Instructions to use prithivMLmods/Infinity-Parser2-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Infinity-Parser2-Flash-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF", device_map="auto") - llama-cpp-python
How to use prithivMLmods/Infinity-Parser2-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/Infinity-Parser2-Flash-GGUF", filename="Infinity-Parser2-Flash.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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Infinity-Parser2-Flash-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Infinity-Parser2-Flash-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Infinity-Parser2-Flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Infinity-Parser2-Flash-GGUF:Q4_K_M
- Unsloth Studio
How to use prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF to start chatting
- Pi
How to use prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use prithivMLmods/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-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/Infinity-Parser2-Flash-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Infinity-Parser2-Flash-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Infinity-Parser2-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Infinity-Parser2-Flash-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Infinity-Parser2-Flash-GGUF-Q4_K_M
List all available models
lemonade list
Infinity-Parser2-Flash-GGUF
Infinity-Parser2-Flash is a low-latency document understanding model from infly-ai, one of two variants in the Infinity-Parser2 flagship family (alongside the accuracy-optimized Infinity-Parser2-Pro), engineered for fast inference while consolidating robust multi-modal parsing into a unified architecture trained via an upgraded synthetic data engine spanning nearly 5 million diverse document samples and a novel multi-task reinforcement learning approach with verifiable rewards across document parsing, element parsing, chart parsing, chemical formula parsing, document VQA, and general multimodal understanding. It delivers a 3.68x speedup over the previous Infinity-Parser-7B model (increasing throughput from 441 to 1,624 tokens/sec) while still posting strong benchmark results — 86.0% on olmOCR-Bench, 72.2% on ParseBench, and 91.98% on OmniDocBench-v1.6 — outperforming frontier models like DeepSeek-OCR-2 and MinerU2.5 on several document-parsing tasks, though trailing its larger Pro sibling on layout analysis, chart/chemical formula parsing, and general multimodal benchmarks (e.g., MMMU, AI2D, MathVista). It extracts structured layout with bounding boxes, category labels, and per-element text (LaTeX for formulas, HTML for tables, Markdown for text), supports command-line and Python API usage via the
infinity_parser2package with vLLM, transformers, or vLLM-server backends, and is released under Apache-2.0, with known limitations primarily around English/Chinese-only support, degraded accuracy on complex charts and rotated table elements, and no fine-grained text formatting (bold, italic, strikethrough) capture.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Infinity-Parser2-Flash.BF16.gguf | BF16 | 3.78 GB | Download |
| Infinity-Parser2-Flash.F16.gguf | F16 | 3.78 GB | Download |
| Infinity-Parser2-Flash.F32.gguf | F32 | 7.54 GB | Download |
| Infinity-Parser2-Flash.Q2_K.gguf | Q2_K | 969 MB | Download |
| Infinity-Parser2-Flash.Q3_K_L.gguf | Q3_K_L | 1.16 GB | Download |
| Infinity-Parser2-Flash.Q3_K_M.gguf | Q3_K_M | 1.1 GB | Download |
| Infinity-Parser2-Flash.Q3_K_S.gguf | Q3_K_S | 1.02 GB | Download |
| Infinity-Parser2-Flash.Q4_0.gguf | Q4_0 | 1.2 GB | Download |
| Infinity-Parser2-Flash.Q4_K_M.gguf | Q4_K_M | 1.27 GB | Download |
| Infinity-Parser2-Flash.Q4_K_S.gguf | Q4_K_S | 1.21 GB | Download |
| Infinity-Parser2-Flash.Q5_0.gguf | Q5_0 | 1.37 GB | Download |
| Infinity-Parser2-Flash.Q5_K_M.gguf | Q5_K_M | 1.41 GB | Download |
| Infinity-Parser2-Flash.Q5_K_S.gguf | Q5_K_S | 1.37 GB | Download |
| Infinity-Parser2-Flash.Q6_K.gguf | Q6_K | 1.56 GB | Download |
| Infinity-Parser2-Flash.Q8_0.gguf | Q8_0 | 2.01 GB | Download |
| Infinity-Parser2-Flash.mmproj-bf16.gguf | mmproj-bf16 | 671 MB | Download |
| Infinity-Parser2-Flash.mmproj-f16.gguf | mmproj-f16 | 671 MB | Download |
| Infinity-Parser2-Flash.mmproj-f32.gguf | mmproj-f32 | 1.33 GB | Download |
| Infinity-Parser2-Flash.mmproj-q8_0.gguf | mmproj-q8_0 | 365 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/Infinity-Parser2-Flash-GGUF
Base model
infly/Infinity-Parser2-Flash
docker model run hf.co/prithivMLmods/Infinity-Parser2-Flash-GGUF: