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

Libraries

How to use SLM-Archive/LowOnMind-8M with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SLM-Archive/LowOnMind-8M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SLM-Archive/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": "SLM-Archive/LowOnMind-8M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SLM-Archive/LowOnMind-8M

SGLang

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

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

DedeProGames commited on 1 day ago

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@@ -12,6 +12,31 @@ tags:
   - pretrained-from-scratch
 ---
 # LowOnMind-8M
 A **8,060,256-parameter** decoder-only language model pre-trained

   - pretrained-from-scratch
 ---
+<div align="center">
+  <h3 align="center">Model archived by:</h3>
+  <table align="center">
+    <tr>
+      <td align="center" valign="middle" width="140">
+        <a href="https://huggingface.co/DedeProGames" target="_blank">
+          <img
+            src="https://cdn-avatars.huggingface.co/v1/production/uploads/685ea8ff7b4139b6845ce395/Im--QSnbrnAhHPPhpX8L0.png"
+            width="80"
+            height="80"
+            alt="DedeProGames"
+            style="display:block; margin:0 auto; border-radius:50%; object-fit:cover;"
+          />
+          <br />
+          <b>DedeProGames</b>
+        </a>
+      </td>
+    </tr>
+  </table>
+</div>
+---
 # LowOnMind-8M
 A **8,060,256-parameter** decoder-only language model pre-trained