Instructions to use shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410 with Transformers:

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

pipe = pipeline("text-generation", model="shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410")
model = AutoModelForCausalLM.from_pretrained("shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410", 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 shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410

SGLang

How to use shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410 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 "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410" \
    --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": "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410",
		"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 "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410" \
        --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": "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410 with Docker Model Runner:
```
docker model run hf.co/shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410
```

Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410 / README.md

shawnzzzzz

Add model configuration and card

d5e3e32 verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

1.33 kB

metadata

license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-30B-A3B-Base
tags:
  - qwen3
  - grpo
  - reasoning
  - math

Qwen3-30B-A3B GRPO Low-Precision Control — Step 410

This is a research checkpoint derived from Qwen/Qwen3-30B-A3B-Base with group-relative policy optimization on mathematical reasoning data. It is the retained step-410 checkpoint from the low-precision control run.

The checkpoint is exported as standard BF16 Hugging Face safetensors; it does not require custom low-precision inference kernels.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shawnzzzzz/Qwen3-30B-A3B-GRPO-LowPrecision-Control-Step410"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

Use transformers>=4.51.0 for Qwen3-MoE support.

Notes

This run ended at step 410, so it is not step-matched to the two step-800 checkpoints in the accompanying comparison. This is an intermediate research checkpoint, not a production release. Users should independently evaluate correctness, safety, and suitability for their deployment setting. The base-model license applies.