How to use from
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:
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/pepperocr-1-4b-GGUF:
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:
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/pepperocr-1-4b-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 prithivMLmods/pepperocr-1-4b-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/pepperocr-1-4b-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 prithivMLmods/pepperocr-1-4b-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/pepperocr-1-4b-GGUF:
Use Docker
docker model run hf.co/prithivMLmods/pepperocr-1-4b-GGUF:
Quick Links

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 via uv with 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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GGUF
Model size
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Architecture
qwen35
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