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

Libraries

How to use nomic-ai/nomic-embed-text-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nomic-ai/nomic-embed-text-v1-GGUF",
	filename="nomic-embed-text-v1.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nomic-ai/nomic-embed-text-v1-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 nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nomic-ai/nomic-embed-text-v1-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 nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nomic-ai/nomic-embed-text-v1-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 nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nomic-ai/nomic-embed-text-v1-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 nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use nomic-ai/nomic-embed-text-v1-GGUF with Ollama:
```
ollama run hf.co/nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use nomic-ai/nomic-embed-text-v1-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 nomic-ai/nomic-embed-text-v1-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 nomic-ai/nomic-embed-text-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use nomic-ai/nomic-embed-text-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M
```

Lemonade

How to use nomic-ai/nomic-embed-text-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nomic-ai/nomic-embed-text-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.nomic-embed-text-v1-GGUF-Q4_K_M

List all available models

lemonade list

Cebtenzzre commited on Apr 28, 2025

Commit

90c80ac

1 Parent(s): 7db7c76

cleanup

Browse files

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README.md +4 -11

README.md CHANGED Viewed

@@ -12,33 +12,26 @@ quantized_by: Nomic
 tags:
   - feature-extraction
   - sentence-similarity
 ---
-***
-**Note**: For compatiblity with current llama.cpp, please download the files published on 2/15/2024. The files originally published here will fail to load.
-***
-<br/>
 # nomic-embed-text-v1 - GGUF
 Original model: [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1)
 ## Usage
-Embedding text with `nomic-embed-text` requires task instruction prefixes at the beginning of each string.
 For example, the code below shows how to use the `search_query` prefix to embed user questions, e.g. in a RAG application.
-To see the full set of task instructions available & how they are designed to be used, visit the model card for [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5).
 ## Description
 This repo contains llama.cpp-compatible files for [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) in GGUF format.
-llama.cpp will default to 2048 tokens of context with these files. To use the full 8192 tokens that Nomic Embed is benchmarked on, you will have to choose a context extension method. The original model uses Dynamic NTK-Aware RoPE scaling, but that is not currently available in llama.cpp. A combination of YaRN and linear scaling is an acceptable substitute.
-These files were converted and quantized with llama.cpp [PR 5500](https://github.com/ggerganov/llama.cpp/pull/5500), commit [34aa045de](https://github.com/ggerganov/llama.cpp/pull/5500/commits/34aa045de44271ff7ad42858c75739303b8dc6eb).
 ## Example `llama.cpp` Command

 tags:
   - feature-extraction
   - sentence-similarity
+new_version: nomic-ai/nomic-embed-text-v1.5-GGUF
 ---
 # nomic-embed-text-v1 - GGUF
 Original model: [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1)
 ## Usage
+Embedding text with `nomic-embed-text` requires task instruction prefixes at the beginning of each string.
 For example, the code below shows how to use the `search_query` prefix to embed user questions, e.g. in a RAG application.
+To see the full set of task instructions available & how they are designed to be used, visit the model card for [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1).
 ## Description
 This repo contains llama.cpp-compatible files for [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) in GGUF format.
+llama.cpp will default to 2048 tokens of context with these files. For the full 8192 token context length, you will have to choose a context extension method. The 🤗 Transformers model uses Dynamic NTK-Aware RoPE scaling, but that is not currently available in llama.cpp.
 ## Example `llama.cpp` Command