Instructions to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/SoMarkAI-Docision-1.0-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/SoMarkAI-Docision-1.0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-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/SoMarkAI-Docision-1.0-GGUF with Ollama:
ollama run hf.co/prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SoMarkAI-Docision-1.0-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/SoMarkAI-Docision-1.0-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "prithivMLmods/SoMarkAI-Docision-1.0-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
SoMarkAI-Docision-1.0-GGUF
Docision-1.0 is SoMark's "Jev-inspired" multimodal decision model for documents, fine-tuned from Qwen3-VL-4B-Instruct (Chinese and English). Given a document image and one or more questions, each with its own caller-defined candidate answers (the number and content of candidates can vary per question), it returns the chosen option plus a probability for every candidate. The card describes its range as document classification, visual attribute recognition (such as whether signatures and seals are present), checking relationships between fields, verifying completeness, applying business rules to route documents, and even analyzing engineering drawings and scientific charts. Several questions about the same image share the context, which the card says gives a multiplicative speedup. On the card's doc-decision-bench (5,234 document records), a chart reports 96.18% accuracy for Docision versus 95.72% for JEV-27B-VL and 86.78% for the Qwen3-VL-4B base, so the 4B model is slightly ahead of a 27B model, though the detailed table is an image and the exact scores come only from that chart. It runs through a demo script that reads a JSON file of image and questions and writes a JSON result, and an MCP bridge lets Codex or Claude Code query a running Docision service over local documents and export JSON or CSV. The card gives no training details. The license is Apache 2.0, and the card asks readers to star the project.
Model Files
| File Name | Quant Type | File Size | File Link | Description |
|---|---|---|---|---|
| Docision-1.0.BF16.gguf | BF16 | 8.83 GB | Link | Full BF16 weights. Highest quality, largest file size. |
| Docision-1.0.Q2_K.gguf | Q2_K | 1.8 GB | Link | Lowest quality, smallest size, good for extreme low RAM availability. |
| Docision-1.0.Q3_K_L.gguf | Q3_K_L | 2.41 GB | Link | Lower quality but usable, good for low RAM availability. |
| Docision-1.0.Q3_K_M.gguf | Q3_K_M | 2.24 GB | Link | Low quality. |
| Docision-1.0.Q4_K_M.gguf | Q4_K_M | 2.72 GB | Link | Good quality, default size for most use cases, recommended. |
| Docision-1.0.Q4_K_S.gguf | Q4_K_S | 2.6 GB | Link | Slightly lower quality with more space savings, recommended. |
| Docision-1.0.Q5_K_M.gguf | Q5_K_M | 3.16 GB | Link | High quality, recommended. |
| Docision-1.0.Q5_K_S.gguf | Q5_K_S | 3.09 GB | Link | High quality, recommended. |
| Docision-1.0.Q6_K.gguf | Q6_K | 3.63 GB | Link | Very high quality, near perfect, recommended. |
| Docision-1.0.Q8_0.gguf | Q8_0 | 4.69 GB | Link | Extremely high quality, generally unneeded but max available quant. |
| Docision-1.0.mmproj-bf16.gguf | mmproj-bf16 | 839 MB | Link | Multimodal projection file in BF16 format. Used for vision/language models. |
| Docision-1.0.mmproj-q8_0.gguf | mmproj-q8_0 | 454 MB | Link | Multimodal projection file in Q8_0 quantization. Smaller size for vision capabilities. |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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