Instructions to use DedeProGames/LowOnMind-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/LowOnMind-8M with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/LowOnMind-8M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/LowOnMind-8M")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/LowOnMind-8M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DedeProGames/LowOnMind-8M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/LowOnMind-8M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/LowOnMind-8M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DedeProGames/LowOnMind-8M

SGLang

How to use DedeProGames/LowOnMind-8M 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 "DedeProGames/LowOnMind-8M" \
    --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": "DedeProGames/LowOnMind-8M",
		"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 "DedeProGames/LowOnMind-8M" \
        --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": "DedeProGames/LowOnMind-8M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DedeProGames/LowOnMind-8M with Docker Model Runner:
```
docker model run hf.co/DedeProGames/LowOnMind-8M
```

LowOnMind-8M

A 8,060,256-parameter decoder-only language model pre-trained from scratch on fineweb-edu, using the NanoDex Trainer Space.

Architecture

A standard LlamaForCausalLM decoder-only transformer — SiLU MLP, RMSNorm, rotary position embeddings, grouped-query attention, tied embeddings, no biases — scaled down in width and depth to fit the parameter budget.


Parameters	8,060,256
Hidden size	288
Layers	9
Attention heads	9 (KV: 3)
FFN size	704
Context length	512
Vocab	2,048 (custom BPE trained on fineweb-edu)

Training


Tokens seen	199,753,728
Steps	381
Tokens / step	524,288
Optimizer	AdamW(0.9, 0.95) wd=0.1 clip=1.0
LR schedule	warmup 2% + cosine to 10% (peak 1e-03)
Final loss	3.8884 (ppl 48.8)
Wall time	29.3 min
Trained by	@DedeProGames

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("DedeProGames/LowOnMind-8M")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/LowOnMind-8M")

ids = tok("The mitochondria is", return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_new_tokens=60, do_sample=True,
                                temperature=0.8, top_k=50)[0]))

Caveats

This is a nano-scale research artifact. At this parameter count and token budget the model learns word shapes, common collocations and a little syntax — it is not a useful assistant and its output is not factual. It exists to make "pre-train a transformer from scratch" something you can actually watch happen.

Downloads last month: 456

Safetensors

Model size

8.06M params

Tensor type

F32

Dataset used to train DedeProGames/LowOnMind-8M

Collection including DedeProGames/LowOnMind-8M

LowOnMind

Collection

Small decoder-only models trained in small amount of tokens • 4 items • Updated 1 day ago • 2