Instructions to use aethertp/PicoLM-V2-81M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aethertp/PicoLM-V2-81M-Instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
llama cli -hf aethertp/PicoLM-V2-81M-Instruct

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
llama cli -hf aethertp/PicoLM-V2-81M-Instruct

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
./llama-cli -hf aethertp/PicoLM-V2-81M-Instruct

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf aethertp/PicoLM-V2-81M-Instruct
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aethertp/PicoLM-V2-81M-Instruct

Use Docker

docker model run hf.co/aethertp/PicoLM-V2-81M-Instruct

LM Studio
Jan

vLLM

How to use aethertp/PicoLM-V2-81M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aethertp/PicoLM-V2-81M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aethertp/PicoLM-V2-81M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aethertp/PicoLM-V2-81M-Instruct

Ollama
How to use aethertp/PicoLM-V2-81M-Instruct with Ollama:
```
ollama run hf.co/aethertp/PicoLM-V2-81M-Instruct
```
Unsloth Desktop
Docker Model Runner
How to use aethertp/PicoLM-V2-81M-Instruct with Docker Model Runner:
```
docker model run hf.co/aethertp/PicoLM-V2-81M-Instruct
```

Lemonade

How to use aethertp/PicoLM-V2-81M-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aethertp/PicoLM-V2-81M-Instruct

Run and chat with the model

lemonade run user.PicoLM-V2-81M-Instruct-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

PicoLM-V2-81M-Instruct / README.md

aethertp

Add benchmark methodology disclosure

d9dfb2f verified 14 days ago

preview code

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---
language:
- en
license: apache-2.0
tags:
- text-generation
- conversational
- pytorch
- safetensors
- gguf
- causal-lm
- slm
- on-device
- mobilellm
- layer-sharing
pipeline_tag: text-generation
widget:
- text: "<|im_start|>user\nWhat is the capital of France?<|im_end|>\n<|im_start|>assistant\n"
---
# 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.

---

## 📌 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

---

## 📊 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-V2-81M (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)* | — | — |
| **Validation Loss** | ~10.11 | 2.68 *(16k)* | **2.78** *(24k)* | — | — |
| **Factual QA ("Capital of France")** | Hallucination | Short | **"The capital of France is Paris."** | Factual | Factual |

---

## 💻 Quickstart (Transformers Native)

```python
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]:]))
```

---

## 🙏 Acknowledgments

Special thanks to **Andrew Thompson ([@AndrewThompson1233](https://huggingface.co/AndrewThompson1233))** and the **[Maba Architecture Project](https://huggingface.co/AndrewThompson1233/maba-v2-architecture)** for invaluable discussions, architectural insights on layer recycling, and residual scaling dampening that helped shape the V2 jump (+16.4% on ARC-Easy).



> **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**.

	---
	language:
	- en
	license: apache-2.0
	tags:
	- text-generation
	- conversational
	- pytorch
	- safetensors
	- gguf
	- causal-lm
	- slm
	- on-device
	- mobilellm
	- layer-sharing
	pipeline_tag: text-generation
	widget:
	- text: "<\|im_start\|>user\nWhat is the capital of France?<\|im_end\|>\n<\|im_start\|>assistant\n"
	---

	# 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.

	---

	## 📌 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

	---

	## 📊 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-V2-81M (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) \| — \| — \|
	\| Validation Loss \| ~10.11 \| 2.68 (16k) \| 2.78 (24k) \| — \| — \|
	\| Factual QA ("Capital of France") \| Hallucination \| Short \| "The capital of France is Paris." \| Factual \| Factual \|

	---

	## 💻 Quickstart (Transformers Native)

	```python
	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]:]))
	```

	---

	## 🙏 Acknowledgments

	Special thanks to Andrew Thompson ([@AndrewThompson1233](https://huggingface.co/AndrewThompson1233)) and the [Maba Architecture Project](https://huggingface.co/AndrewThompson1233/maba-v2-architecture) for invaluable discussions, architectural insights on layer recycling, and residual scaling dampening that helped shape the V2 jump (+16.4% on ARC-Easy).



	> 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.