Instructions to use dongguanting/ToolSpace-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dongguanting/ToolSpace-8B with Transformers:

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

pipe = pipeline("text-generation", model="dongguanting/ToolSpace-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dongguanting/ToolSpace-8B")
model = AutoModelForCausalLM.from_pretrained("dongguanting/ToolSpace-8B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dongguanting/ToolSpace-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dongguanting/ToolSpace-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dongguanting/ToolSpace-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dongguanting/ToolSpace-8B

SGLang

How to use dongguanting/ToolSpace-8B 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 "dongguanting/ToolSpace-8B" \
    --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": "dongguanting/ToolSpace-8B",
		"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 "dongguanting/ToolSpace-8B" \
        --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": "dongguanting/ToolSpace-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dongguanting/ToolSpace-8B with Docker Model Runner:
```
docker model run hf.co/dongguanting/ToolSpace-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ToolSpace-8B

Code · Evaluation · 8B · 35B-A3B · 744B-A40B

ToolSpace-8B is a Qwen3-8B agent model trained with ToolSpace trajectories and on-policy agent reinforcement learning for long-horizon tool use.

ToolSpace constructs a tool-composition graph from real execution traces, capturing data, state, and retry dependencies. It synthesizes cross-environment tasks through state-aware walks with rollback and keeps trajectories that pass execution-based verification.

Paper: ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents.

https://huggingface.co/papers/2611.05036

Model information

Item	Details
Backbone	Qwen3-8B
Architecture	Dense, 8B parameters
Context length	65,536 tokens
Weight format	BF16 Safetensors
Training	Supervised fine-tuning + agent RL

Results

Results from Table 1 of the ToolSpace paper. Avg. is the unweighted mean over the seven benchmarks.

Model	AutomationBench	SkillsBench	JobBench	τ³-Bench Banking	BFCL V4	OfficeQA Pro	Avg.
Qwen3-8B	0.0	0.1	0.1	4.1	42.8	3.0	7.2
ToolSpace-8B (SFT)	32.3	3.9	13.1	10.3	65.3	12.0	19.6
ToolSpace-8B + Agent RL (this checkpoint)	34.3	4.1	15.5	11.3	67.5	12.0	20.7

Usage

Serve with vLLM and the included chat template:

vllm serve dongguanting/ToolSpace-8B \
  --served-model-name ToolSpace-8B \
  --max-model-len 65536 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --reasoning-parser deepseek_r1

The server exposes an OpenAI-compatible endpoint at http://localhost:8000/v1.

Evaluation commands and benchmark configurations are available in the ToolSpace evaluation directory.