PicoLM-V2.1-81M-Instruct 🚀

PicoLM-V2.1-81M-Instruct is the targeted alignment release of the PicoLM architecture, engineered with MobileLLM-LS (Immediate Block-wise Layer Sharing).

Operating with an effective computational depth of 36 layers across an 81.86-million parameter footprint, PicoLM-V2.1 incorporates surgical instruction tuning with synthetic algorithmic scratchpads, explicit persona alignment, and targeted commonsense repairs.

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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, Atomic <thought> tags)
• 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:

Benchmark / Task	Random Baseline	PicoLM-80M (V1)	PicoLM-V2.1-81M (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)	—	—
Identity Alignment	Hallucinated	Generic	"I am PicoLM-V2.1, developed by Emre Polat."	Corporate	Corporate
Algorithmic Python	Broken Parity	Syntax only	Clean Recursive Factorial Execution	Working	Working
Stop Token Discipline	Loops	Strict	**100% strict `<	im_end	>` termination**

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🛠️ V2.1 Alignment Upgrades

1. Explicit Identity & Persona: Aligned to correctly identify as PicoLM-V2.1, created by Emre Polat, avoiding generic synthetic hallucination loops.
2. Algorithmic Recursion Repairs: Fixed mathematical parity confusion in recursive Python code generation (factorial recursive inductive steps verified).
3. Biological & Ontological Grounding: Eliminated semantic category bleeding (cats/dogs accurately identified as felines/canines with distinct traits).
4. Scratchpad Arithmetic Traces: Multi-step arithmetic reasoning traces embedded directly into post-training representations.

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💻 Quickstart (Transformers Native)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "aethertp/PicoLM-V2.1-81M-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).cuda()

messages = [{"role": "user", "content": "Hello! Who are you?"}]
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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📱 Mobile Deployment (GGUF)

PicoLM-V2.1 runs out of the box on mobile devices via PocketPal AI and MobAI:

• File: picolm-v2.1-81m-instruct-fp16.gguf
• Memory Footprint: ~175 MB RAM
• Mobile Throughput: ~40-45 tokens/sec