Instructions to use prithivMLmods/pepperocr-1-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/pepperocr-1-4b-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-GGUF", device_map="auto") - llama-cpp-python
How to use prithivMLmods/pepperocr-1-4b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/pepperocr-1-4b-GGUF", filename="pepperocr-1-4b.BF16.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 Settings
- llama.cpp
How to use prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/pepperocr-1-4b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/pepperocr-1-4b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/pepperocr-1-4b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-GGUF with Ollama:
ollama run hf.co/prithivMLmods/pepperocr-1-4b-GGUF:Q4_K_M
- Unsloth Studio
How to use prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-GGUF to start chatting
- Pi
How to use prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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/pepperocr-1-4b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use prithivMLmods/pepperocr-1-4b-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/pepperocr-1-4b-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/pepperocr-1-4b-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"
- Docker Model Runner
How to use prithivMLmods/pepperocr-1-4b-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/pepperocr-1-4b-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/pepperocr-1-4b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/pepperocr-1-4b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.pepperocr-1-4b-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"
}
}
]
}
]
)pepperocr-1-4b-GGUF
PepperOCR-1-4B is a 4.54-billion-parameter multilingual vision-language model for document parsing, fine-tuned from Qwen3.5-4B (architecture
Qwen3_5ForConditionalGeneration) by sionic-ai, designed to convert document images directly into Markdown output and released as a public pre-release evaluation repository with weights distributed in BF16 safetensors (2 shards, ~9.1 GB). The model ships with a fully reproducible MDPBench evaluation runtime locked viauvwith stable vLLM 0.24.0, PyTorch 2.11.0+cu130, CUDA 13.0, and Transformers 5.11.0, processing all 17 public MDPBench languages with a CPU-based document-orientation classifier (PP-LCNet_x1_0_doc_ori) preceding inference; on locally reproduced Korean and Thai public-set scores it achieves 92.2 and 83.2 overall accuracy respectively (87.7 KO/TH macro average), broken down further into digital versus photographed document subsets, using a greedy-first decoding strategy with a degeneration guard that retried roughly 10% of samples. As a pre-release evaluation preview, no final license has yet been applied to the fine-tuning contribution itself (though the underlying Qwen3.5-4B base remains Apache-2.0, and the final PepperOCR-1-4B release is planned under Apache-2.0 pending evaluation approval), and public downloadability does not currently grant redistribution or reuse rights.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| pepperocr-1-4b.BF16.gguf | BF16 | 8.42 GB | Download |
| pepperocr-1-4b.F16.gguf | F16 | 8.42 GB | Download |
| pepperocr-1-4b.Q3_K_L.gguf | Q3_K_L | 2.42 GB | Download |
| pepperocr-1-4b.Q3_K_M.gguf | Q3_K_M | 2.26 GB | Download |
| pepperocr-1-4b.Q3_K_S.gguf | Q3_K_S | 2.07 GB | Download |
| pepperocr-1-4b.Q4_K_M.gguf | Q4_K_M | 2.71 GB | Download |
| pepperocr-1-4b.Q4_K_S.gguf | Q4_K_S | 2.56 GB | Download |
| pepperocr-1-4b.Q5_K_M.gguf | Q5_K_M | 3.07 GB | Download |
| pepperocr-1-4b.Q5_K_S.gguf | Q5_K_S | 2.99 GB | Download |
| pepperocr-1-4b.Q8_0.gguf | Q8_0 | 4.48 GB | Download |
| pepperocr-1-4b.mmproj-bf16.gguf | mmproj-bf16 | 676 MB | Download |
| pepperocr-1-4b.mmproj-f16.gguf | mmproj-f16 | 676 MB | Download |
| pepperocr-1-4b.mmproj-q8_0.gguf | mmproj-q8_0 | 367 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/pepperocr-1-4b-GGUF
Base model
sionic-ai/pepperocr-1-4b
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/pepperocr-1-4b-GGUF", filename="", )