Instructions to use Hon-Wong/VoRA-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hon-Wong/VoRA-7B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="Hon-Wong/VoRA-7B-Instruct", trust_remote_code=True)
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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Hon-Wong/VoRA-7B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hon-Wong/VoRA-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hon-Wong/VoRA-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hon-Wong/VoRA-7B-Instruct",
		"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/Hon-Wong/VoRA-7B-Instruct

SGLang

How to use Hon-Wong/VoRA-7B-Instruct 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 "Hon-Wong/VoRA-7B-Instruct" \
    --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": "Hon-Wong/VoRA-7B-Instruct",
		"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 "Hon-Wong/VoRA-7B-Instruct" \
        --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": "Hon-Wong/VoRA-7B-Instruct",
		"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 Hon-Wong/VoRA-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Hon-Wong/VoRA-7B-Instruct
```

Improve model card

by nielsr HF Staff - opened Apr 8, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+40

-29

Files changed (1) hide show

README.md +40 -29

README.md CHANGED Viewed

@@ -1,37 +1,48 @@
 # VoRA
 * [ArXiv Paper](https://arxiv.org/abs/2503.20680)
 * [Github](https://github.com/Hon-Wong/VoRA)
 ## Quickstart
 ```python
-    import torch
-    from transformers import AutoProcessor, AutoModelForCausalLM
-    model_name = "Hon-Wong/VoRA-7B-Instruct"
-    processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-    conversation = [
-        {
-            "role":"user",
-            "content":[
-                {
-                    "type":"image",
-                    "url": "{image path or url}"
-                },
-                {
-                    "type":"text",
-                    "text":"<image> Describe this image."
-                }
-            ]
-        }
-    ]
-    model_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_tensors='pt', return_dict=True).to(model.device)
-    gen_kwargs = {"max_new_tokens": 1024, "eos_token_id": processor.tokenizer.eos_token_id}
-    with torch.inference_mode():
-        outputs = model.generate(model_inputs, **gen_kwargs)
-        output_text = processor.tokenizer.batch_decode(
-            outputs, skip_special_tokens=True
-        )
-        print(output_text)
 ```

+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model:
+- Hon-Wong/VoRA-7B-Base
+datasets:
+- Hon-Wong/VoRA-Recap-29M
+---
 # VoRA
 * [ArXiv Paper](https://arxiv.org/abs/2503.20680)
 * [Github](https://github.com/Hon-Wong/VoRA)
 ## Quickstart
+The model can be used as follows:
 ```python
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+model_name = "Hon-Wong/VoRA-7B-Instruct"
+processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+conversation = [
+    {
+        "role":"user",
+        "content":[
+            {
+                "type":"image",
+                "url": "{image path or url}"
+            },
+            {
+                "type":"text",
+                "text":"<image> Describe this image."
+            }
+        ]
+    }
+]
+model_inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_tensors='pt', return_dict=True).to(model.device)
+gen_kwargs = {"max_new_tokens": 1024, "eos_token_id": processor.tokenizer.eos_token_id}
+with torch.inference_mode():
+    outputs = model.generate(model_inputs, **gen_kwargs)
+    output_text = processor.tokenizer.batch_decode(
+        outputs, skip_special_tokens=True
+    )
+    print(output_text)
 ```