Instructions to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Soulfate24/OvisOCR2-ASHQ1-Remix-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("Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-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 Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF # Run inference directly in the terminal: llama cli -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF # Run inference directly in the terminal: llama cli -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
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 Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF # Run inference directly in the terminal: ./llama-cli -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
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 Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
Use Docker
docker model run hf.co/Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
- LM Studio
- Jan
- vLLM
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Soulfate24/OvisOCR2-ASHQ1-Remix-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": "Soulfate24/OvisOCR2-ASHQ1-Remix-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/Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
- SGLang
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-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 "Soulfate24/OvisOCR2-ASHQ1-Remix-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": "Soulfate24/OvisOCR2-ASHQ1-Remix-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 "Soulfate24/OvisOCR2-ASHQ1-Remix-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": "Soulfate24/OvisOCR2-ASHQ1-Remix-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 Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with Ollama:
ollama run hf.co/Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
- Unsloth Desktop
- Pi
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
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": "Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with Docker Model Runner:
docker model run hf.co/Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
- Lemonade
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
Run and chat with the model
lemonade run user.OvisOCR2-ASHQ1-Remix-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-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 Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
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 Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF
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 "Soulfate24/OvisOCR2-ASHQ1-Remix-GGUF" \ --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"
OvisOCR2 - ASHQ1-Remix
This is a GGUF quantized version of the original model.
📈 Release Benchmarks (wiki.test.raw, symmetric FA-auto reference)
| Tier | Size | PPL | KLD | RMS Δp | top-p |
|---|---|---|---|---|---|
| Fidelity-48pc | 704 MiB | 31.0673 | 0.0021 | 0.98% | 97.4% |
| Precision-42pc 🥈 Second Choice | 668 MiB | 31.0809 | 0.0026 | 1.06% | 97.1% |
| Quality-36pc ⭐ Recommended | 531 MiB | 32.0240 | 0.0155 | 2.68% | 93.0% |
| Compact-33pc | 487 MiB | 32.7223 | 0.0306 | 3.90% | 90.3% |
| Mini-30pc | 474 MiB | 32.8232 | 0.0352 | 4.17% | 89.8% |
| Nano-27pc | 457 MiB | 33.1136 | 0.0648 | 5.95% | 86.7% |
| Pico-24pc | 442 MiB | 34.7822 | 0.0775 | 6.33% | 85.2% |
ℹ️ About ASHQ1-Remix Suite
Activation-aware GGUF quantization whose every ratio, floor, and cap traces to a measured experiment. Plain-BF16-native first; AutoRound lineage supported with explicit saturation bounds. Full seven-tier ladder validated across six model families.
🔗 Link: https://huggingface.co/Soulfate24/AutoRound-ASHQ1-Remix_Double-Quantization_Suite
- Downloads last month
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We're not able to determine the quantization variants.