Instructions to use amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid with Transformers:

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

pipe = pipeline("text-generation", model="amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid", dtype="auto")

Transformers.js

How to use amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid');

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid

SGLang

How to use amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid 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 "amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid" \
    --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": "amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid",
		"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 "amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid" \
        --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": "amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid with Docker Model Runner:
```
docker model run hf.co/amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid
```

amd/SmolLM2-135M-Instruct-onnx-ryzenai-1.7-hybrid

Introduction

This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing.
Quantization Strategy
- AWQ / Group 128 / Asymmetric / UINT4 Weights / BFP16 activations
- Excluded Layers: None
Quick Start