False)
model.eval()
tok = tiktoken.get_encoding("gpt2")
ids = torch.tensor([tok.encode_ordinary("<|user|>\nWhat is the capital of Italy?\n<|assistant|>\n")])
print(tok.decode(model.generate(ids, max_new_tokens=40, temperature=0.3)[0].tolist()))
```

---

## 📐 Model details

| Property | Value |
|----------|-------|
| Parameters | 151.9M |
| Architecture | Decoder-only Transformer (LayerNorm · RoPE · SwiGLU) |
| Layers / Hidden / Heads | 12 / 768 / 12 |
| Context length | 512 tokens |
| Vocabulary | 50,257 (GPT-2 BPE) |
| Base | AILO-152M (FineWeb-Edu, 182k steps) |
| Fine-tuning | SFT + **distillation from Gemma 3 4B**: instruction + reasoning (GSM8K) + context-following (SQuAD) + context-override + tool-use |
| Formats | GGUF (q4_k_m, q8_0, f16) + PyTorch |

---

## ⚠️ Limitations
- 152M params: limited world knowledge and multi-step reasoning vs larger models.
- 512-token context: best with short, focused prompts; not for long documents.
- Web-search quality depends on search-result quality; best for well-defined factual questions.
- For exact arithmetic, use the tool/agent layer (the calculator does the math).
- Primarily English.

---

## 📜 License

This project uses a **dual-license** model.

### 🆓 Non-Commercial License
Released under **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)**.

You are free to:
- Use the model for **research, education, and personal projects**
- **Modify and fine-tune** the model
- **Redistribute derivatives** under the same license

You must:
- Provide **attribution**
- Keep the **same license** for derivative works
- **Not** use the model for **commercial purposes**

### 💼 Commercial License
Commercial use of AILO-152M is **not permitted** under the free license. Commercial use includes:
- Integration into paid products or services
- Use in SaaS platforms, APIs, or enterprise systems
- Any application that generates revenue directly or indirectly

For commercial licensing, a separate paid agreement (royalty or license fee) is required. Please contact the author.

---

## 📬 Contact

For research collaboration or commercial licensing inquiries, contact the project maintainer:

**Riccardo Sparacino**  [LinkedIn](https://www.linkedin.com/in/riccardo-sparacino-developer-php-javascript-mysql-app-ios-android/)

---

## 📑 Citation
```bibtex
@misc{ailo152m_v2_2026,
  title  = {AILO-152M-v2: A Tiny Instruction-Tuned LLM with Reasoning and Web Search},
  author = {Sparacino, Riccardo},
  year   = {2026},
  note   = {Dual-licensed CC BY-NC-SA 4.0 / commercial}
}
```

## 🙏 Acknowledgments
Built with [Ollama](https://ollama.com) and [llama.cpp](https://github.com/ggerganov/llama.cpp). Fine-tuning data: Alpaca-cleaned, GSM8K, SQuAD. Knowledge-distillation teacher: **Gemma 3 4B**. Embeddings for web re-ranking: sentence-transformers MiniLM.

---

*Keywords: small language model, tiny LLM, 152M, efficient LLM, edge AI, on-device LLM, CPU inference, Raspberry Pi LLM, Ollama model, GGUF, instruction-tuned, reasoning model, web search LLM, RAG, offline assistant, low-resource, fast inference.*