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

Libraries

How to use prithivMLmods/NetraEmbed-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/NetraEmbed-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/NetraEmbed-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/NetraEmbed-GGUF",
	filename="NetraEmbed.BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/NetraEmbed-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/NetraEmbed-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/NetraEmbed-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/NetraEmbed-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/NetraEmbed-GGUF:BF16

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/NetraEmbed-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/NetraEmbed-GGUF:BF16

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/NetraEmbed-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/NetraEmbed-GGUF:BF16

Use Docker

docker model run hf.co/prithivMLmods/NetraEmbed-GGUF:BF16

LM Studio
Jan
Ollama
How to use prithivMLmods/NetraEmbed-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/NetraEmbed-GGUF:BF16
```

Unsloth Studio new

How to use prithivMLmods/NetraEmbed-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/NetraEmbed-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/NetraEmbed-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/NetraEmbed-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/NetraEmbed-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/NetraEmbed-GGUF:BF16
```

Lemonade

How to use prithivMLmods/NetraEmbed-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/NetraEmbed-GGUF:BF16

Run and chat with the model

lemonade run user.NetraEmbed-GGUF-BF16

List all available models

lemonade list

NetraEmbed-GGUF

NetraEmbed from Cognitive-Lab is a state-of-the-art multilingual multimodal embedding model powered by a Gemma3-4B-IT backbone with SigLIP vision encoder, designed for visual document retrieval via BiEncoder architecture that encodes images of documents and text queries into compact single dense vectors supporting Matryoshka dimensions of 768 (fastest, 95% accuracy retention), 1536 (balanced), or 2560 (maximum accuracy) for flexible inference without model reloading. It achieves groundbreaking performance on Nayana-IR Bench (22 languages) with 0.716 NDCG@5 on cross-lingual tasks—152% improvement over ColPali-v1.3—and 0.738 on monolingual, while being 250x more storage-efficient (~10KB per document vs. 2.5MB multi-vector) than traditional approaches, preserving visual elements like charts, tables, and layouts without OCR errors. Ideal for scalable semantic search across millions of multilingual PDFs/scans using cosine similarity in vector DBs like FAISS, Milvus, or Pinecone, it enables enterprise-grade cross-lingual document discovery for revenue charts, hierarchies, or diagrams in diverse scripts.

NetraEmbed [GGUF]

File Name	Quant Type	File Size	File Link
NetraEmbed.BF16.gguf	BF16	7.77 GB	Download
NetraEmbed.F16.gguf	F16	7.77 GB	Download
NetraEmbed.F32.gguf	F32	15.5 GB	Download
NetraEmbed.Q8_0.gguf	Q8_0	4.13 GB	Download
NetraEmbed.mmproj-bf16.gguf	mmproj-bf16	851 MB	Download
NetraEmbed.mmproj-f16.gguf	mmproj-f16	851 MB	Download
NetraEmbed.mmproj-f32.gguf	mmproj-f32	1.67 GB	Download
NetraEmbed.mmproj-q8_0.gguf	mmproj-q8_0	591 MB	Download