🧠 danAI-55M-Reasoning

An Ultra-Lightweight 54.5M Agentic & Reasoning Language Model for Edge Devices, Mobile Intelligence, and Microcontrollers

Created by Asjad Ilahi (@asjadilahi)

🌟 About danAI (دانا - Wise / Intelligent)

danAI-55M-Reasoning is an ultra-compact 54.5 Million parameter Small Language Model (SLM) designed from the ground up to bring high-grade reasoning, instruction following, and autonomous agentic capabilities to low-power edge hardware, mobile processors, and IoT devices in an ultra-low 13 MB to 104 MB memory footprint.

Named after the Urdu word Dānā (دانا) meaning wise or intelligent, danAI proves that extreme efficiency and agentic intelligence can coexist without requiring multi-gigabyte models.

⚡ Quantized Models & Formats (16-bit, 8-bit, 4-bit, 2-bit)

All official quantizations and weights are hosted directly in this repository:

Bit Width	Format	Target File	File Size	Memory Footprint	Recommended Use Case / Target Hardware
16-bit	PyTorch Safetensors	`model.safetensors`	104 MB	104 MB	Standard PyTorch / Hugging Face BF16 inference
16-bit	PyTorch Safetensors	`model.fp16.safetensors`	104 MB	104 MB	Standard Half-Precision CUDA GPUs
16-bit	Master FP32	`model.fp32.safetensors`	208 MB	208 MB	Research, analysis & master precision benchmarking
16-bit	GGUF F16	`danAI-55M-Reasoning-F16.gguf`	137 MB	137 MB	llama.cpp / Ollama unquantized reference baseline
16-bit	GGUF BF16	`danAI-55M-Reasoning-BF16.gguf`	137 MB	137 MB	Apple Silicon Metal & modern CPU engines
8-bit	GGUF Q8_0	`danAI-55M-Reasoning-Q8_0.gguf`	73.5 MB	~74 MB	⭐ Recommended for Ollama / llama.cpp (Near 0% quality loss)
8-bit	PyTorch INT8	`danAI-55M-Reasoning-INT8.safetensors`	52.2 MB	~52 MB	High-speed PyTorch 8-bit symmetric weight inference
6-bit	GGUF Q6_K	`danAI-55M-Reasoning-Q6_K.gguf`	57.1 MB	~57 MB	High-precision K-quant balance
5-bit	GGUF Q5_K_M	`danAI-55M-Reasoning-Q5_K_M.gguf`	50.9 MB	~51 MB	Compact balanced K-quant
4-bit	GGUF Q4_K_M	`danAI-55M-Reasoning-Q4_K_M.gguf`	45.0 MB	~45 MB	⭐ Optimal Edge Balance: Mobile chips, Raspberry Pi, Edge AI
4-bit	GGUF Q4_0	`danAI-55M-Reasoning-Q4_0.gguf`	43.7 MB	~44 MB	Universal 4-bit block quant for embedded platforms
4-bit	PyTorch INT4	`danAI-55M-Reasoning-INT4.safetensors`	26.4 MB	~26 MB	Ultra-compact 4-bit packed weights for PyTorch on-device
3-bit	GGUF Q3_K_M	`danAI-55M-Reasoning-Q3_K_M.gguf`	38.6 MB	~39 MB	Sub-40MB K-quant compression
2-bit	GGUF Q2_K	`danAI-55M-Reasoning-Q2_K.gguf`	32.8 MB	~33 MB	2-bit K-quant for microcontrollers & extreme RAM limits
2-bit	PyTorch INT2	`danAI-55M-Reasoning-INT2.safetensors`	13.4 MB	~13 MB	Ultra-low 2-bit packed representation (4 weights/byte)

🚀 Quickstart & Inference

1. Ollama (1-Command Local Run)

# Download GGUF and Modelfile
huggingface-cli download asjadilahi/danAI-55M-Reasoning danAI-55M-Reasoning-Q8_0.gguf Modelfile --local-dir .

# Create and run with Ollama
ollama create danai -f Modelfile
ollama run danai "What is 5 + 7?"

2. llama.cpp / llama-cli

# Download any GGUF quant (e.g. Q8_0 or Q4_K_M)
huggingface-cli download asjadilahi/danAI-55M-Reasoning danAI-55M-Reasoning-Q8_0.gguf --local-dir .

# Run with llama-cli (>300 tokens/sec on Apple Silicon / CPU)
llama-cli -m danAI-55M-Reasoning-Q8_0.gguf -p "System: You are danAI, a helpful AI assistant.\n\nUser: Solve 25 * 4.\n\nAssistant: <think>" -n 128

3. Interactive Python Chat with Live Tool Calling

# Clone repository
git clone https://github.com/Asjad-Ilahi/danAI-55M.git
cd danAI-55M
pip install torch safetensors huggingface-hub tokenizers

# Run interactive assistant (automatically pulls weights from Hugging Face)
python scripts/chat.py

4. Standalone Quantized PyTorch Runner (INT8, INT4, INT2, BF16)

import torch
from tokenizers import Tokenizer
from safetensors.torch import load_file
from scripts.quantized_chat import load_quantized_model

# Load 8-bit, 4-bit, or 2-bit quantized safetensors
model, mode = load_quantized_model("hf_export/danAI-55M-Reasoning-INT8.safetensors")
tokenizer = Tokenizer.from_file("tokenizer/tokenizer.json")

prompt = "System: You are danAI, a helpful AI assistant.\n\nUser: What is 2 + 2?\n\nAssistant: <think>"
input_ids = torch.tensor([tokenizer.encode(prompt).ids], dtype=torch.long)

with torch.no_grad():
    for _ in range(30):
        logits = model(input_ids)[0]
        next_token = torch.argmax(logits[0, -1, :]).item()
        input_ids = torch.cat([input_ids, torch.tensor([[next_token]])], dim=1)
        if next_token in [0, tokenizer.token_to_id("<|endoftext|>")]:
            break

print(tokenizer.decode(input_ids[0].tolist()))

💎 Core Strengths

⚡ Ultra-Low 13 MB to 104 MB RAM Footprint:
- Runs smoothly on mobile chips, Apple Silicon, Raspberry Pi, and microcontrollers.
🛠️ Native Agentic Tool Calling (100% Invocation Rate):
- Automatically emits structured <tool_call> JSON blocks to offload exact multi-digit math to a calculator tool (123433 * 564332 = 69657191756) and live real-time queries to search_web.
💭 Chain-of-Thought (<think>) Step-by-Step Reasoning:
- Decomposes multi-step arithmetic, logic, and planning inside <think> tokens before emitting the final answer.
🥇 #1 in Direct Sub-100M Science Benchmarks:
- Decisively outperforms Pythia-70M across ARC-Easy (39.2% vs 37.4%), ARC-Challenge (25.2% vs 18.1%), and MMLU (27.4% vs 25.1%) while being 22% smaller.
🏆 Outperforms OpenAI GPT-2 Small (124M):
- Beats GPT-2 on ARC-Easy (39.2% vs 35.8%), ARC-Challenge (25.2% vs 21.4%), and MMLU (27.4% vs 26.2%) at less than half the memory.

🏆 Full-Dataset Benchmark Leaderboard

Evaluated across 100% of all official test and validation samples (>20,000+ test questions) against all major sub-150M open models:

Model	Active Params	Training Scale	GSM8K (Direct)	Agentic Tools	ARC-Challenge (Hard Science)	ARC-Easy (2,376 q)	ARC (Avg)	MMLU (1,520 q)	RAM Footprint	PIQA (1,838 q)
danAI-55M-Reasoning	54.5M	~3B tokens	3.0%	100.0% (Native)	25.2%	39.2%	32.2%	27.4%	13MB – 104 MB	56.1%
Pythia-70M (EleutherAI)	70M	300B tokens	0.0%	0.0%	18.1%	37.4%	27.8%	25.1%	140 MB	59.5%
GPT-2 Small (OpenAI)	124M	40B tokens	0.0%	0.0%	21.4%	35.8%	28.6%	26.2%	248 MB	63.3%
MobileLLM-125M (Meta AI)	125M	1,000B tokens	0.5%	0.0%	27.7%	45.5%	36.6%	-	250 MB	64.6%
SmolLM-135M (Hugging Face)	135M	600B tokens	1.0%	0.0%	-	-	42.4%	30.2%	270 MB	68.4%
SmolLM2-135M (Hugging Face)	135M	2,000B tokens	1.4%	0.0%	-	-	43.9%	31.5%	270 MB	68.4%

📐 Architecture Specifications

Model Name: danAI-55M-Reasoning
Total Parameters: 54,525,952 (54.5M)
Layers: 12 Transformer Blocks
Hidden Dimension: 512
Attention Heads: 8 Query Heads
KV Heads: 4 Key/Value Heads (Grouped Query Attention - GQA)
Head Dimension: 64
Intermediate Dimension: 1376 (SwiGLU MLP)
Vocab Size: 32,768 (Byte-Pair Encoding, Tied Embeddings)
Positional Embeddings: RoPE (Rotary Position Embeddings, Base theta=10000.0, NTK Extension factor=2.0)
Max Context Length: 2048 tokens
Memory Footprint: 13 MB (INT2) / 26 MB (INT4) / 45 MB (Q4_K_M) / 74 MB (Q8_0) / 104 MB (FP16/BF16)

🔗 Official Links & Resources

GitHub Repository: https://github.com/Asjad-Ilahi/danAI-55M
Hugging Face Hub: https://huggingface.co/asjadilahi/danAI-55M-Reasoning

📜 Citation & License

@misc{ilahi2026danai55m,
  author = {Asjad Ilahi},
  title = {danAI-55M-Reasoning: Ultra-Lightweight Agentic and Reasoning Language Model},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/asjadilahi/danAI-55M-Reasoning}}
}

License: Apache 2.0 (Open-source, commercial use permitted)
Author: Asjad Ilahi (@asjadilahi)

asjadilahi/danAI-55M-Reasoning