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.

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📌 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

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📊 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 `<

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💻 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]:]))

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⚠️ 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.