ollama/docs/import.md

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# Import a model
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This guide walks through importing a GGUF, PyTorch or Safetensors model.
## Importing (GGUF)
### Step 1: Write a `Modelfile`
Start by creating a `Modelfile`. This file is the blueprint for your model, specifying weights, parameters, prompt templates and more.
```
FROM ./mistral-7b-v0.1.Q4_0.gguf
```
(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 ./mistral-7b-v0.1.Q4_0.gguf
TEMPLATE "[INST] {{ .Prompt }} [/INST]"
```
### Step 2: Create the Ollama model
Finally, create a model from your `Modelfile`:
```
ollama create example -f Modelfile
```
### Step 3: Run your model
Next, test the model with `ollama run`:
```
ollama run example "What is your favourite condiment?"
```
## Importing (PyTorch & Safetensors)
> Importing from PyTorch and Safetensors is a longer process than importing from GGUF. Improvements that make it easier are a work in progress.
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### Setup
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First, clone the `ollama/ollama` repo:
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```
git clone git@github.com:ollama/ollama.git ollama
cd ollama
```
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and then fetch its `llama.cpp` submodule:
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```shell
git submodule init
git submodule update llm/llama.cpp
```
Next, install the Python dependencies:
```
python3 -m venv llm/llama.cpp/.venv
source llm/llama.cpp/.venv/bin/activate
pip install -r llm/llama.cpp/requirements.txt
```
Then build the `quantize` tool:
```
make -C llm/llama.cpp quantize
```
### Clone the HuggingFace repository (optional)
If the model is currently hosted in a HuggingFace repository, first clone that repository to download the raw model.
Install [Git LFS](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage), verify it's installed, and then clone the model's repository:
```
git lfs install
git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 model
```
### Convert the model
> Note: some model architectures require using specific convert scripts. For example, Qwen models require running `convert-hf-to-gguf.py` instead of `convert.py`
```
python llm/llama.cpp/convert.py ./model --outtype f16 --outfile converted.bin
```
### Quantize the model
```
llm/llama.cpp/quantize converted.bin quantized.bin q4_0
```
### Step 3: Write a `Modelfile`
Next, create a `Modelfile` for your model:
```
FROM quantized.bin
TEMPLATE "[INST] {{ .Prompt }} [/INST]"
```
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### Step 4: Create the Ollama model
Finally, create a model from your `Modelfile`:
```
ollama create example -f Modelfile
```
### Step 5: Run your model
Next, test the model with `ollama run`:
```
ollama run example "What is your favourite condiment?"
```
## Publishing your model (optional early 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](https://ollama.ai/signup)
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2. Run `cat ~/.ollama/id_ed25519.pub` to view your Ollama public key. Copy this to the clipboard.
3. Add your public key to your [Ollama account](https://ollama.ai/settings/keys)
Next, copy your model to your username's namespace:
```
ollama cp example <your username>/example
```
Then push the model:
```
ollama push <your username>/example
```
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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`
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- `f16`