Instructions to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", dtype="auto") - llama-cpp-python
How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF", filename="DeepCaption-VLA-7B.IQ4_XS.gguf", )
llm.create_chat_completion( 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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 new
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
llm.create_chat_completion(
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"
}
}
]
}
]
)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 controlโfrom concise summaries to fine-grained attributionsโvia 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 |
| DeepCaption-VLA-7B.Q2_K.gguf | Q2_K | 3.02 GB | Download |
| DeepCaption-VLA-7B.Q3_K_L.gguf | Q3_K_L | 4.09 GB | Download |
| DeepCaption-VLA-7B.Q3_K_M.gguf | Q3_K_M | 3.81 GB | Download |
| DeepCaption-VLA-7B.Q3_K_S.gguf | Q3_K_S | 3.49 GB | Download |
| DeepCaption-VLA-7B.Q4_K_M.gguf | Q4_K_M | 4.68 GB | Download |
| DeepCaption-VLA-7B.Q4_K_S.gguf | Q4_K_S | 4.46 GB | Download |
| DeepCaption-VLA-7B.Q5_K_M.gguf | Q5_K_M | 5.44 GB | Download |
| DeepCaption-VLA-7B.Q5_K_S.gguf | Q5_K_S | 5.32 GB | Download |
| DeepCaption-VLA-7B.Q6_K.gguf | Q6_K | 6.25 GB | Download |
| DeepCaption-VLA-7B.Q8_0.gguf | Q8_0 | 8.1 GB | Download |
| DeepCaption-VLA-7B.f16.gguf | F16 | 15.2 GB | Download |
| DeepCaption-VLA-7B.mmproj-Q8_0.gguf | mmproj-Q8_0 | 856 MB | Download |
| DeepCaption-VLA-7B.mmproj-f16.gguf | mmproj-f16 | 1.35 GB | Download |
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):
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Model tree for prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF
Base model
Qwen/Qwen2.5-VL-7B-Instruct
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/DeepCaption-VLA-7B-AIO-GGUF", filename="", )