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
```

muradil211 commited on 2 days ago

Commit

23c5d63

verified ·

1 Parent(s): 444bdbe

Update Stage 3 evaluation results

Browse files

Remove the release-status sentence and add the Stage 3 eval results from the project's main README.

Files changed (1) hide show

README.md +12 -2

README.md CHANGED Viewed

@@ -25,8 +25,6 @@ tags:
 </div>
-> ✅ **Release status:** final ToolWeave Stage 3 model.
 ## 🧭 At a glance
 | Field | Details |
@@ -39,6 +37,18 @@ tags:
 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.
 ## 🚀 Usage
 ```python

 </div>
 ## 🧭 At a glance
 | Field | Details |
 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