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 7 days ago

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@@ -47,10 +47,10 @@ This repository contains **AREX-Base**, the larger model in the AREX family. It
 **Model Family**
-| Model | Architecture | Backbone | Context | Positioning |
-| --- | --- | --- | ---: | --- |
-| [AREX-Base](https://huggingface.co/BAAI/AREX-Base) | 122B total / 10B activated MoE | Qwen3.5-122B-A10B | 256K | Higher-capacity model with the strongest overall AREX results. |
-| [AREX-Turbo](https://huggingface.co/BAAI/AREX-Turbo) | 4B dense | Qwen3.5-4B | 256K | Compact model for lower-cost research and tool-use settings. |
 ## Method
@@ -64,7 +64,7 @@ This design preserves useful work across iterations and reduces repeated explora
 ## Evaluation
-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">
@@ -76,7 +76,7 @@ AREX is evaluated through a unified long-horizon search-agent interface with `se
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">xbench-2510</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">DeepSearchQA</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">WideSearch-en</th>
-<th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">HLE w/ tools</th>
 </tr></thead>
 <tbody>
 <tr><td colspan="8" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124,58,237,0.2);background:rgba(124,58,237,0.1)">Frontier models</td></tr>

 **Model Family**
+| Model | Backbone | Context | Positioning |
+| --- | --- | ---: | --- |
+| [AREX-Base](https://huggingface.co/BAAI/AREX-Base) | Qwen3.5-122B-A10B | 256K | Higher-capacity model with the strongest overall AREX results. |
+| [AREX-Turbo](https://huggingface.co/BAAI/AREX-Turbo) | Qwen3.5-4B | 256K | Compact model for lower-cost research and tool-use settings. |
 ## Method
 ## Evaluation
+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.
 <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">
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">xbench-2510</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">DeepSearchQA</th>
 <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px">WideSearch-en</th>
+<th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed;font-size:14px;white-space:nowrap">HLE&nbsp;w/&nbsp;tools</th>
 </tr></thead>
 <tbody>
 <tr><td colspan="8" style="padding:8px 12px;font-weight:600;color:#7c3aed;border-bottom:1px solid rgba(124,58,237,0.2);background:rgba(124,58,237,0.1)">Frontier models</td></tr>