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导入模型

本指南将引导您导入 GGUF、PyTorch 或 Safetensors 模型。

导入 (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.

Setup

First, clone the ollama/ollama repo:

git clone git@github.com:ollama/ollama.git ollama
cd ollama

and then fetch its llama.cpp submodule:

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, 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]"

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
  2. Copy your Ollama public key:
  3. macOS: cat ~/.ollama/id_ed25519.pub
  4. Windows: type %USERPROFILE%\.ollama\id_ed25519.pub
  5. Linux: cat /usr/share/ollama/.ollama/id_ed25519.pub
  6. 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.com/<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
  • f16