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

Libraries

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

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

pipe = pipeline("text-generation", model="SupraLabs/Supra2-Medium-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra2-Medium-Instruct")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra2-Medium-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra2-Medium-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra2-Medium-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use SupraLabs/Supra2-Medium-Instruct 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-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra2-Medium-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra2-Medium-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

LH-Tech-AI commited on about 20 hours ago

Commit

7e22157

verified ·

1 Parent(s): 3bf934d

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -14

README.md CHANGED Viewed

@@ -32,20 +32,6 @@ base_model:
 ---
-## **Get started with Ollama**
-```bash
-ollama run hf.co/SupraLabs/Supra2-Medium-Instruct:F16
-```
-### **Available GGUF quantizations**
-- F16: `ollama run hf.co/SupraLabs/Supra2-Medium-Instruct:F16`
-- Q8_0: `ollama run hf.co/SupraLabs/Supra2-Medium-Instruct:Q8_0`
-- Q4_K_M: `ollama run hf.co/SupraLabs/Supra2-Medium-Instruct:Q4_K_M`
----
 ## **Some samples...**
 Prompt: "How does ChatGPT work?"

 ---
 ## **Some samples...**
 Prompt: "How does ChatGPT work?"