Instructions to use cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench with Transformers:

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

pipe = pipeline("text-generation", model="cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench")
model = AutoModelForCausalLM.from_pretrained("cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench

SGLang

How to use cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench 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 "cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench" \
    --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": "cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench",
		"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 "cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench" \
        --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": "cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench with Docker Model Runner:
```
docker model run hf.co/cx-cmu/AutoGEO_mini_Qwen1.7B_GEOBench
```

zhongshsh commited on Jan 18

Commit

04e55b7

verified ·

1 Parent(s): b083592

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -18,9 +18,9 @@ base_model: Qwen/Qwen3-1.7B
 datasets:
 - cx-cmu/GEO-Bench
 ---
-# AutoGEO<sub>Mini</sub> (Qwen1.7B, GEOBench)
-AutoGEO<sub>Mini</sub> (Qwen1.7B, GEOBench) is a GEO model designed to improve how web document is incorporated into answers generated by **LLM-based search engines**.
 The model rewrites a given document to better match the preferences of generative engines (e.g., GPT, Gemini, Claude), with the goal of increasing the document’s **visibility and coverage** in generated responses, while **preserving the original meaning and factual content**.
@@ -41,7 +41,7 @@ from autogeo.rewriters import rewrite_document
 rewritten_text = rewrite_document(
     document="Input text.",
-    dataset="GEOBench",
     engine_llm="gemini",
     model_path="cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO",
 )
@@ -53,7 +53,7 @@ Evaluation:
 python -m autogeo.evaluate \
   --model autogeo_mini \
   --model_path cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO \
-  --dataset GEOBench
 ```
@@ -62,7 +62,7 @@ python -m autogeo.evaluate \
 * **Paper:** [https://arxiv.org/abs/2510.11438](https://arxiv.org/abs/2510.11438)
 * **Code:** [https://github.com/cxcscmu/AutoGEO](https://github.com/cxcscmu/AutoGEO)
-* **Dataset:** [https://huggingface.co/datasets/cx-cmu/GEOBench](https://huggingface.co/datasets/cx-cmu/GEOBench)
 ## Citation

 datasets:
 - cx-cmu/GEO-Bench
 ---
+# AutoGEO<sub>Mini</sub> (Qwen1.7B, GEO-Bench)
+AutoGEO<sub>Mini</sub> (Qwen1.7B, GEO-Bench) is a GEO model designed to improve how web document is incorporated into answers generated by **LLM-based search engines**.
 The model rewrites a given document to better match the preferences of generative engines (e.g., GPT, Gemini, Claude), with the goal of increasing the document’s **visibility and coverage** in generated responses, while **preserving the original meaning and factual content**.
 rewritten_text = rewrite_document(
     document="Input text.",
+    dataset="GEO-Bench",
     engine_llm="gemini",
     model_path="cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO",
 )
 python -m autogeo.evaluate \
   --model autogeo_mini \
   --model_path cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO \
+  --dataset GEO-Bench
 ```
 * **Paper:** [https://arxiv.org/abs/2510.11438](https://arxiv.org/abs/2510.11438)
 * **Code:** [https://github.com/cxcscmu/AutoGEO](https://github.com/cxcscmu/AutoGEO)
+* **Dataset:** [https://huggingface.co/datasets/cx-cmu/GEO-Bench](https://huggingface.co/datasets/cx-cmu/GEO-Bench)
 ## Citation