Instructions to use cfli/A-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cfli/A-base with Transformers:

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

pipe = pipeline("text-generation", model="cfli/A-base")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("cfli/A-base")
model = AutoModelForMultimodalLM.from_pretrained("cfli/A-base", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cfli/A-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cfli/A-base

SGLang

How to use cfli/A-base 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 "cfli/A-base" \
    --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": "cfli/A-base",
		"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 "cfli/A-base" \
        --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": "cfli/A-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cfli/A-base with Docker Model Runner:
```
docker model run hf.co/cfli/A-base
```

cfli commited on 5 days ago

Commit

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README.md +4 -5

README.md CHANGED Viewed

@@ -66,11 +66,11 @@ This design preserves useful work across iterations and reduces repeated explora
 AREX is evaluated through a unified long-horizon search-agent interface with `search`, `visit`, `update_context`, and `finish` tools. HLE with tools additionally provides a Python tool. The reported setup allows up to 300 inner-loop turns and 5 outer-loop self-improvement operations.
-<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1050px;margin:0 auto;padding:16px 0;overflow-x:auto">
-<table style="width:100%;min-width:900px;border-collapse:collapse;font-size:13px">
 <thead><tr>
 <th style="padding:10px 10px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed">Model</th>
-<th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px"># Total<br>Params</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">BrowseComp</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">GAIA</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">xbench-2510</th>
@@ -152,10 +152,9 @@ AREX is evaluated through a unified long-horizon search-agent interface with `se
 </tr>
 </tbody>
 </table>
 </div>
-`*` marks results reported on the full HLE. Unmarked HLE results use the text-only subset.
 ## Inference
 See [`inference/README.md`](inference/README.md) for OpenAI-compatible serving and generation examples. The folder also contains the complete, directly formattable system and user prompts used for BrowseComp and HLE with tools.

 AREX is evaluated through a unified long-horizon search-agent interface with `search`, `visit`, `update_context`, and `finish` tools. HLE with tools additionally provides a Python tool. The reported setup allows up to 300 inner-loop turns and 5 outer-loop self-improvement operations.
+<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:900px;margin:0 auto;padding:16px 0">
+<table style="width:100%;border-collapse:collapse;font-size:13px">
 <thead><tr>
 <th style="padding:10px 10px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed">Model</th>
+<th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">Total Params</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">BrowseComp</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">GAIA</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">xbench-2510</th>
 </tr>
 </tbody>
 </table>
+<div style="font-size:12px;color:#6b7280;margin-top:8px;line-height:1.5">* Results reported on the full HLE. Unmarked HLE results use the text-only subset.</div>
 </div>
 ## Inference
 See [`inference/README.md`](inference/README.md) for OpenAI-compatible serving and generation examples. The folder also contains the complete, directly formattable system and user prompts used for BrowseComp and HLE with tools.