Instructions to use omurberaisik/NoTokenLM-MicroGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omurberaisik/NoTokenLM-MicroGen with Transformers:

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

pipe = pipeline("text-generation", model="omurberaisik/NoTokenLM-MicroGen")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("omurberaisik/NoTokenLM-MicroGen", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use omurberaisik/NoTokenLM-MicroGen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omurberaisik/NoTokenLM-MicroGen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omurberaisik/NoTokenLM-MicroGen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/omurberaisik/NoTokenLM-MicroGen

SGLang

How to use omurberaisik/NoTokenLM-MicroGen 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 "omurberaisik/NoTokenLM-MicroGen" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omurberaisik/NoTokenLM-MicroGen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "omurberaisik/NoTokenLM-MicroGen" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omurberaisik/NoTokenLM-MicroGen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use omurberaisik/NoTokenLM-MicroGen with Docker Model Runner:
```
docker model run hf.co/omurberaisik/NoTokenLM-MicroGen
```

NoTokenLM · MicroGen series

Tokenizer-free, byte-level language models, trained entirely from scratch

What is this?

MicroGen is a research series of tiny, from-scratch language models — ranging from about 1,900 to 500,000 parameters — trained directly on raw UTF-8 bytes, with no tokenizer at all. This is a research project, not a product: the goal is to find out how far a model can be pushed toward real language behavior while staying this small, and to document that process honestly at every step.

Each version builds on the previous one by changing roughly one thing at a time: first depth and attention, then optimizer, then data quality, then multilinguality. Read together, the series works as a practical log of small-scale language model training — including the mistakes.

All models live in this single repository. Each is a separate checkpoint; the model picker below lets you choose which one to load and use.

This is a research artifact, not an assistant. None of these models are instruction-tuned or aligned in any way — they are raw next-byte predictors. See the honest limitations section before you draw any conclusions from their output.

Models

Model	Parameters	Architecture summary	Context	Languages
2.5	~1.9K	Single layer, single attention head	96 bytes	EN
2.6	~15K	3 layers, RoPE + SwiGLU, multi-head attention	128 bytes	EN
2.7	~60K	4 layers, Muon optimizer + Flash Attention	128 bytes	EN
3.5	~345K	5 layers, WSD scheduler, bf16, FineWeb-Edu	256 bytes	EN
3.6	~500K	6 layers, trained with a focus on meaning/topic coherence	256 bytes	EN
3.7	~500K	Same architecture as 3.6, multilingual data	256 bytes	EN, ES, ID, IT

All models: byte-level input (vocab size 256, no tokenizer), causal decoder-only, trained from random initialization — no pretrained weights were used anywhere in this series.

Through 3.6, every model in this series is single-language (English). 3.7 is the first and only multilingual model in the series.

Data budgets were targets, not guarantees. With the exception of 2.5 (which did complete its 20GB target), none of the other models finished the amount of data they were originally budgeted for — training was stopped earlier for practical reasons (time/compute), so treat the "target data" figures below as an upper bound the model was moving toward, not a completed run.

MicroGen 2.5

~1,925 parameters. The smallest model in the series — a single attention head, a single layer, d_model=5. Targeted 20GB of FineWeb data in streaming mode, and is the only model in the series that actually completed its full data target. This model exists purely to probe whether a model at this scale can produce anything beyond noise — it is not expected to produce coherent text.