ollama/docs/import.md

Import a model

This guide walks through creating an Ollama model from an existing model on HuggingFace from PyTorch, Safetensors or GGUF. It optionally covers pushing the model to ollama.ai.

Supported models

Ollama supports a set of model architectures, with support for more coming soon:

• Llama
• Mistral
• Falcon & RW
• GPTNeoX
• BigCode

To view a model's architecture, check its config.json file. You should see an entry under architecture (e.g. LlamaForCausalLM).

Importing

Step 1: Clone the HuggingFace repository

git lfs install
git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
cd Mistral-7B-Instruct-v0.1

Step 2: Convert and quantize

• Install Docker

Until Ollama supports conversion and quantization as a built-in feature, a Docker image with this tooling built-in is available.

To convert and quantize your model, run:

docker run --rm -v .:/model ollama/quantize -q q4_0 /model

This will output two files into the directory:

• f16.bin: the model converted to GGUF
• q4_0.bin the model quantized to a 4-bit quantization

Step 3: Write a Modelfile

Next, create a Modelfile for your model. This file is the blueprint for your model, specifying weights, parameters, prompt templates and more.

FROM ./q4_0.bin

(Optional) many chat models require a prompt template in order to answer correctly. A default prompt template can be specified with the TEMPLATE instruction in the Modelfile:

FROM ./q4_0.bin
TEMPLATE "[INST] {{ .Prompt }} [/INST]"

Step 4: Create an Ollama model

Finally, create a model from your Modelfile:

ollama create example -f Modelfile

Next, test the model with ollama run:

ollama run example "What is your favourite condiment?"

Step 5: Publish your model (optional - in alpha)

Publishing models is in early alpha. If you'd like to publish your model to share with others, follow these steps:

1. Create an account
2. Ollama uses SSH keys similar to Git. Find your public key with cat ~/.ollama/id_ed25519.pub and copy it to your clipboard.
3. Add your public key to your Ollama account

Next, copy your model to your username's namespace:

ollama cp example <your username>/example

Then push the model:

ollama push <your username>/example

After publishing, your model will be available at https://ollama.ai/<your username>/example

Quantization reference

The quantization options are as follow (from highest highest to lowest levels of quantization). Note: some architectures such as Falcon do not support K quants.

• q2_K
• q3_K
• q3_K_S
• q3_K_M
• q3_K_L
• q4_0 (recommended)
• q4_1
• q4_K
• q4_K_S
• q4_K_M
• q5_0
• q5_1
• q5_K
• q5_K_S
• q5_K_M
• q6_K
• q8_0

Manually converting & quantizing models

Prerequisites

Start by cloning the llama.cpp repo to your machine in another directory:

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

Next, install the Python dependencies:

pip install -r requirements.txt

Finally, build the quantize tool:

make quantize

Convert the model

Run the correct conversion script for your model architecture:

# LlamaForCausalLM or MistralForCausalLM
python3 convert.py <path to model directory>

# FalconForCausalLM
python3 convert-falcon-hf-to-gguf.py <path to model directory>

# GPTNeoXForCausalLM
python3 convert-falcon-hf-to-gguf.py <path to model directory>

# GPTBigCodeForCausalLM
python3 convert-starcoder-hf-to-gguf.py <path to model directory>

Quantize the model

quantize <path to model dir>/ggml-model-f32.bin <path to model dir>/q4_0.bin q4_0