Instructions to use Intel/llava-gemma-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intel/llava-gemma-2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Intel/llava-gemma-2b")
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("Intel/llava-gemma-2b")
model = AutoModelForMultimodalLM.from_pretrained("Intel/llava-gemma-2b", 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 Intel/llava-gemma-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Intel/llava-gemma-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intel/llava-gemma-2b",
		"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/Intel/llava-gemma-2b

SGLang

How to use Intel/llava-gemma-2b 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 "Intel/llava-gemma-2b" \
    --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": "Intel/llava-gemma-2b",
		"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 "Intel/llava-gemma-2b" \
        --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": "Intel/llava-gemma-2b",
		"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 Intel/llava-gemma-2b with Docker Model Runner:
```
docker model run hf.co/Intel/llava-gemma-2b
```

bconsolvo commited on May 31, 2024

Commit

5cde804

verified ·

1 Parent(s): 50a4edc

Update README.md

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README.md +10 -10

README.md CHANGED Viewed

@@ -18,31 +18,31 @@ model-index:
     metrics:
     - type: GQA
       name: GQA
-      value: 0.587
     - type: MME Cog.
       name: MME Cog.
-      value: 309
     - type: MME Per.
       name: MME Per.
-      value: 1133
     - type: MM-Vet
       name: MM-Vet
-      value: 19.1
     - type: POPE Acc.
       name: POPE Acc.
-      value: 0.853
     - type: POPE F1
       name: POPE F1
       value: 0.839
     - type: VQAv2
       name: VQAv2
-      value: 71.4
     - type: MMVP
       name: MMVP
-      value: 0.327
     - type: ScienceQA Image
       name: ScienceQA Image
-      value: 0.636
 library_name: transformers
 pipeline_tag: image-text-to-text
 ---
@@ -165,8 +165,8 @@ Performance of LLaVA-Gemma models across seven benchmarks. Highlighted box indic
 | LM Backbone | Vision Model | Pretrained Connector | GQA   | MME cognition | MME perception | MM-Vet | POPE accuracy | POPE F1 | VQAv2 | ScienceQA Image | MMVP  |
 | ----------- | ------------ | -------------------- | ----- | ------------- | -------------- | ------ | ------------- | ------- | ----- | --------------- | ----- |
-| gemma-2b-it | CLIP         | Yes                  | 0.531 | 236           | 1130           | 17.7   | 0.850         |<mark>0.839</mark>| 70.65 | 0.564  | 0.287 |
-| **gemma-2b-it** | CLIP         | No                   | 0.481 | 248           | 935            | 13.1   | 0.784         | 0.762   | 61.74 | 0.549           | 0.180 |
 | gemma-2b-it | DinoV2       | Yes                  |<mark>0.587</mark>| 307| <mark>1133</mark>   |<mark>19.1</mark>| <mark>0.853</mark>   | 0.838   |<mark>71.37</mark>| 0.555         | 0.227 |
 | gemma-2b-it | DinoV2       | No                   | 0.501 | <mark>309</mark>| 959          | 14.5   | 0.793         | 0.772   | 61.65 | 0.568           | 0.180 |
 |             |              |                      |       |               |                |        |               |         |       |                 |       |

     metrics:
     - type: GQA
       name: GQA
+      value: 0.531
     - type: MME Cog.
       name: MME Cog.
+      value: 236
     - type: MME Per.
       name: MME Per.
+      value: 1130
     - type: MM-Vet
       name: MM-Vet
+      value: 17.7
     - type: POPE Acc.
       name: POPE Acc.
+      value: 0.850
     - type: POPE F1
       name: POPE F1
       value: 0.839
     - type: VQAv2
       name: VQAv2
+      value: 70.7
     - type: MMVP
       name: MMVP
+      value: 0.287
     - type: ScienceQA Image
       name: ScienceQA Image
+      value: 0.564
 library_name: transformers
 pipeline_tag: image-text-to-text
 ---
 | LM Backbone | Vision Model | Pretrained Connector | GQA   | MME cognition | MME perception | MM-Vet | POPE accuracy | POPE F1 | VQAv2 | ScienceQA Image | MMVP  |
 | ----------- | ------------ | -------------------- | ----- | ------------- | -------------- | ------ | ------------- | ------- | ----- | --------------- | ----- |
+| **gemma-2b-it** | CLIP         | Yes                  | 0.531 | 236           | 1130           | 17.7   | 0.850         |<mark>0.839</mark>| 70.65 | 0.564  | 0.287 |
+| gemma-2b-it | CLIP         | No                   | 0.481 | 248           | 935            | 13.1   | 0.784         | 0.762   | 61.74 | 0.549           | 0.180 |
 | gemma-2b-it | DinoV2       | Yes                  |<mark>0.587</mark>| 307| <mark>1133</mark>   |<mark>19.1</mark>| <mark>0.853</mark>   | 0.838   |<mark>71.37</mark>| 0.555         | 0.227 |
 | gemma-2b-it | DinoV2       | No                   | 0.501 | <mark>309</mark>| 959          | 14.5   | 0.793         | 0.772   | 61.65 | 0.568           | 0.180 |
 |             |              |                      |       |               |                |        |               |         |       |                 |       |