Instructions to use SupraLabs/Supra-Mini-0.1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra-Mini-0.1M with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra-Mini-0.1M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-Mini-0.1M")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-Mini-0.1M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SupraLabs/Supra-Mini-0.1M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra-Mini-0.1M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Mini-0.1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra-Mini-0.1M

SGLang

How to use SupraLabs/Supra-Mini-0.1M 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 "SupraLabs/Supra-Mini-0.1M" \
    --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": "SupraLabs/Supra-Mini-0.1M",
		"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 "SupraLabs/Supra-Mini-0.1M" \
        --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": "SupraLabs/Supra-Mini-0.1M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SupraLabs/Supra-Mini-0.1M with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra-Mini-0.1M
```

LH-Tech-AI commited on 12 days ago

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@@ -19,6 +19,19 @@ tags:
 # 🦅 Supra Mini 0.1M
 Supra Mini 0.1M is a very tiny base model trained on 500 million tokens of Fineweb-Edu for 2 epochs to prove how well very tiny models can perform on world knowledge.
 ## Benchmarks
 All benchmarks were executed using `lm-eval`.

 # 🦅 Supra Mini 0.1M
 Supra Mini 0.1M is a very tiny base model trained on 500 million tokens of Fineweb-Edu for 2 epochs to prove how well very tiny models can perform on world knowledge.
+# Model Config
+- Parameters: 117,648 (0.1M)
+- Architecture: Llama
+- Vocab size with custom BPE tokenizer: 250
+- Hidden Size: 48
+- Intermediate Size: 96
+- Hidden Layers: 4
+- Attention Heads: 4
+- Max Position Embeddings: 256
+- Learning rate: 6e-4
+- Weight Decay: 0.01
 ## Benchmarks
 All benchmarks were executed using `lm-eval`.