71 lines
1.6 KiB
Python
71 lines
1.6 KiB
Python
"""
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Usage: python3 srt_example_llava.py
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"""
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import sglang as sgl
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@sgl.function
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def image_qa(s, image_path, question):
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s += sgl.user(sgl.image(image_path) + question)
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s += sgl.assistant(sgl.gen("answer"))
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def single():
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state = image_qa.run(
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image_path="images/cat.jpeg", question="What is this?", max_new_tokens=128
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)
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print(state["answer"], "\n")
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def stream():
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state = image_qa.run(
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image_path="images/cat.jpeg",
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question="What is this?",
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max_new_tokens=64,
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stream=True,
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)
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for out in state.text_iter("answer"):
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print(out, end="", flush=True)
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print()
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def batch():
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states = image_qa.run_batch(
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[
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{"image_path": "images/cat.jpeg", "question": "What is this?"},
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{"image_path": "images/dog.jpeg", "question": "What is this?"},
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],
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max_new_tokens=128,
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)
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for s in states:
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print(s["answer"], "\n")
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if __name__ == "__main__":
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runtime = sgl.Runtime(
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model_path="liuhaotian/llava-v1.6-vicuna-7b",
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tokenizer_path="llava-hf/llava-1.5-7b-hf",
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)
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sgl.set_default_backend(runtime)
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print(f"chat template: {runtime.endpoint.chat_template.name}")
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# Or you can use API models
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# sgl.set_default_backend(sgl.OpenAI("gpt-4-vision-preview"))
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# sgl.set_default_backend(sgl.VertexAI("gemini-pro-vision"))
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# Run a single request
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print("\n========== single ==========\n")
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single()
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# Stream output
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print("\n========== stream ==========\n")
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stream()
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# Run a batch of requests
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print("\n========== batch ==========\n")
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batch()
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runtime.shutdown()
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