Instructions to use SLM-Archive/LowOnMind-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SLM-Archive/LowOnMind-1M with Transformers:

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

pipe = pipeline("text-generation", model="SLM-Archive/LowOnMind-1M", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SLM-Archive/LowOnMind-1M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SLM-Archive/LowOnMind-1M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SLM-Archive/LowOnMind-1M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SLM-Archive/LowOnMind-1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SLM-Archive/LowOnMind-1M

SGLang

How to use SLM-Archive/LowOnMind-1M 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 "SLM-Archive/LowOnMind-1M" \
    --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": "SLM-Archive/LowOnMind-1M",
		"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 "SLM-Archive/LowOnMind-1M" \
        --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": "SLM-Archive/LowOnMind-1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SLM-Archive/LowOnMind-1M with Docker Model Runner:
```
docker model run hf.co/SLM-Archive/LowOnMind-1M
```

LowOnMind-1M / model.safetensors

DedeProGames

LowOnMind-1M — 985,152 params, 200M tokens fineweb-edu, val ppl 19.90

5082d41 verified about 1 month ago

History Blame Contribute Delete

3.95 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 7cdc4d9582128cf4ba070f4a010d0993d84532c99c8c08941c16ca4061a10a2d
Size of remote file:: 3.95 MB
SHA256:: 80316e2dd44f8262c957a7957e1bf6b680c43f84f6efd75f1fbecbb5b769ce73

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.