194 lines
4.5 KiB
Markdown
194 lines
4.5 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 ./q4_0.bin
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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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### Supported models
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Ollama supports a set of model architectures, with support for more coming soon:
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- Llama & Mistral
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- Falcon & RW
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- BigCode
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To view a model's architecture, check the `config.json` file in its HuggingFace repo. You should see an entry under `architectures` (e.g. `LlamaForCausalLM`).
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### Step 1: 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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```
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git lfs install
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git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
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cd Mistral-7B-Instruct-v0.1
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```
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### Step 2: Convert and quantize to a `.bin` file (optional, for PyTorch and Safetensors)
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If the model is in PyTorch or Safetensors format, a [Docker image](https://hub.docker.com/r/ollama/quantize) with the tooling required to convert and quantize models is available.
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First, Install [Docker](https://www.docker.com/get-started/).
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Next, to convert and quantize your model, run:
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```
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docker run --rm -v .:/model ollama/quantize -q q4_0 /model
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```
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This will output two files into the directory:
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- `f16.bin`: the model converted to GGUF
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- `q4_0.bin` the model quantized to a 4-bit quantization (we will use this file to create the Ollama model)
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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 ./q4_0.bin
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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 ./q4_0.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.ai/signup)
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2. Run `cat ~/.ollama/id_ed25519.pub` 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.ai/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.ai/<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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## Manually converting & quantizing models
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### Prerequisites
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Start by cloning the `llama.cpp` repo to your machine in another directory:
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```
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git clone https://github.com/ggerganov/llama.cpp.git
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cd llama.cpp
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```
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Next, install the Python dependencies:
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```
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pip install -r requirements.txt
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```
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Finally, build the `quantize` tool:
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```
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make quantize
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```
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### Convert the model
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Run the correct conversion script for your model architecture:
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```shell
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# LlamaForCausalLM or MistralForCausalLM
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python convert.py <path to model directory>
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# FalconForCausalLM
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python convert-falcon-hf-to-gguf.py <path to model directory>
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# GPTBigCodeForCausalLM
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python convert-starcoder-hf-to-gguf.py <path to model directory>
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```
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### Quantize the model
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```
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quantize <path to model dir>/ggml-model-f32.bin <path to model dir>/q4_0.bin q4_0
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```
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