Michael Yang
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docs/import.md
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docs/import.md
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# Import a model
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# Import
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This guide walks through importing a GGUF, PyTorch or Safetensors model.
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GGUF models and select Safetensors models can be imported directly into Ollama.
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## Importing (GGUF)
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## Import GGUF
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### Step 1: Write a `Modelfile`
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A binary GGUF file can be imported directly into Ollama through a Modelfile.
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Start by creating a `Modelfile`. This file is the blueprint for your model, specifying weights, parameters, prompt templates and more.
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```
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FROM ./mistral-7b-v0.1.Q4_0.gguf
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```dockerfile
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FROM /path/to/file.gguf
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```
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(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`:
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## Import Safetensors
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```
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FROM ./mistral-7b-v0.1.Q4_0.gguf
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TEMPLATE "[INST] {{ .Prompt }} [/INST]"
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If the model being imported is one of these architectures, it can be imported directly into Ollama through a Modelfile:
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- LlamaForCausalLM
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- MistralForCausalLM
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- GemmaForCausalLM
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```dockerfile
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FROM /path/to/safetensors/directory
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```
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### Step 2: Create the Ollama model
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For architectures not directly convertable by Ollama, see llama.cpp's [guide](https://github.com/ggerganov/llama.cpp/blob/master/README.md#prepare-and-quantize) on conversion. After conversion, see [Import GGUF](#import-gguf).
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Finally, create a model from your `Modelfile`:
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## Automatic Quantization
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> [!NOTE]
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> Automatic quantization requires v0.1.35 or higher.
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Ollama is capable of quantizing FP16 or FP32 models to any of the supported quantizations with the `-q/--quantize` flag in `ollama create`.
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```dockerfile
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FROM /path/to/my/gemma/f16/model
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```
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ollama create example -f Modelfile
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```
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### Step 3: Run your model
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Next, test the model with `ollama run`:
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```
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ollama run example "What is your favourite condiment?"
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```
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## Importing (PyTorch & Safetensors)
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> 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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```
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git clone git@github.com:ollama/ollama.git ollama
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cd ollama
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```
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and then fetch its `llama.cpp` submodule:
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```shell
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git submodule init
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git submodule update llm/llama.cpp
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$ ollama create -q Q4_K_M mymodel
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transferring model data
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quantizing F16 model to Q4_K_M
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creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
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creating new layer sha256:0853f0ad24e5865173bbf9ffcc7b0f5d56b66fd690ab1009867e45e7d2c4db0f
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writing manifest
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success
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```
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Next, install the Python dependencies:
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### Supported Quantizations
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```
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python3 -m venv llm/llama.cpp/.venv
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source llm/llama.cpp/.venv/bin/activate
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pip install -r llm/llama.cpp/requirements.txt
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<details>
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<summary>Legacy Quantization</summary>
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- `Q4_0`
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- `Q4_1`
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- `Q5_0`
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- `Q5_1`
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- `Q8_0`
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</details>
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<details>
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<summary>K-means Quantization</summary>`
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- `Q3_K_S`
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- `Q3_K_M`
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- `Q3_K_L`
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- `Q4_K_S`
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- `Q4_K_M`
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- `Q5_K_S`
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- `Q5_K_M`
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- `Q6_K`
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</details>
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> [!NOTE]
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> Activation-aware Weight Quantization (i.e. IQ) are not currently supported for automatic quantization however you can still import the quantized model into Ollama, see [Import GGUF](#import-gguf).
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## Template Detection
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> [!NOTE]
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> Template detection requires v0.1.42 or higher.
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Ollama uses model metadata, specifically `tokenizer.chat_template`, to automatically create a template appropriate for the model you're importing.
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```dockerfile
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FROM /path/to/my/gemma/model
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```
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Then build the `quantize` tool:
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```
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make -C llm/llama.cpp quantize
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```shell
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$ ollama create mymodel
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transferring model data
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using autodetected template gemma-instruct
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creating new layer sha256:baa2a0edc27d19cc6b7537578a9a7ba1a4e3214dc185ed5ae43692b319af7b84
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creating new layer sha256:ba66c3309914dbef07e5149a648fd1877f030d337a4f240d444ea335008943cb
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writing manifest
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success
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```
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### Clone the HuggingFace repository (optional)
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If the model is currently hosted in a HuggingFace repository, first clone that repository to download the raw model.
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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:
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```
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git lfs install
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git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 model
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```
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### Convert the model
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> Note: some model architectures require using specific convert scripts. For example, Qwen models require running `convert-hf-to-gguf.py` instead of `convert.py`
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```
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python llm/llama.cpp/convert.py ./model --outtype f16 --outfile converted.bin
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```
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### Quantize the model
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```
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llm/llama.cpp/quantize converted.bin quantized.bin q4_0
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```
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### Step 3: Write a `Modelfile`
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Next, create a `Modelfile` for your model:
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```
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FROM quantized.bin
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TEMPLATE "[INST] {{ .Prompt }} [/INST]"
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```
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### Step 4: Create the Ollama model
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Finally, create a model from your `Modelfile`:
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```
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ollama create example -f Modelfile
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```
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### Step 5: Run your model
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Next, test the model with `ollama run`:
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```
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ollama run example "What is your favourite condiment?"
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```
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## Publishing your model (optional – early alpha)
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Publishing models is in early alpha. If you'd like to publish your model to share with others, follow these steps:
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1. Create [an account](https://ollama.com/signup)
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2. Copy your Ollama public key:
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- macOS: `cat ~/.ollama/id_ed25519.pub | pbcopy`
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- Windows: `type %USERPROFILE%\.ollama\id_ed25519.pub`
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- Linux: `cat /usr/share/ollama/.ollama/id_ed25519.pub`
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3. Add your public key to your [Ollama account](https://ollama.com/settings/keys)
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Next, copy your model to your username's namespace:
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```
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ollama cp example <your username>/example
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```
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> Note: model names may only contain lowercase letters, digits, and the characters `.`, `-`, and `_`.
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Then push the model:
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```
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ollama push <your username>/example
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```
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After publishing, your model will be available at `https://ollama.com/<your username>/example`.
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## Quantization reference
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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.
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- `q2_K`
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- `q3_K`
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- `q3_K_S`
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- `q3_K_M`
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- `q3_K_L`
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- `q4_0` (recommended)
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- `q4_1`
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- `q4_K`
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- `q4_K_S`
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- `q4_K_M`
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- `q5_0`
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- `q5_1`
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- `q5_K`
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- `q5_K_S`
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- `q5_K_M`
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- `q6_K`
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- `q8_0`
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- `f16`
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Defining a template in the Modelfile will disable this feature which may be useful if you want to use a different template than the autodetected one.
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