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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Intel/llava-gemma-2b")
model = AutoModelForImageTextToText.from_pretrained("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?"}
        ]
    },
]
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

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
```

Musashi Hinck commited on Mar 22, 2024

Commit

cb4912f

1 Parent(s): 5b48a72

Initial model card

Browse files

Files changed (1) hide show

README.md +54 -1

README.md CHANGED Viewed

@@ -3,4 +3,57 @@ license_name: gemma-terms
 license_link: https://ai.google.dev/gemma/terms
 language:
 - en
----

 license_link: https://ai.google.dev/gemma/terms
 language:
 - en
+---
+# LLaVA-Gemma Model Card
+_This model card corresponds to the 2B version of the model with the CLIP-based vision encoder._
+## Overview
+`llava-gemma-2b` is a large multimodal model (LMM) trained using the [LLaVA-v1.5 framework](https://arxiv.org/abs/2310.03744) with the 2-billion parameter `google/gemma-2b-it` model as language backbone.
+## Uses
+The model has been finetuned for multimodal benchmark evaluations, but can also be used as a multimodal chatbot.
+## Bias, Risks, and Limitations
+This model has not been assessed for harm or biases, and should not be used for sensitive applications where it may cause harm.
+## How to Get Started with the Model
+Using the LLaVA-Gemma models currently requires a custom fork of the [`LLaVA`](https://github.com/haotian-liu/LLaVA) library. _We will release converted checkpoints compatible with the HuggingFace implementation of LLaVA shortly._
+## Training Details
+The `llava-gemma-2b` model was trained on 8 Gaudi 2 accelerators.
+### Training Data
+The model was trained using the LLaVA-v1.5 data mixture.
+This is listed as follows:
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 450K academic-task-oriented VQA data mixture.
+- 40K ShareGPT data.
+## Evaluation
+| LM Backbone | Vision Model | Pretrained Connector | GQA   | MME cognition | MME perception | MM-Vet | POPE accuracy | POPE F1 | VQAv2 | TextVQA | ScienceQA Image | MMVP  |
+| ------------ | ------------- | --------------------- | ------ | ---------------- | ----------------- | ------- | ------------------ | ------------ | ------ | -------- | -------------------- | ------ |
+| gemma-2b-it | CLIP         | Yes                  | 0.531 | 236.071         | 1130.492         | 17.706 | 0.850             | 0.839       | 70.65 | 28.06   | 0.564               | 0.287 |
+| gemma-2b-it | CLIP         | No                   | 0.481 | 247.857         | 934.611          | 13.119 | 0.784             | 0.762       | 61.74 |         | 0.549               | 0.180 |
+| gemma-7b-it | CLIP         | Yes                  | 0.472 | 253.571         | 894.910          | 18.165 | 0.848             | 0.829       | 68.7  |         | 0.625               | 0.327 |
+| gemma-7b-it | CLIP         | No                   | 0.472 | 278.214         | 857.274          | 19.083 | 0.782             | 0.734       | 65.09 |         | 0.636               | 0.240 |
+| gemma-2b-it | DinoV2       | Yes                  | 0.587 | 307.143         | 1132.970         | 19.128 | 0.853             | 0.838       | 71.37 | 12.53   | 0.555               | 0.227 |
+| gemma-2b-it | DinoV2       | No                   | 0.501 | 308.929         | 959.351          | 14.541 | 0.793             | 0.772       | 61.65 | 11.1    | 0.568               | 0.180 |