🚀 Major Update (September 2026): PicoLM-V2-81M-Instruct is officially released! Featuring 36 layers of computational depth (MobileLLM-LS), a 24k vocabulary, and a massive +16.4% gain on ARC-Easy (reaching 42.00%). We strongly recommend using V2!
PicoLM-80M-Instruct 🚀
PicoLM-80M-Instruct is an ultra-compact, 80.24-million parameter causal language model designed for extreme efficiency, fast inference, and on-device deployment.
Trained completely from scratch on Kaggle dual Tesla T4 GPUs with zero budget, PicoLM-80M proves what can be achieved through strict modern architecture optimizations (SwiGLU, Grouped-Query Attention, RMSNorm, QK-Norm, and Tied Embeddings) paired with dense educational synthetic data.
📌 Model Overview
- Developer: Emre Polat
- Parameters: 80,242,240 (~80.2M)
- Context Window: 2,048 tokens
- Vocabulary: 16,384 (Single-digit regex split, Byte-level BPE)
- Format: Safetensors (FP16) & GGUF
- Primary Language: English + Python Code
- License: Apache 2.0
📊 Empirical Benchmark Results (Verified)
All scores below were empirically measured directly on the model weights using standard log-likelihood evaluations:
| Benchmark / Task | Random Baseline | SmolLM2-135M (HF) | Gemma 3 270M (Google) | PicoLM-80M-Instruct (Ours) |
|---|---|---|---|---|
| HellaSwag (Commonsense) | 25.00% | 42.10% | 37.70% | 31.20% (+6.2% above random) |
| ARC-Easy (Science QA) | 25.00% | 58.50% | 57.70% | 25.60% (Floor effect) |
| Validation Perplexity | ~16,384 | — | — | 14.65 |
| Factual QA ("Capital of France") | Hallucination | Factual | Factual | "The capital of France is Paris." |
| Stop Token Discipline | Loops | Strict | Strict | **100% strict `< |
💻 Quickstart (Transformers Native)
You can load and chat with PicoLM directly via Hugging Face transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "aethertp/PicoLM-80M-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=50, temperature=0.6, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:]))
⚠️ Limitations
- Factual Depth: With 80M parameters, the model cannot serve as a comprehensive encyclopedia. Factual queries should be supported by RAG.
- Multi-step Math: Elementary arithmetic works, but complex multi-variable algebra requires external verification.