Instructions to use apus-ailab/APUS-OpenJev-v1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apus-ailab/APUS-OpenJev-v1-9B with Transformers:

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

pipe = pipeline("text-generation", model="apus-ailab/APUS-OpenJev-v1-9B")
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("apus-ailab/APUS-OpenJev-v1-9B")
model = AutoModelForMultimodalLM.from_pretrained("apus-ailab/APUS-OpenJev-v1-9B", 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 apus-ailab/APUS-OpenJev-v1-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apus-ailab/APUS-OpenJev-v1-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "apus-ailab/APUS-OpenJev-v1-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/apus-ailab/APUS-OpenJev-v1-9B

SGLang

How to use apus-ailab/APUS-OpenJev-v1-9B 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 "apus-ailab/APUS-OpenJev-v1-9B" \
    --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": "apus-ailab/APUS-OpenJev-v1-9B",
		"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 "apus-ailab/APUS-OpenJev-v1-9B" \
        --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": "apus-ailab/APUS-OpenJev-v1-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use apus-ailab/APUS-OpenJev-v1-9B with Docker Model Runner:
```
docker model run hf.co/apus-ailab/APUS-OpenJev-v1-9B
```

gump2049 commited on 8 days ago

Commit

9dd2482

verified ·

1 Parent(s): 675200f

Link model collection and clarify selected series checkpoints

Browse files

Files changed (3) hide show

README.md +1 -1
README.zh-CN.md +1 -1
release-manifest.json +8 -4

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ tags:
 ---
 # APUS-OpenJev-v1-9B
-[English](README.md) | [中文](README.zh-CN.md) · [Model family](https://huggingface.co/apus-ailab/APUS-OpenJev-v1) · [Technical Report](https://huggingface.co/apus-ailab/APUS-OpenJev-v1/blob/main/TECHNICAL_REPORT.md) · [Runtime](RUNTIME.md)
 A Qwen3.5-based decision model for browser action selection, workflow routing, and natural-language principle judgments. This repository contains **9B checkpoint-3000 merged BF16 weights**, ready to download independently without a separate LoRA adapter.

 ---
 # APUS-OpenJev-v1-9B
+[English](README.md) | [中文](README.zh-CN.md) · [Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825) · [Model family](https://huggingface.co/apus-ailab/APUS-OpenJev-v1) · [Technical Report](https://huggingface.co/apus-ailab/APUS-OpenJev-v1/blob/main/TECHNICAL_REPORT.md) · [Runtime](RUNTIME.md)
 A Qwen3.5-based decision model for browser action selection, workflow routing, and natural-language principle judgments. This repository contains **9B checkpoint-3000 merged BF16 weights**, ready to download independently without a separate LoRA adapter.

README.zh-CN.md CHANGED Viewed

@@ -15,7 +15,7 @@ tags:
 ---
 # APUS-OpenJev-v1-9B
-[English](README.md) | [中文](README.zh-CN.md) · [Model family](https://huggingface.co/apus-ailab/APUS-OpenJev-v1) · [Technical Report](https://huggingface.co/apus-ailab/APUS-OpenJev-v1/blob/main/TECHNICAL_REPORT.md) · [Runtime](RUNTIME.md)
 基于 Qwen3.5 的通用决策模型，面向浏览器动作选择、业务流程路由与基于自然语言原则的判断。本仓库提供 **9B checkpoint-3000 合并 BF16 权重**，可独立下载，无需另外加载 LoRA。

 ---
 # APUS-OpenJev-v1-9B
+[English](README.md) | [中文](README.zh-CN.md) · [Collection](https://huggingface.co/collections/apus-ailab/apus-openjev-v1-6ab1ee888eb002fcdd3a2825) · [Model family](https://huggingface.co/apus-ailab/APUS-OpenJev-v1) · [Technical Report](https://huggingface.co/apus-ailab/APUS-OpenJev-v1/blob/main/TECHNICAL_REPORT.md) · [Runtime](RUNTIME.md)
 基于 Qwen3.5 的通用决策模型，面向浏览器动作选择、业务流程路由与基于自然语言原则的判断。本仓库提供 **9B checkpoint-3000 合并 BF16 权重**，可独立下载，无需另外加载 LoRA。

release-manifest.json CHANGED Viewed

@@ -21,13 +21,13 @@
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     {
       "path": "RUNTIME.md",
@@ -221,5 +221,9 @@
       "README.zh-CN.md"
     ],
     "weights_and_runtime_unchanged": true
   }
 }

     },
     {
       "path": "README.md",
+      "bytes": 3029,
+      "sha256": "779c0ebf1ceb954e7b8a11fe91e47fe3520e16907e96762a6c1e2b52b65bc08e"
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       "path": "README.zh-CN.md",
+      "bytes": 2784,
+      "sha256": "ff7aaa1618e9fa2404e32cf23e5c9228973619bfb55c2ca66724b14d9b8a79f1"
     },
     {
       "path": "RUNTIME.md",
       "README.zh-CN.md"
     ],
     "weights_and_runtime_unchanged": true
+  },
+  "card_update": {
+    "previous_revision": "675200fd8c4dfd13a638e9e738f3821c0808b5ee",
+    "scope": "Collection link and series checkpoint selection wording; no model changes"
   }
 }