Instructions to use cstr/nomic-embed-text-v1.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cstr/nomic-embed-text-v1.5-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 cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS

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 cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS

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 cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS

Use Docker

docker model run hf.co/cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS

LM Studio
Jan
Ollama
How to use cstr/nomic-embed-text-v1.5-GGUF with Ollama:
```
ollama run hf.co/cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS
```

Unsloth Studio

How to use cstr/nomic-embed-text-v1.5-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 cstr/nomic-embed-text-v1.5-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 cstr/nomic-embed-text-v1.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cstr/nomic-embed-text-v1.5-GGUF to start chatting

Docker Model Runner
How to use cstr/nomic-embed-text-v1.5-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS
```

Lemonade

How to use cstr/nomic-embed-text-v1.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/nomic-embed-text-v1.5-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.nomic-embed-text-v1.5-GGUF-IQ4_XS

List all available models

lemonade list

Atomic Chat

nomic-embed-text-v1.5 GGUF

GGUF format of nomic-ai/nomic-embed-text-v1.5 for use with CrispEmbed and Ollama-compatible runtimes.

Files

File	Quantization	Size	Parity (cos vs HF)
nomic-embed-text-v1.5.gguf	F32	~522 MB	1.0000
nomic-embed-text-v1.5-q8_0.gguf	Q8_0	~139 MB	0.9980

NomicBERT uses SwiGLU which is sensitive to aggressive quantization. Q5_K (cos~~0.95) and Q4_K (cos~~0.85) are not provided as they degrade significantly.

Architecture

Model: NomicBERT (BERT + RoPE + SwiGLU, 137M params)
Embedding dimension: 768 (Matryoshka: 512, 256, 128, 64)
Pooling: Mean pooling + L2 normalize
Context length: 2,048 tokens
License: Apache 2.0

Notes

Ollama-compatible format (bert.* namespace). RoPE-based encoder with SwiGLU FFN.

Provenance and EU AI Act Art. 53 note

Upstream model: nomic-ai/nomic-embed-text-v1.5 — published by nomic-ai.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

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GGUF

Model size

0.1B params

Architecture

bert

Hardware compatibility

4-bit

8-bit

View +1 variant

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nomic-ai/nomic-embed-text-v1.5

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