Instructions to use muradil211/ToolWeave_stage3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muradil211/ToolWeave_stage3 with Transformers:

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

pipe = pipeline("text-generation", model="muradil211/ToolWeave_stage3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muradil211/ToolWeave_stage3")
model = AutoModelForCausalLM.from_pretrained("muradil211/ToolWeave_stage3", 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 muradil211/ToolWeave_stage3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muradil211/ToolWeave_stage3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muradil211/ToolWeave_stage3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muradil211/ToolWeave_stage3

SGLang

How to use muradil211/ToolWeave_stage3 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 "muradil211/ToolWeave_stage3" \
    --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": "muradil211/ToolWeave_stage3",
		"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 "muradil211/ToolWeave_stage3" \
        --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": "muradil211/ToolWeave_stage3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use muradil211/ToolWeave_stage3 with Docker Model Runner:
```
docker model run hf.co/muradil211/ToolWeave_stage3
```

ToolWeave · Stage 3

🧭 Boundary-Guided Online-Learning Reference

A reference starting point for ToolWeave's boundary-guided tool-use stage.

🤗 Model · 🧵 Project · 📄 RODS paper

⚠️ Release status: reference checkpoint only. Formal ToolWeave Stage 3 training remains pending.

🧭 At a glance

Field	Details
🧠 Reference model	Qwen3-4B-RODS
🪜 Intended role	Stage 3 reference / starting point
🔬 Project status	Formal ToolWeave Stage 3 training pending
📦 Release status	Reference checkpoint only

This repository stores the Qwen3-4B-RODS reference checkpoint used in the ToolWeave Stage 3 workspace. It must not be interpreted as a completed ToolWeave Stage 3 release or as the official RODS implementation. ToolWeave is a separate project that adapts public ideas and infrastructure with project-specific extensions.

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "muradil211/ToolWeave_stage3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

Tool-use inference requires the model's function schemas and the Qwen3-compatible tool-call format.

🔗 References

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Paper for muradil211/ToolWeave_stage3

RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents

Paper • 2606.19047 • Published Jun 17 • 4