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_stage3 / README.md

muradil211

Update Stage 3 evaluation results

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	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qwen3
	- tool-calling
	- bfcl
	- agentic-rl
	- progress-reward
	- online-synthesis
	---

	<div align="center">

	<img src="assets/toolweave-mark.svg" alt="ToolWeave mark" width="128">

	<h1>ToolWeave · Stage 3</h1>

	<p><strong>🧭 Boundary-Guided Online Reinforcement Learning</strong></p>
	<p>Verified online data synthesis for multi-turn tool-calling agents.</p>

	<p>
	<a href="https://github.com/Muradil-mamat-211/ToolWeave">🧵 Project</a>
	</p>

	</div>

	## 🧭 At a glance

	\| Field \| Details \|
	\|---\|---\|
	\| 🧠 Base family \| Qwen3-4B-Instruct \|
	\| 🪜 Curriculum stage \| Stage 3 — Boundary-Guided Online Reinforcement Learning \|
	\| 🧱 Starting point \| ToolWeave Stage 2 update 25 \|
	\| 🎛️ Training signal \| Verified online data synthesis + multi-turn Progress Reward \|
	\| ✅ Release status \| Final ToolWeave Stage 3 model \|

	ToolWeave Stage 3 expands multi-turn tool-use learning through capability-boundary detection, verified online data synthesis, strict execution and semantic validation, dynamic replay, and combined global/local tool-call credit.

	## 📊 Stage 3 evaluation

	The final ToolWeave Stage 3 checkpoint was evaluated on the canonical balanced 400-row held-in set: 100 entries each from Base, Missing Function, Missing Parameter, and Long Context. These values are complete-entry BFCL Multi-Turn accuracies, not the training-time Progress Reward (`R_P`).

	\| Model \| Overall \| Base \| Missing Function \| Missing Parameter \| Long Context \| Correct entries \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| ToolWeave Stage 3 \| 48.50 \| 56.00 \| 50.00 \| 42.00 \| 46.00 \| 194 / 400 \|

	Because the four categories are balanced, the overall score is their unweighted mean and the complete-entry accuracy over all 400 entries:

	`(56.00 + 50.00 + 42.00 + 46.00) / 4 = 48.50`

	## 🚀 Usage

	```python
	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.

	## 🔗 Links

	- [🧵 ToolWeave project](https://github.com/Muradil-mamat-211/ToolWeave)