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 · Stage 2

🎯 Progress-Reward Learning

Turning correct tool use into measurable multi-turn task progress.

🧵 Project

🎯 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

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

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Safetensors

Model size

4B params

Tensor type

BF16