Instructions to use aethertp/PicoLM-V2-81M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aethertp/PicoLM-V2-81M-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
llama cli -hf aethertp/PicoLM-V2-81M-Instruct

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
llama cli -hf aethertp/PicoLM-V2-81M-Instruct

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
./llama-cli -hf aethertp/PicoLM-V2-81M-Instruct

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aethertp/PicoLM-V2-81M-Instruct

Use Docker

docker model run hf.co/aethertp/PicoLM-V2-81M-Instruct

LM Studio
Jan

vLLM

How to use aethertp/PicoLM-V2-81M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aethertp/PicoLM-V2-81M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aethertp/PicoLM-V2-81M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aethertp/PicoLM-V2-81M-Instruct

Ollama
How to use aethertp/PicoLM-V2-81M-Instruct with Ollama:
```
ollama run hf.co/aethertp/PicoLM-V2-81M-Instruct
```
Unsloth Desktop
Docker Model Runner
How to use aethertp/PicoLM-V2-81M-Instruct with Docker Model Runner:
```
docker model run hf.co/aethertp/PicoLM-V2-81M-Instruct
```

Lemonade

How to use aethertp/PicoLM-V2-81M-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aethertp/PicoLM-V2-81M-Instruct

Run and chat with the model

lemonade run user.PicoLM-V2-81M-Instruct-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

PicoLM-V2-81M-Instruct 🚀

PicoLM-V2-81M-Instruct is an ultra-compact, 81.86-million parameter language model engineered with MobileLLM-LS (Immediate Block-wise Layer Sharing).

By passing token representations through 18 physical Transformer blocks twice, PicoLM-V2 achieves an effective computational depth of 36 layers (deeper than Llama-3-8B's 32 layers) while maintaining a lightweight ~170MB memory footprint.

Trained completely from scratch on Kaggle dual Tesla T4 GPUs with zero budget, PicoLM-V2 decisively shatters the sub-100M performance floor.

📌 Model Overview

Developer: Emre Polat
Physical Parameters: 81,861,696 (~81.86M)
Computational Depth: 36 Layers (18 physical blocks $\times$ 2 passes)
Context Window: 2,048 tokens
Vocabulary: 24,576 (Single-digit regex split, Byte-level BPE)
Format: Safetensors (FP16) & GGUF
License: Apache 2.0

📊 Empirical Benchmark Results (Verified)

All scores below were empirically measured directly on the model weights using standardized log-likelihood evaluations:

Benchmark / Task	Random Baseline	PicoLM-80M (V1)	PicoLM-V2-81M (Ours)	Gemma 3 270M (Google)	SmolLM2-135M (HF)
ARC-Easy (Science QA)	25.00%	25.60% (Floor)	42.00% (+16.4%)	57.70%	58.50%
HellaSwag (Commonsense)	25.00%	31.20%	34.40% (+3.2%)	37.70%	42.10%
Validation Perplexity	~24,576	14.65 (16k)	16.08 (24k)	—	—
Validation Loss	~10.11	2.68 (16k)	2.78 (24k)	—	—
Factual QA ("Capital of France")	Hallucination	Short	"The capital of France is Paris."	Factual	Factual

💻 Quickstart (Transformers Native)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "aethertp/PicoLM-V2-81M-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).cuda()

messages = [{"role": "user", "content": "What is the capital of France?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=60, temperature=0.6, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:]))

🙏 Acknowledgments

Special thanks to Andrew Thompson (@AndrewThompson1233) and the Maba Architecture Project for invaluable discussions, architectural insights on layer recycling, and residual scaling dampening that helped shape the V2 jump (+16.4% on ARC-Easy).

Downloads last month: 225

Safetensors

Model size

96M params

Tensor type

F16