Instructions to use PleIAs/Monad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PleIAs/Monad with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PleIAs/Monad")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PleIAs/Monad")
model = AutoModelForCausalLM.from_pretrained("PleIAs/Monad", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PleIAs/Monad with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PleIAs/Monad

SGLang

How to use PleIAs/Monad with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PleIAs/Monad" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PleIAs/Monad",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PleIAs/Monad" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PleIAs/Monad",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PleIAs/Monad with Docker Model Runner:
```
docker model run hf.co/PleIAs/Monad
```

Monad

File size: 2,921 Bytes

---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- transformers
library_name: transformers
---

# ⚛️ Monad

<div align="center">
  <img src="figures/pleias.jpg" width="60%" alt="Pleias" />
</div>

<p align="center">
  <a href="https://pleias.fr/blog/blogsynth-the-new-data-frontier"><b>Blog announcement</b></a>
</p>

**Monad** is a 56 million parameters generalist Small Reasoning Model, trained on 200 billions tokens from <a href="https://huggingface.co/PleIAs/Baguettotron">SYNTH</a>, a fully open generalist dataset.

As of 2025, Monad is the best contender for the smallest viable language models. Despite being less than half of gpt-2, Monad not only answers in consistent English but performs significanly beyond chance on MMLU and other major industry benchmarks.

<p align="center">
  <img width="80%" src="figures/training_efficiency.jpeg">
</p>

Monad's name is a reference to Leibniz concept and general idea of the smallest possible unit of intelligence.

## Features
Monad has been natively trained for instructions with thinking traces. We implemented a series of dedicated pipelines for:
* Memorization of encyclopedic knowledge (50,000 vital articles from Wikipedia), though in this size range hallucinations have to be expected.
* Retrieval-Augmented Generation with grounding (following on our initial experiments with Pleias-RAG series)
* Arithmetic and simple math resolution problem
* Editing tasks
* Information extraction
* Creative writing, including unusual synthetic exercises like lipograms or layout poems.

Monad is strictly monolingual in English. We trained a new custom tokenizer (likely one of the smallest tokenizer to date, less than 8,000 individual tokens), exclusively trained on SYNTH so that we maintain a relatively good compression ratio. 

## Model design and training
Monad is a 56M parameters decoders with a standard Qwen/Llama-like design, except for its extremely compact size and overall opiniated architecture for depth (with 64 layers)
<p align="center">
  <img width="80%" src="figures/monad_structure.png">
</p>

Monad was trained on 16 h100 from Jean Zay (compute plan n°A0191016886). Full pre-training took a bit less than 6 hours.

## Evaluation
Monad attains performance on MMLU significantly beyond chance with close to 30% of positive rate. We also find non-random results on gsm8k (8%) and HotPotQA (8%)

To our knowledge, there is no model remotely close in this size range for evaluation comparison. Spiritually and practically, Monad remains unique.

## Use and deployment
Monad has been trained on the standard instruction style from Qwen.

```xml
<|im_start|>user
Who are you?<|im_end|>
<|im_start|>assistant
<think>
```

Monad has no support yet for multi-turn.

A major envisioned use case for Monad is explainability, as the model does provide a unique trade-off between observability and actual reasoning performance.