Instructions to use opencsg/Agentic-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opencsg/Agentic-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="opencsg/Agentic-27B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("opencsg/Agentic-27B")
model = AutoModelForMultimodalLM.from_pretrained("opencsg/Agentic-27B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use opencsg/Agentic-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opencsg/Agentic-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencsg/Agentic-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/opencsg/Agentic-27B

SGLang

How to use opencsg/Agentic-27B 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 "opencsg/Agentic-27B" \
    --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": "opencsg/Agentic-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "opencsg/Agentic-27B" \
        --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": "opencsg/Agentic-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use opencsg/Agentic-27B with Docker Model Runner:
```
docker model run hf.co/opencsg/Agentic-27B
```

ZionWong commited on Sep 15

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@@ -88,7 +88,7 @@ Agentic-27B 使用两个互补评测来展示能力：
 Agentic-27B 全量运行了 119 个任务，其中 103 个任务得到有效评分。平均分、`pass^3` 和 `pass@3` 均按这 103 个可评分任务统计；
-![Agentic Eval Results](.assets/Agentic-Eval-2.jpg)
 Agentic Eval 使用 119 个智能体任务，每题独立运行 3 次，共 357 次 trial。任务包含中文和非中文任务，并覆盖 easy、medium、hard 多个难度层级。
@@ -132,7 +132,7 @@ task_score = safety × base
 Arena-Hard-v2.0 用于补充观察模型在通用难题指令遵从和创作任务上的表现。对 Agent 模型来说，这一点很重要：工具执行能力提升不应以普通任务能力明显脆化为代价。
-![Arena-Hard-v2 Results](.assets/Arena-Hard-v2.jpg)
 这些结果显示，Agentic-27B 相比首个公开版本同时提升了智能体任务执行能力和通用难题处理能力。
@@ -258,7 +258,7 @@ Together, these two benchmarks show that Agentic-27B improves real agent executi
 Agentic-27B was run on the full 119-task suite. In this run, 103 tasks were scoreable. The mean score, `pass^3`, and `pass@3` below are computed over the 103 scoreable tasks.
-![Agentic Eval Results](.assets/Agentic-Eval-2.jpg)
 Agentic Eval uses 119 agent tasks. Each task runs independently three times, for 357 trials in total. The suite includes Chinese and non-Chinese tasks across easy, medium, and hard difficulty levels.
@@ -302,7 +302,7 @@ A trial passes at `task_score >= 0.75`. `pass^3` means all three attempts for th
 Arena-Hard-v2.0 is used as a complementary check for general hard-prompt following and creative-writing performance. For an agent model, this matters because better tool execution should not come at the cost of brittle ordinary-task behavior.
-![Arena-Hard-v2 Results](.assets/Arena-Hard-v2.jpg)
 These results show that Agentic-27B improves both agent-task execution and general hard-prompt capability compared with the first public release.

 Agentic-27B 全量运行了 119 个任务，其中 103 个任务得到有效评分。平均分、`pass^3` 和 `pass@3` 均按这 103 个可评分任务统计；
+![Agentic Eval Results](./assets/Agentic-Eval-2.jpg)
 Agentic Eval 使用 119 个智能体任务，每题独立运行 3 次，共 357 次 trial。任务包含中文和非中文任务，并覆盖 easy、medium、hard 多个难度层级。
 Arena-Hard-v2.0 用于补充观察模型在通用难题指令遵从和创作任务上的表现。对 Agent 模型来说，这一点很重要：工具执行能力提升不应以普通任务能力明显脆化为代价。
+![Arena-Hard-v2 Results](./assets/Arena-Hard-v2.jpg)
 这些结果显示，Agentic-27B 相比首个公开版本同时提升了智能体任务执行能力和通用难题处理能力。
 Agentic-27B was run on the full 119-task suite. In this run, 103 tasks were scoreable. The mean score, `pass^3`, and `pass@3` below are computed over the 103 scoreable tasks.
+![Agentic Eval Results](./assets/Agentic-Eval-2.jpg)
 Agentic Eval uses 119 agent tasks. Each task runs independently three times, for 357 trials in total. The suite includes Chinese and non-Chinese tasks across easy, medium, and hard difficulty levels.
 Arena-Hard-v2.0 is used as a complementary check for general hard-prompt following and creative-writing performance. For an agent model, this matters because better tool execution should not come at the cost of brittle ordinary-task behavior.
+![Arena-Hard-v2 Results](./assets/Arena-Hard-v2.jpg)
 These results show that Agentic-27B improves both agent-task execution and general hard-prompt capability compared with the first public release.