165 lines
3.8 KiB
Markdown
165 lines
3.8 KiB
Markdown
# Import a model
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This guide walks through importing a GGUF, PyTorch or Safetensors model.
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## Importing (GGUF)
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### Step 1: Write 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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```
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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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```
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FROM ./mistral-7b-v0.1.Q4_0.gguf
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TEMPLATE "[INST] {{ .Prompt }} [/INST]"
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```
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### Step 2: 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 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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```
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Next, install the Python dependencies:
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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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```
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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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```
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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. Run `cat ~/.ollama/id_ed25519.pub` (or `type %USERPROFILE%\.ollama\id_ed25519.pub` on Windows) to view your Ollama public key. Copy this to the clipboard.
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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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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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