Instructions to use SupraLabs/Supra2-Medium-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra2-Medium-Base with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra2-Medium-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra2-Medium-Base")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra2-Medium-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SupraLabs/Supra2-Medium-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra2-Medium-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra2-Medium-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra2-Medium-Base

SGLang

How to use SupraLabs/Supra2-Medium-Base 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/Supra2-Medium-Base" \
    --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/Supra2-Medium-Base",
		"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/Supra2-Medium-Base" \
        --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/Supra2-Medium-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SupraLabs/Supra2-Medium-Base with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra2-Medium-Base
```

LH-Tech-AI commited on 1 day ago

Commit

a7534a1

verified ·

1 Parent(s): d4dd533

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -53,7 +53,7 @@ All benchmarks were evaluated using the EleutherAI LM-Eval Harness.
 | Model | PIQA (acc\_norm) | HellaSwag (acc\_norm) | ARC-Easy (acc\_norm) | ARC-Challenge (acc\_norm) |
 | ----- | :---: | :---: | :---: | :---: |
 | Supra-50M-Base (50M) | 0.62 | 0.32 | 0.46 | 0.25 |
-| **Supra2-Medium-Base (25M)** | **29.29** | **59.14** | **41.84** | **23.72** |
 | Supra2-100M-Base (100M) | 0.65 | 0.36 | 0.48 | 0.25 |
 **Final Train Loss: 3.2469** (no eval loss available)

 | Model | PIQA (acc\_norm) | HellaSwag (acc\_norm) | ARC-Easy (acc\_norm) | ARC-Challenge (acc\_norm) |
 | ----- | :---: | :---: | :---: | :---: |
 | Supra-50M-Base (50M) | 0.62 | 0.32 | 0.46 | 0.25 |
+| **Supra2-Medium-Base (25M)** | **59.14** | **29.29** | **41.84** | **23.72** |
 | Supra2-100M-Base (100M) | 0.65 | 0.36 | 0.48 | 0.25 |
 **Final Train Loss: 3.2469** (no eval loss available)