PicoLM-V2-81M-Instruct

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.

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📌 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

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📊 Empirical Benchmark Results (Verified)

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

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💻 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]:]))

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🙏 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).