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

Commit

537b7d7

verified ·

1 Parent(s): cd197e3

Update README.md

Browse files

Files changed (1) hide show

README.md +52 -0

README.md CHANGED Viewed

@@ -1,3 +1,55 @@
 ---
 license: apache-2.0
 ---

 ---
 license: apache-2.0
+datasets:
+- HuggingFaceFW/fineweb-edu
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- small
+- cpu
+- supra
+- tiny
+- mini
+- open
+- open-source
 ---
+# 🦅 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`.
+| Task          | Value        | Rating     |
+| :------------ | :----------: | ---------: |
+| Arc_Easy      | 0.2x         | RATING IN WORDS HERE |
+| Wikitext      | xx           | RATING IN WORDS HERE |
+| BLiMP         | 5x           | RATING IN WORDS HERE |
+## Examples
+**Prompt:** PROMPT_HERE<br>
+**Output:**: OUTPUT_HERE
+<br><br>
+**Prompt:** PROMPT_HERE<br>
+**Output:**: OUTPUT_HERE
+<br><br>
+**Prompt:** PROMPT_HERE<br>
+**Output:**: OUTPUT_HERE
+## Usage
+To use our model, just run this code using HF Transformers to execute the model:
+```python3
+### CODE HERE
+```
+## Training guide
+We trained Supra Mini 0.1M on a single T4 GPU in ~45 minutes for 2 epochs.<br>
+The full training code can be found in this repo as `train_tokenizer.py` (train costum BPE tokenizer with vocab size of 250), `train.py` (train the model) and `inference.py` (test the model).<br>
+The model was trained on the first 500 million tokens of Sample-10BT from Fineweb-Edu using streaming tokenization.
+## Final thoughts
+As the new founded organization **SupraLabs**, we are proud the introduce our first Tiny-LLM to prove that our pipeline is running.<br>
+More models will release soon...