Instructions to use amd/PARD-Qwen3-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/PARD-Qwen3-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="amd/PARD-Qwen3-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amd/PARD-Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained("amd/PARD-Qwen3-0.6B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amd/PARD-Qwen3-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/PARD-Qwen3-0.6B"
# 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/PARD-Qwen3-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/PARD-Qwen3-0.6B

SGLang

How to use amd/PARD-Qwen3-0.6B 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/PARD-Qwen3-0.6B" \
    --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/PARD-Qwen3-0.6B",
		"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/PARD-Qwen3-0.6B" \
        --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/PARD-Qwen3-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/PARD-Qwen3-0.6B with Docker Model Runner:
```
docker model run hf.co/amd/PARD-Qwen3-0.6B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation

| Paper | Github | Blog |

Introduction

PARD is a high-performance speculative decoding method that also enables low-cost adaptation of autoregressive draft models into parallel draft models. It offers the following advantages:

Low-Cost Training: PARD adapts AR (autoregressive) draft models into parallel draft models with minimal overhead. Compared to pure AR draft models, PARD achieves an average inference speedup of 1.78×. By introducing a conditional drop-token strategy, PARD improves training efficiency by up to 3× while maintaining the same level of accuracy.
Generalizability: Thanks to its target-independent design, a single PARD draft model can accelerate an entire family of target models. This contrasts with target-dependent approaches such as Medusa and EAGLE, which require retraining or tuning for each new target. As a result, PARD significantly reduces both deployment complexity and adaptation cost.
High Performance: When integrated into an optimized inference framework called Transformers+ PARD delivers up to a 4.08× speedup, with LLaMA3.1 8B reaches a state-of-the-art 311.5 tokens per second. When integrated into vLLM, PARD delivers up to 3.06× speedup, outperforming other speculative decoding methods in vLLM by 1.51×.

AR and AR+ represent baseline auto-regressive generation using Transformers and Transformers+, respectively. VSD denotes vanilla speculative decoding. PARD refers to the proposed method in this work.

Model Weights

Model Series	Model Name	Download
llama3	PARD-Llama-3.2-1B	🤗 HuggingFace
DSR Qwen	PARD-DeepSeek-R1-Distill-Qwen-1.5B	🤗 HuggingFace
Qwen	PARD-Qwen2.5-0.5B	🤗 HuggingFace

How To Use

Please visit PARD repo for more information

Citation

@article{an2025pard,
  title={PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation},
  author={An, Zihao and Bai, Huajun and Liu, Ziqiong and Li, Dong and Barsoum, Emad},
  journal={arXiv preprint arXiv:2504.18583},
  year={2025}
}