Update README.md

README.md

@@ -3,43 +3,50 @@ license: apache-2.0
 pipeline_tag: image-text-to-text
 ---
 
-moondream is a small vision language model designed to run efficiently on edge devices. Check out the [GitHub repository](https://github.com/vikhyat/moondream) for details, or try it out on the [Hugging Face Space](https://huggingface.co/spaces/vikhyatk/moondream2)!
+Moondream is a small vision language model designed to run efficiently on edge devices. 
 
-**Benchmarks**
+* [Website](https://moondream.ai/)
+* [Demo](https://moondream.ai/playground)
+* [GitHub](https://github.com/vikhyat/moondream)
 
-| Release | VQAv2 | GQA | TextVQA | DocVQA | TallyQA<br>(simple/full) | POPE<br>(rand/pop/adv) |
-| --- | --- | --- | --- | --- | --- | --- |
-| **2024-08-26** (latest) | 80.3 | 64.3 | 65.2 | 70.5 | 82.6 / 77.6 | 89.6 / 88.8 / 87.2 |
-| 2024-07-23 | 79.4 | 64.9 | 60.2 | 61.9 | 82.0 / 76.8 | 91.3 / 89.7 / 86.9 |
-| 2024-05-20 | 79.4 | 63.1 | 57.2 | 30.5 | 82.1 / 76.6 | 91.5 / 89.6 / 86.2 |
-| 2024-05-08 | 79.0 | 62.7 | 53.1 | 30.5 | 81.6 / 76.1 | 90.6 / 88.3 / 85.0 |
-| 2024-04-02 | 77.7 | 61.7 | 49.7 | 24.3 | 80.1 / 74.2 | - |
-| 2024-03-13 | 76.8 | 60.6 | 46.4 | 22.2 | 79.6 / 73.3 | - |
-| 2024-03-06 | 75.4 | 59.8 | 43.1 | 20.9 | 79.5 / 73.2 | - |
-| 2024-03-04 | 74.2 | 58.5 | 36.4 | - | - | - |
+This repository contains the latest (**2025-01-09**) release of Moondream, as well as historical releases. The model is updated frequently, so we recommend specifying a revision as shown below if you're using it in a production application.
 
 
 **Usage**
 
-```bash
-pip install transformers einops
-```
-
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 
-model_id = "vikhyatk/moondream2"
-revision = "2024-08-26"
 model = AutoModelForCausalLM.from_pretrained(
-    model_id, trust_remote_code=True, revision=revision
+    "vikhyatk/moondream2",
+    revision="2025-01-09",
+    trust_remote_code=True,
+    # Uncomment to run on GPU.
+    # device_map={"": "cuda"}
 )
-tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
-
-image = Image.open('<IMAGE_PATH>')
-enc_image = model.encode_image(image)
-print(model.answer_question(enc_image, "Describe this image.", tokenizer))
-```
 
-The model is updated regularly, so we recommend pinning the model version to a
-specific release as shown above.
+# Captioning
+print("Short caption:")
+print(model.caption(image, length="short")["caption"])
+
+print("\nNormal caption:")
+for t in model.caption(image, length="normal", stream=True)["caption"]:
+    # Streaming generation example, supported for caption() and detect()
+    print(t, end="", flush=True)
+print(model.caption(image, length="normal"))
+
+# Visual Querying
+print("\nVisual query: 'How many people are in the image?'")
+print(model.query(image, "How many people are in the image?")["answer"])
+
+# Object Detection
+print("\nObject detection: 'face'")
+objects = model.detect(image, "face")["objects"]
+print(f"Found {len(objects)} face(s)")
+
+# Pointing
+print("\nPointing: 'person'")
+points = model.point(image, "person")["points"]
+print(f"Found {len(points)} person(s)")
+```