Instructions to use SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR with Transformers:

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

pipe = pipeline("text-generation", model="SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR")
model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR")
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 SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR

SGLang

How to use SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR 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 "SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR" \
    --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": "SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR",
		"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 "SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR" \
        --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": "SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR with Docker Model Runner:
```
docker model run hf.co/SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR
```

Qwen3-8B-Tooluse-RLSD-TR

This repository contains the Qwen3-8B tooluse RLSD_TR batch-size-32 run.

Performance

Dataset	Method	Base model	Train batch size	Best val mean@16	Best checkpoint	Final val mean@16	Final checkpoint
Tool-use / tooluse	RLSD_TR	Qwen3-8B	32	63.42%	100	63.42%	100

Validation Mean@16

| step | val_mean16 | percent | |---:|---:|---:|---:| | 10 | 0.572610294118 | 57.26% | | 20 | 0.600183823529 | 60.02% | | 30 | 0.588235294118 | 58.82% | | 40 | 0.605698529412 | 60.57% | | 50 | 0.619485294118 | 61.95% | | 60 | 0.614889705882 | 61.49% | | 70 | 0.602022058824 | 60.20% | | 80 | 0.610294117647 | 61.03% | | 90 | 0.625919117647 | 62.59% | | 100 | 0.634191176471 | 63.42% |

Source

Checkpoint: /mnt/mole/SDPO/L2T/checkpoints/datasets/tooluse/qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-8B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8/global_step_100/actor
W&B run: run-20260703_141322-che709vc