Instructions to use ionos/Llama-3.3-70B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ionos/Llama-3.3-70B-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="ionos/Llama-3.3-70B-Instruct-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ionos/Llama-3.3-70B-Instruct-FP8")
model = AutoModelForCausalLM.from_pretrained("ionos/Llama-3.3-70B-Instruct-FP8", 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 ionos/Llama-3.3-70B-Instruct-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ionos/Llama-3.3-70B-Instruct-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ionos/Llama-3.3-70B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ionos/Llama-3.3-70B-Instruct-FP8

SGLang

How to use ionos/Llama-3.3-70B-Instruct-FP8 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 "ionos/Llama-3.3-70B-Instruct-FP8" \
    --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": "ionos/Llama-3.3-70B-Instruct-FP8",
		"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 "ionos/Llama-3.3-70B-Instruct-FP8" \
        --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": "ionos/Llama-3.3-70B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ionos/Llama-3.3-70B-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/ionos/Llama-3.3-70B-Instruct-FP8
```

mjochmann-ionos commited on Jul 22, 2025

Commit

8d421dd

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@@ -179,7 +179,7 @@ if __name__ == '__main__':
 ## Training and Optimization Details
 **Quantization Process:**
-This model employs SmoothQuant quantization implemented through LLM Compressor. SmoothQuant redistributes quantization difficulty from activations to weights by applying mathematically equivalent transformations, enabling effective FP8 quantization. The quantization calibration was performed using the WikiText-2 dataset. The quantization process specifically targets the weights and activations of linear operators within transformer blocks, preserving model accuracy while significantly reducing computational requirements.
 **Calibration Dataset:**
 - **WikiText**: Used for SmoothQuant calibration to optimize quantization parameters

 ## Training and Optimization Details
 **Quantization Process:**
+This model employs SmoothQuant quantization implemented through LLM Compressor. SmoothQuant redistributes quantization difficulty from activations to weights by applying mathematically equivalent transformations, enabling effective FP8 quantization. The quantization calibration was performed using the WikiText dataset. The quantization process specifically targets the weights and activations of linear operators within transformer blocks, preserving model accuracy while significantly reducing computational requirements.
 **Calibration Dataset:**
 - **WikiText**: Used for SmoothQuant calibration to optimize quantization parameters