Instructions to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF",
		"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/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF 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 "prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF" \
    --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": "prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF",
		"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 "prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF" \
        --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": "prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepCaption-VLA-7B-AIO-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

prithivMLmods commited on Dec 25, 2025

Commit

0e43885

verified ·

1 Parent(s): c905d3f

Update README.md

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README.md CHANGED Viewed

@@ -14,4 +14,32 @@ tags:
 # **DeepCaption-VLA-7B-AIO-GGUF**
-> DeepCaption-VLA-7B from prithivMLmods is a 7B-parameter vision-language model fine-tuned from Qwen2.5-VL-7B-Instruct, specialized for image captioning and Vision Language Attribution (VLA) that generates precise, attribute-rich descriptions of visual properties, object attributes, scene details, colors, environments, moods, and actions across general, artistic, technical, abstract, or low-context images in diverse aspect ratios (wide, tall, square, irregular). Trained on curated datasets like blip3o-caption-mini-arrow, Caption3o-Opt-v3/v2, Caltech101 attributes, and private domain-specific sources to emphasize object-attribute alignment and descriptive fluency, it supports variational detail control—from concise summaries to fine-grained attributions—via structured outputs including captions, comma-separated attributes, and {class_name==core_theme} syntax, primarily in English with multilingual prompt adaptability. Ideal for research/dataset creation, object detection annotation, scene understanding, and creative applications using Transformers/Qwen2VLForConditionalGeneration inference, it handles non-standard visuals robustly but may over-attribute ambiguous content or vary by prompt phrasing.

 # **DeepCaption-VLA-7B-AIO-GGUF**
+> DeepCaption-VLA-7B from prithivMLmods is a 7B-parameter vision-language model fine-tuned from Qwen2.5-VL-7B-Instruct, specialized for image captioning and Vision Language Attribution (VLA) that generates precise, attribute-rich descriptions of visual properties, object attributes, scene details, colors, environments, moods, and actions across general, artistic, technical, abstract, or low-context images in diverse aspect ratios (wide, tall, square, irregular). Trained on curated datasets like blip3o-caption-mini-arrow, Caption3o-Opt-v3/v2, Caltech101 attributes, and private domain-specific sources to emphasize object-attribute alignment and descriptive fluency, it supports variational detail control—from concise summaries to fine-grained attributions—via structured outputs including captions, comma-separated attributes, and {class_name==core_theme} syntax, primarily in English with multilingual prompt adaptability. Ideal for research/dataset creation, object detection annotation, scene understanding, and creative applications using Transformers/Qwen2VLForConditionalGeneration inference, it handles non-standard visuals robustly but may over-attribute ambiguous content or vary by prompt phrasing.
+## DeepCaption-VLA-7B [GGUF]
+| File Name | Quant Type | File Size | File Link |
+| - | - | - | - |
+| DeepCaption-VLA-7B.IQ4_XS.gguf | IQ4_XS | 4.25 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.IQ4_XS.gguf) |
+| DeepCaption-VLA-7B.Q2_K.gguf | Q2_K | 3.02 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q2_K.gguf) |
+| DeepCaption-VLA-7B.Q3_K_L.gguf | Q3_K_L | 4.09 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q3_K_L.gguf) |
+| DeepCaption-VLA-7B.Q3_K_M.gguf | Q3_K_M | 3.81 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q3_K_M.gguf) |
+| DeepCaption-VLA-7B.Q3_K_S.gguf | Q3_K_S | 3.49 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q3_K_S.gguf) |
+| DeepCaption-VLA-7B.Q4_K_M.gguf | Q4_K_M | 4.68 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q4_K_M.gguf) |
+| DeepCaption-VLA-7B.Q4_K_S.gguf | Q4_K_S | 4.46 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q4_K_S.gguf) |
+| DeepCaption-VLA-7B.Q5_K_M.gguf | Q5_K_M | 5.44 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q5_K_M.gguf) |
+| DeepCaption-VLA-7B.Q5_K_S.gguf | Q5_K_S | 5.32 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q5_K_S.gguf) |
+| DeepCaption-VLA-7B.Q6_K.gguf | Q6_K | 6.25 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q6_K.gguf) |
+| DeepCaption-VLA-7B.Q8_0.gguf | Q8_0 | 8.1 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.Q8_0.gguf) |
+| DeepCaption-VLA-7B.f16.gguf | F16 | 15.2 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.f16.gguf) |
+| DeepCaption-VLA-7B.mmproj-Q8_0.gguf | mmproj-Q8_0 | 856 MB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.mmproj-Q8_0.gguf) |
+| DeepCaption-VLA-7B.mmproj-f16.gguf | mmproj-f16 | 1.35 GB | [Download](https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF/blob/main/DeepCaption-VLA-7B.mmproj-f16.gguf) |
+## Quants Usage
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)