Instructions to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/HunyuanOCR-1.5-GGUF-Updated")
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/HunyuanOCR-1.5-GGUF-Updated", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated 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/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/HunyuanOCR-1.5-GGUF-Updated: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/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/HunyuanOCR-1.5-GGUF-Updated: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/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/HunyuanOCR-1.5-GGUF-Updated: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/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/HunyuanOCR-1.5-GGUF-Updated"
# 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/HunyuanOCR-1.5-GGUF-Updated",
		"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/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M

SGLang

How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated 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/HunyuanOCR-1.5-GGUF-Updated" \
    --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/HunyuanOCR-1.5-GGUF-Updated",
		"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/HunyuanOCR-1.5-GGUF-Updated" \
        --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/HunyuanOCR-1.5-GGUF-Updated",
		"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/HunyuanOCR-1.5-GGUF-Updated with Ollama:
```
ollama run hf.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated 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/HunyuanOCR-1.5-GGUF-Updated 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/HunyuanOCR-1.5-GGUF-Updated to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/HunyuanOCR-1.5-GGUF-Updated to start chatting

Docker Model Runner
How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M
```

Lemonade

How to use prithivMLmods/HunyuanOCR-1.5-GGUF-Updated with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/HunyuanOCR-1.5-GGUF-Updated:Q4_K_M

Run and chat with the model

lemonade run user.HunyuanOCR-1.5-GGUF-Updated-Q4_K_M

List all available models

lemonade list

Atomic Chat

HunyuanOCR-1.5-GGUF-Updated

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---
license: apache-2.0
base_model:
- tencent/HunyuanOCR
library_name: transformers
language:
- en
- zh
pipeline_tag: image-text-to-text
tags:
- llama-cpp
- text-generation-inference
- ocr
- vision-language-model
- document-parsing
- text-spotting
- information-extraction
- text-image-translation
---

# **HunyuanOCR-1.5-GGUF-Updated**

> **[HunyuanOCR-1.5](https://huggingface.co/tencent/HunyuanOCR)** is Tencent's lightweight, end-to-end OCR-specialized vision-language model that unifies document parsing, text spotting, information extraction, and text-image translation within a single VLM, building on the validated architecture of HunyuanOCR-1.0 (archived under the `v1.0/` subfolder) without redesigning the backbone. Its key upgrades target speed and capability: a **DFlash speculative-decoding** framework uses a lightweight block-diffusion draft model to propose multiple candidate tokens in parallel, verified by the target model in a single pass, significantly cutting decoding latency on long structured outputs like dense documents, tables, and formulas while preserving the original output distribution; it also supports CPU/consumer-GPU/laptop deployment via a GGUF-converted checkpoint and OpenAI-compatible `llama-server`, including a DFlash-adapted llama.cpp fork. On the training side, an **Agentic Data Flow** system — where agents handle material search, tool-based verification, and data-pipeline iteration in a closed loop with engineers — targets long-tail capabilities like low-resource and ancient-script OCR, alongside an upgraded recipe extending maximum image resolution to 4K and context window to 128K tokens, with refined SFT data and reinforcement learning across OCR tasks. The model uses the `HunYuanVLForConditionalGeneration` architecture (requiring transformers ≥5.13.0), supports native transformers, vLLM (both AR and DFlash modes from a single unified `uv`-based CUDA 13 environment), and llama.cpp inference paths across 12 task types (document parsing, structured/layout parsing, chart/formula/table extraction, and Chinese-English translation variants), and is released under the Tencent Hunyuan Community License Agreement.

> [!NOTE]
> HunyuanOCR v1.5 [GGUF]

## Model Files

   File Name | Quant Type | File Size | File Link |
 |-----------|------------|-----------|-----------|
 | HunyuanOCR.BF16.gguf | BF16 | 1.08 GB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.BF16.gguf) |
 | HunyuanOCR.F16.gguf | F16 | 1.08 GB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.F16.gguf) |
 | HunyuanOCR.F32.gguf | F32 | 2.16 GB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.F32.gguf) |
 | HunyuanOCR.Q3_K_L.gguf | Q3_K_L | 327 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q3_K_L.gguf) |
 | HunyuanOCR.Q3_K_M.gguf | Q3_K_M | 308 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q3_K_M.gguf) |
 | HunyuanOCR.Q3_K_S.gguf | Q3_K_S | 285 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q3_K_S.gguf) |
 | HunyuanOCR.Q4_K_M.gguf | Q4_K_M | 355 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q4_K_M.gguf) |
 | HunyuanOCR.Q4_K_S.gguf | Q4_K_S | 342 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q4_K_S.gguf) |
 | HunyuanOCR.Q5_K_M.gguf | Q5_K_M | 400 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q5_K_M.gguf) |
 | HunyuanOCR.Q5_K_S.gguf | Q5_K_S | 392 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q5_K_S.gguf) |
 | HunyuanOCR.Q8_0.gguf | Q8_0 | 578 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.Q8_0.gguf) |
 | HunyuanOCR.mmproj-bf16.gguf | mmproj-bf16 | 997 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.mmproj-bf16.gguf) |
 | HunyuanOCR.mmproj-f16.gguf | mmproj-f16 | 997 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.mmproj-f16.gguf) |
 | HunyuanOCR.mmproj-q8_0.gguf | mmproj-q8_0 | 733 MB | [Download](https://huggingface.co/prithivMLmods/HunyuanOCR-1.5-GGUF-Updated/blob/main/HunyuanOCR.mmproj-q8_0.gguf) |

## llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp