aethertp/PicoLM-V2.1-81M-Instruct

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.

📌 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

📊 Empirical Benchmark Results (Verified)

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

🛠️ V2.1 Alignment Upgrades

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

💻 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]:]))

📱 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

Benchmark Methodology Note: The 42.00% ARC-Easy score reported for PicoLM-V2 / V2.1 was evaluated on a legacy, non-standardized 250-question unnormalized sample. Standardized, full-suite evaluations under the official EleutherAI LM-Evaluation-Harness (character-normalized) were introduced starting with PicoLM-V3.