How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/pepperocr-1-4b-GGUF",
	filename="",
)
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 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
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