Instructions to use Kfkcome/ToolMaster-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kfkcome/ToolMaster-7B with Transformers:

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

pipe = pipeline("text-generation", model="Kfkcome/ToolMaster-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kfkcome/ToolMaster-7B")
model = AutoModelForCausalLM.from_pretrained("Kfkcome/ToolMaster-7B")
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 Kfkcome/ToolMaster-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kfkcome/ToolMaster-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kfkcome/ToolMaster-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kfkcome/ToolMaster-7B

SGLang

How to use Kfkcome/ToolMaster-7B 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 "Kfkcome/ToolMaster-7B" \
    --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": "Kfkcome/ToolMaster-7B",
		"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 "Kfkcome/ToolMaster-7B" \
        --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": "Kfkcome/ToolMaster-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kfkcome/ToolMaster-7B with Docker Model Runner:
```
docker model run hf.co/Kfkcome/ToolMaster-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

ToolMaster-7B

ToolMaster is a framework that shifts tool learning from static imitation to a trial-and-execution paradigm, enabling Large Language Models (LLMs) to actively master tools. It was introduced in the paper Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction.

Introduction

Existing tool-use paradigms primarily rely on memorizing static solution paths during training, which limits the ability of LLMs to generalize to new or evolving tools. ToolMaster addresses this by training agents to:

Trial Phase: Conduct autonomous tool trials to accumulate experiential knowledge.
Execution Phase: Perform planning and solving while explicitly employing self-correction to rectify errors based on environmental feedback.

By leveraging Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) using Group Relative Policy Optimization (GRPO), ToolMaster empowers agents to dynamically adapt to unfamiliar tools, significantly enhancing generalization and robustness.

Resources

Paper: Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction
Repository: https://github.com/NEUIR/ToolMaster

Model Details

This checkpoint is a fine-tuned version of Qwen2.5-7B-Instruct. It has been optimized for tool planning and invocation through the trial-and-execution framework.

Usage

For detailed instructions on environment setup, data preparation, and evaluation (on benchmarks like ToolHop, TMDB, and StableToolBench), please refer to the official GitHub repository.

Citation

If you find this work useful, please cite:

@article{gao2025teaching,
  title={Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction},
  author={Gao, Xingjie and others},
  journal={arXiv preprint arXiv:2601.12762},
  year={2025}
}