Instructions to use xushuwen23/GraphWalker-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xushuwen23/GraphWalker-7B with Transformers:

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

pipe = pipeline("text-generation", model="xushuwen23/GraphWalker-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xushuwen23/GraphWalker-7B")
model = AutoModelForCausalLM.from_pretrained("xushuwen23/GraphWalker-7B", 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 xushuwen23/GraphWalker-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xushuwen23/GraphWalker-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": "xushuwen23/GraphWalker-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xushuwen23/GraphWalker-7B

SGLang

How to use xushuwen23/GraphWalker-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 "xushuwen23/GraphWalker-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": "xushuwen23/GraphWalker-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 "xushuwen23/GraphWalker-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": "xushuwen23/GraphWalker-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xushuwen23/GraphWalker-7B with Docker Model Runner:
```
docker model run hf.co/xushuwen23/GraphWalker-7B
```

nielsr HF Staff commited on Apr 7

Commit

26cacf5

verified ·

1 Parent(s): 63330cc

Update pipeline tag and add library name

Browse files

This PR improves the model card for GraphWalker-7B by:
- Updating the `pipeline_tag` to `text-generation` to reflect its architecture and improve discoverability.
- Adding `library_name: transformers` to enable automated usage snippets.
- Maintaining all original documentation including evaluation results and GitHub links.

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -1,8 +1,9 @@
 ---
-license: apache-2.0
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: question-answering
 tags:
 - LLM Agent
 - Knowledge Graph
@@ -37,7 +38,7 @@ pip install vllm transformers
 ```bash
 # Via huggingface-cli
-huggingface-cli download <your-org>/GraphWalker-7B --local-dir ./GraphWalker-7B
 ```
 ### 3. Inference with vLLM (Recommended)
@@ -45,7 +46,7 @@ huggingface-cli download <your-org>/GraphWalker-7B --local-dir ./GraphWalker-7B
 **Start the vLLM server:**
 ```bash
-vllm serve "./GraphWalker-7B" \
     --host 0.0.0.0 --port 22240 \
     --served-model-name graphwalker-7b \
     --gpu-memory-utilization 0.9 \

 ---
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 tags:
 - LLM Agent
 - Knowledge Graph
 ```bash
 # Via huggingface-cli
+huggingface-cli download xushuwen23/GraphWalker-7B --local-dir ./GraphWalker-7B
 ```
 ### 3. Inference with vLLM (Recommended)
 **Start the vLLM server:**
 ```bash
+vllm serve "xushuwen23/GraphWalker-7B" \
     --host 0.0.0.0 --port 22240 \
     --served-model-name graphwalker-7b \
     --gpu-memory-utilization 0.9 \