---
language:
- en
license: apache-2.0
tags:
- picolm
- slm
- on-device
- custom_code
- causal-lm
- text-generation
pipeline_tag: text-generation
---

# PicoLM-V3-82M-Instruct

<p align="center">
  <b>PicoLM Project</b> • Ultra-Compact 82.2M On-Device Language Model
</p>

## Overview

**PicoLM-V3-82M-Instruct** is an ultra-compact, 82.2-million parameter causal language model designed specifically for edge devices, microcontrollers, and low-latency local inference. Developed entirely from scratch within the open-source **PicoLM Project**, it combines recurrent weight-tied transformer blocks with factorized low-rank linear embeddings to maximize knowledge density per parameter.

PicoLM-V3 Standard is specialized for **natural dialogue, commonsense reasoning, and concise daily interactions** within a native 2,048-token context window.

---

## Model Specifications

| Attribute | Specification |
| :--- | :--- |
| **Total Parameters** | 82,233,792 unique parameters (tied I/O embeddings, 0 dead weights) |
| **Physical Transformer Blocks** | 21 layers |
| **Effective Layers (Recurrent Pass)** | 42 effective layers ($21 \times 2$ macro-loop passes) |
| **Hidden Dimension ($d_{\text{model}}$)** | 576 |
| **Attention Architecture** | Grouped-Query Attention (9 Query Heads, 3 KV Heads; GQA 3:1) |
| **Head Dimension ($d_{\text{head}}$)** | 64 |
| **Feed-Forward Dimension ($d_{\text{ffn}}$)** | 1,664 (SwiGLU activation) |
| **Factorized Embedding Projection** | $24,576 \rightarrow 128 \rightarrow 576$ (Rank-128 linear bottleneck) |
| **Normalization** | Pre-LN RMSNorm ($\epsilon = 10^{-5}$) with split-pass independent gains |
| **Positional Encoding** | Rotary Position Embeddings (RoPE, $\theta = 10,000.0$) |
| **Context Length** | 2,048 tokens native sequence length |
| **Vocabulary Size** | 24,576 BPE tokens |

---


## Standardized Evaluation & Generational Progress

All models evaluated strictly using the **EleutherAI LM-Evaluation-Harness** standard:

| Model | Parameters | Tokens | Context | Legacy Scale (250 Q, Raw) | Official ARC-Easy (`acc_norm`) | PIQA (`acc_norm`) | HellaSwag (`acc_norm`) | ARC-Challenge (`acc_norm`) |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **PicoLM-V3 (Standard)** | **82.2M** | **758M** | **2,048** | **44.80%** | **39.32%** | **58.49%** | **36.00%** | **23.63%** |
| PicoLM-V3-Pro (Flagship)| 82.2M | 1.62B | 4,096 | 46.00% | 43.54% (Base) / 39.10% | 57.89% | 34.95% | 24.06% |
| PicoLM-V2.1-Instruct | 81.9M | ~380M | 2,048 | 42.00%* | *(legacy 250Q only)* | ~56.5% | ~32.0% | ~22.5% |
| PicoLM-V2-Instruct | 81.9M | ~380M | 2,048 | 42.00%* | *(legacy 250Q only)* | ~56.5% | ~32.0% | ~22.5% |
| GPT-2 (OpenAI) | 124M | ~10B | 1,024 | - | 31.40% | 62.80% | 31.50% | 22.10% |
| MobileLLM-125M (Meta) | 125M | 1.0T | 2,048 | - | 43.90% | 65.30% | 38.90% | 27.10% |
| SmolLM2-135M (HF) | 135M | 2.0T | 8,192 | - | 43.90% | 68.40% | 42.10% | 30.20% |

> *\*PicoLM-V2 and V2.1 were evaluated exclusively on the legacy unnormalized 250-question sample. On the exact same 250-question scale, PicoLM-V3 outperforms V2 by +2.8 points (44.80% vs 42.00%).*


## Pre-Training & SFT Pipeline

### 1. Pre-Training (Part B - 758M Tokens)
- **Data Mixture:** Cosmopedia v2, FineWeb-Edu (score $\ge 4$), Python-Edu, and curated synthetic QA.
- **Optimization:** AdamW ($\beta_1=0.9, \beta_2=0.95$, weight decay 0.1), Cosine decay schedule to $1.0 \times 10^{-5}$, FP16 mixed precision.

### 2. Four-Pillar Supervised Fine-Tuning (SFT-Gold)
Aligned over 9,796 strictly filtered instruction conversations with assistant-only loss masking:
1. **Python Algorithmic Code (3,000 samples):** Clean Python functions covering sorting, string manipulation, and list algorithms.
2. **Natural Science & Trivia (2,500 samples):** Un-templated, direct QA on general facts and physical sciences.
3. **Reasoning Proofs (2,000 samples):** Step-by-step math problems formatted with `<thought>` scratchpads.
4. **Natural Everyday Conversation (2,200 samples):** Multi-turn chitchat, greetings, and assistant interaction etiquette.
5. **Anti-Sycophancy & Boundary Anchors (140 samples):** Strict model identity assertions, polite refusals of impossible future predictions.

---

## Quickstart & Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "aethertp/PicoLM-V3-82M-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float32,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "There are 5 baskets. Each basket contains 6 pears. If 4 pears rot, how many good pears are left?"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=80,
        do_sample=False,
        use_cache=False,
        eos_token_id=2
    )

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response.strip())
```

---

## Limitations & Ethical Considerations

- **Scale Constraints:** At 82.2M parameters, complex multi-step mental arithmetic without external scratchpads or tool access can hallucinate calculation steps.
- **Language Coverage:** The model is optimized strictly for English text and code.
- **Decontamination:** Training datasets were not decontaminated against benchmark test sets.
- **No Native Tool Use:** Cannot browse the web or execute live code.

---

## Citation

```bibtex
@misc{picolm2026v3,
  author = {Emre Polat and PicoLM Project Contributors},
  title = {PicoLM-V3: Efficient Sub-100M On-Device Language Modeling},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/aethertp/PicoLM-V3-82M-Instruct}}
}
```