Instructions to use muradil211/ToolWeave_stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muradil211/ToolWeave_stage2 with Transformers:

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

pipe = pipeline("text-generation", model="muradil211/ToolWeave_stage2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muradil211/ToolWeave_stage2")
model = AutoModelForCausalLM.from_pretrained("muradil211/ToolWeave_stage2", 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_stage2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muradil211/ToolWeave_stage2"
# 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_stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muradil211/ToolWeave_stage2

SGLang

How to use muradil211/ToolWeave_stage2 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_stage2" \
    --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_stage2",
		"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_stage2" \
        --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_stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use muradil211/ToolWeave_stage2 with Docker Model Runner:
```
docker model run hf.co/muradil211/ToolWeave_stage2
```

ToolWeave_stage2 / README.md

muradil211

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

	<div align="center">

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

	<h1>ToolWeave · Stage 2</h1>

	<p><strong>🎯 Progress-Reward Learning</strong></p>
	<p>Turning correct tool use into measurable multi-turn task progress.</p>

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

	</div>

	> 🎯 Curriculum role: build on Stage 1 tool competence and optimize actual progress through multi-turn environment interaction.

	## 🧭 At a glance

	\| Field \| Details \|
	\|---\|---\|
	\| 🧠 Base family \| Qwen3-4B-Instruct \|
	\| 🪜 Curriculum stage \| Stage 2 — Progress-Reward Learning \|
	\| 🧱 Starting point \| ToolWeave Stage 1 update 25 \|
	\| 📍 Checkpoint \| Selected Stage 2 update 25 \|
	\| 🎛️ Training signal \| Fixed-denominator multi-turn Progress Reward \|
	\| ✅ Release status \| Selected checkpoint; not the final ToolWeave Stage 3 model \|

	ToolWeave Stage 2 trains on multi-turn BFCL environment tasks with a fixed-denominator Progress Reward, moving from correct tool execution toward reliable task completion.

	## 📊 Evaluation (eval_400)

	This is an internal ToolWeave validation on `val_400_combined`: 400 examples, with 100 examples each from Base, Long Context, Missing Function, and Missing Parameter. Validation used deterministic decoding (`n=1`, `do_sample=false`). The validation split is not included in this model repository, and these results are not official BFCL leaderboard results.

	For Stage 2, `score` is the fixed-denominator Progress Reward and is equal to `progress` in this evaluation. It is therefore not directly comparable to the Stage 1 format-gate score.

	### Overall

	\| Samples \| Score / Progress \| Format reward \| Tool-call reward \| Tool-call rate \| Terminal coverage \| Incomplete trajectories \|
	\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| 400 \| 0.4567 \| 0.8582 \| 0.9174 \| 0.9275 \| 0.8739 \| 0.1450 \|

	### By evaluation category

	\| Split \| Samples \| Progress / Score \| Terminal coverage \| Incomplete trajectories \|
	\|---\|---:\|---:\|---:\|---:\|
	\| Base \| 100 \| 0.6027 \| 0.9575 \| 0.0500 \|
	\| Long Context \| 100 \| 0.3952 \| 0.7730 \| 0.2600 \|
	\| Missing Function \| 100 \| 0.4515 \| 0.8868 \| 0.1300 \|
	\| Missing Parameter \| 100 \| 0.3774 \| 0.8781 \| 0.1400 \|

	## 🚀 Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "muradil211/ToolWeave_stage2"
	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)