Upload examples/vision_example.py with huggingface_hub

examples/vision_example.py

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+#!/usr/bin/env python3
+"""
+Sixpert K2 - Vision/Multimodal Example
+=======================================
+Demonstrates how to use Sixpert K2's vision capabilities with
+image inputs. K2's MoE architecture provides specialized experts
+for visual understanding.
+
+Usage:
+    python vision_example.py --image ./diagram.png --prompt "Analyze this diagram"
+"""
+
+import argparse
+import base64
+import sys
+
+try:
+    from llama_cpp import Llama
+except ImportError:
+    print("Installing llama-cpp-python...")
+    import subprocess
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "llama-cpp-python"])
+    from llama_cpp import Llama
+
+
+def image_to_base64(image_path: str) -> str:
+    """Convert an image file to base64 data URL."""
+    with open(image_path, "rb") as f:
+        data = base64.b64encode(f.read()).decode("utf-8")
+
+    if image_path.lower().endswith(".png"):
+        mime = "image/png"
+    elif image_path.lower().endswith(".jpg") or image_path.lower().endswith(".jpeg"):
+        mime = "image/jpeg"
+    elif image_path.lower().endswith(".webp"):
+        mime = "image/webp"
+    elif image_path.lower().endswith(".gif"):
+        mime = "image/gif"
+    else:
+        mime = "image/png"
+
+    return f"data:{mime};base64,{data}"
+
+
+def analyze_image(
+    model_path: str,
+    image_path: str,
+    prompt: str,
+    max_tokens: int = 4096,
+):
+    """Analyze an image using Sixpert K2."""
+    print(f"Loading Sixpert K2 (MoE)...")
+    llm = Llama(
+        model_path=model_path,
+        n_ctx=131072,
+        n_gpu_layers=-1,
+        verbose=False,
+    )
+
+    image_data = image_to_base64(image_path)
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are Sixpert K2, a deep reasoning engine with advanced multimodal capabilities. Analyze images with exceptional detail and precision.",
+        },
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": prompt},
+                {"type": "image_url", "image_url": {"url": image_data}},
+            ],
+        },
+    ]
+
+    print(f"\nPrompt: {prompt}")
+    print(f"Image: {image_path}")
+    print("-" * 40)
+    print("Analyzing with MoE vision experts...")
+    print("-" * 40)
+
+    response = llm.create_chat_completion(
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=0.6,
+        stream=True,
+    )
+
+    for chunk in response:
+        delta = chunk["choices"][0]["delta"].get("content", "")
+        if delta:
+            print(delta, end="", flush=True)
+
+    print("\n")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Sixpert K2 Vision Example")
+    parser.add_argument("--model", type=str, default="SixpertK2.gguf", help="Path to GGUF model")
+    parser.add_argument("--image", type=str, required=True, help="Path to input image")
+    parser.add_argument("--prompt", type=str, default="Describe this image in detail.", help="Prompt")
+    parser.add_argument("--max-tokens", type=int, default=4096, help="Max tokens")
+
+    args = parser.parse_args()
+    analyze_image(args.model, args.image, args.prompt, args.max_tokens)
+
+
+if __name__ == "__main__":
+    main()