- Welcome to the InstructLab CLI
- ๐บ What's New
- โ What is
ilab
- ๐ Requirements
- โ Getting started
- ๐ป Creating new knowledge or skills and training the model
- ๐ฃ Chat with the new model (not optional this time)
- ๐ Upgrade InstructLab to latest version
- ๐ Submit your new knowledge or skills
- ๐ฌ Contributing
InstructLab ๐ถ uses a novel synthetic data-based alignment tuning method for Large Language Models (LLMs.) The "lab" in InstructLab ๐ถ stands for Large-Scale Alignment for ChatBots [1].
[1] Shivchander Sudalairaj*, Abhishek Bhandwaldar*, Aldo Pareja*, Kai Xu, David D. Cox, Akash Srivastava*. "LAB: Large-Scale Alignment for ChatBots", arXiv preprint arXiv: 2403.01081, 2024. (* denotes equal contributions)
InstructLab release 0.17.0 on June 14, 2024 contains updates to the ilab
CLI design. The ilab
commands now fall into groups for an easier workflow and understanding of the commands. For more information, see the InstructLab CLI reference To view all the available flags for each command group, use the --help
tag after the command. The original commands are still in effect, but will be deprecated in release 0.19.0 on July 11, 2024.
ilab
is a Command-Line Interface (CLI) tool that allows you to perform the following actions:
- Download a pre-trained Large Language Model (LLM).
- Chat with the LLM.
To add new knowledge and skills to the pre-trained LLM, add information to the companion taxonomy repository.
After you have added knowledge and skills to the taxonomy, you can perform the following actions:
- Use
ilab
to generate new synthetic training data based on the changes in your localtaxonomy
repository. - Re-train the LLM with the new training data.
- Chat with the re-trained LLM to see the results.
graph TD;
download-->chat
chat[Chat with the LLM]-->add
add[Add new knowledge\nor skill to taxonomy]-->generate[generate new\nsynthetic training data]
generate-->train
train[Re-train]-->|Chat with\nthe re-trained LLM\nto see the results|chat
For an overview of the full workflow, see the workflow diagram.
Important
We have optimized InstructLab so that community members with commodity hardware can perform these steps. However, running InstructLab on a laptop will provide a low-fidelity approximation of synthetic data generation
(using the ilab data generate
command) and model instruction tuning (using the ilab model train
command, which uses QLoRA). To achieve higher quality, use more sophisticated hardware and configure InstructLab to use a
larger teacher model such as Mixtral.
- ๐ Apple M1/M2/M3 Mac or ๐ง Linux system (tested on Fedora). Note Linux is not fully supported (testing a trained model does not currently work on Linux). We anticipate support for more operating systems in the future.
- C++ compiler
- Python 3.10 or Python 3.11
- Approximately 60GB disk space (entire process)
NOTE: Python 3.12 is currently not supported, because some dependencies don't work on Python 3.12, yet.
NOTE: When installing the
ilab
CLI on macOS, you may have to run thexcode select --install
command, installing the required packages previously listed.
-
When installing on Fedora Linux, install C++, Python 3.10 or 3.11, and other necessary tools by running the following command:
sudo dnf install gcc gcc-c++ make git python3.11 python3.11-devel
If you are running on macOS, this installation is not necessary and you can begin your process with the following step.
-
Create a new directory called
instructlab
to store the files theilab
CLI needs when running andcd
into the directory by running the following command:mkdir instructlab cd instructlab
NOTE: The following steps in this document use Python venv for virtual environments. However, if you use another tool such as pyenv or Conda Miniforge for managing Python environments on your machine continue to use that tool instead. Otherwise, you may have issues with packages that are installed but not found in
venv
. -
There are a few ways you can locally install the
ilab
CLI. Select your preferred installation method from the following instructions. You can then installilab
and activate yourvenv
environment.NOTE: โณ
pip install
may take some time, depending on your internet connection. In case installation fails with errorunsupported instruction `vpdpbusd'
, append-C cmake.args="-DLLAMA_NATIVE=off"
topip install
command.See the GPU acceleration documentation for how to to enable hardware acceleration for interaction and training on AMD ROCm, Apple Metal Performance Shaders (MPS), and Nvidia CUDA.
python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install instructlab \ --extra-index-url=https://download.pytorch.org/whl/cpu \ -C cmake.args="-DLLAMA_NATIVE=off"
NOTE: Additional Build Argument for Intel Macs
If you have an Mac with an Intel CPU, you must add a prefix of
CMAKE_ARGS="-DLLAMA_METAL=off"
to thepip install
command to ensure that the build is done without Apple M-series GPU support.(venv) $ CMAKE_ARGS="-DLLAMA_METAL=off" pip install ...
python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install instructlab \ --extra-index-url https://download.pytorch.org/whl/rocm6.0 \ -C cmake.args="-DLLAMA_HIPBLAS=on" \ -C cmake.args="-DAMDGPU_TARGETS=all" \ -C cmake.args="-DCMAKE_C_COMPILER=/opt/rocm/llvm/bin/clang" \ -C cmake.args="-DCMAKE_CXX_COMPILER=/opt/rocm/llvm/bin/clang++" \ -C cmake.args="-DCMAKE_PREFIX_PATH=/opt/rocm" \ -C cmake.args="-DLLAMA_NATIVE=off"
On Fedora 40+, use
-DCMAKE_C_COMPILER=clang-17
and-DCMAKE_CXX_COMPILER=clang++-17
.NOTE: Make sure your system Python build is
Mach-O 64-bit executable arm64
by usingfile -b $(command -v python)
.python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install instructlab
python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install instructlab \ -C cmake.args="-DLLAMA_CUDA=on" \ -C cmake.args="-DLLAMA_NATIVE=off"
-
From your
venv
environment, verifyilab
is installed correctly, by running theilab
command.ilab
Example output of the
ilab
command(venv) $ ilab Usage: ilab [OPTIONS] COMMAND [ARGS]... CLI for interacting with InstructLab. If this is your first time running InstructLab, it's best to start with `ilab config init` to create the environment. Options: --config PATH Path to a configuration file. [default: config.yaml] --version Show the version and exit. --help Show this message and exit. Command: config Command group for Interacting with the Config of InstructLab data Command group for Interacting with the Data of generated by... model Command group for Interacting with the Models in InstructLab sysinfo Print system information taxonomy Command group for Interacting with the Taxonomy in InstructLab Aliases: chat: model chat convert: model convert diff: taxonomy diff download: model download generate: data generate init: config init serve: model serve test: model test train: model train
IMPORTANT: every
ilab
command needs to be run from within your Python virtual environment. To enter the Python environment, run the following command:source venv/bin/activate
-
Optional: You can enable tab completion for the
ilab
command.Enable tab completion in
bash
with the following command:eval "$(_ILAB_COMPLETE=bash_source ilab)"
To have this enabled automatically every time you open a new shell, you can save the completion script and source it from
~/.bashrc
:_ILAB_COMPLETE=bash_source ilab > ~/.ilab-complete.bash echo ". ~/.ilab-complete.bash" >> ~/.bashrc
Enable tab completion in
zsh
with the following command:eval "$(_ILAB_COMPLETE=zsh_source ilab)"
To have this enabled automatically every time you open a new shell, you can save the completion script and source it from
~/.zshrc
:_ILAB_COMPLETE=zsh_source ilab > ~/.ilab-complete.zsh echo ". ~/.ilab-complete.zsh" >> ~/.zshrc
Enable tab completion in
fish
with the following command:_ILAB_COMPLETE=fish_source ilab | source
To have this enabled automatically every time you open a new shell, you can save the completion script and source it from
~/.bashrc
:_ILAB_COMPLETE=fish_source ilab > ~/.config/fish/completions/ilab.fish
-
Initialize
ilab
by running the following command:ilab config init
Example output
Welcome to InstructLab CLI. This guide will help you set up your environment. Please provide the following values to initiate the environment [press Enter for defaults]: Path to taxonomy repo [taxonomy]: <ENTER>
-
When prompted by the interface, press Enter to add a new default
config.yaml
file. -
When prompted, clone the
https://github.com/instructlab/taxonomy.git
repository into the current directory by typing y.Optional: If you want to point to an existing local clone of the
taxonomy
repository, you can pass the path interactively or alternatively with the--taxonomy-path
flag.Example output after initializing
ilab
(venv) $ ilab config init Welcome to InstructLab CLI. This guide will help you set up your environment. Please provide the following values to initiate the environment [press Enter for defaults]: Path to taxonomy repo [taxonomy]: <ENTER> `taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y Cloning https://github.com/instructlab/taxonomy.git... Generating `config.yaml` in the current directory... Initialization completed successfully, you're ready to start using `ilab`. Enjoy!
ilab
will use the default configuration file unless otherwise specified. You can override this behavior with the--config
parameter for anyilab
command.
-
Run the
ilab model download
command.ilab model download
ilab model download
downloads a compact pre-trained version of the model (~4.4G) from HuggingFace and store it in amodels
directory:(venv) $ ilab model download Downloading model from instructlab/merlinite-7b-lab-GGUF@main to models... (venv) $ ls models merlinite-7b-lab-Q4_K_M.gguf
NOTE โณ This command can take few minutes or immediately depending on your internet connection or model is cached. If you have issues connecting to Hugging Face, refer to the Hugging Face discussion forum for more details.
-
Specify repository, model, and a Hugging Face token if necessary. More information about Hugging Face tokens can be found here
HF_TOKEN=<YOUR HUGGINGFACE TOKEN GOES HERE> ilab model download --repository=TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF --filename=mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
-
Specify repository, and a Hugging Face token if necessary. For example:
HF_TOKEN=<YOUR HUGGINGFACE TOKEN GOES HERE> ilab model download --repository=mistralai/Mixtral-8x7B-v0.1
-
Serve the model by running the following command:
ilab model serve
-
Serve a non-default model (e.g. Mixtral-8x7B-Instruct-v0.1):
ilab model serve --model-path models/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
-
Once the model is served and ready, you'll see the following output:
(venv) $ ilab model serve INFO 2024-03-02 02:21:11,352 lab.py:201 Using model 'models/ggml-merlinite-7b-lab-Q4_K_M.gguf' with -1 gpu-layers and 4096 max context size. Starting server process After application startup complete see http://127.0.0.1:8000/docs for API. Press CTRL+C to shut down the server.
NOTE: If multiple
ilab
clients try to connect to the same InstructLab server at the same time, the 1st will connect to the server while the others will start their own temporary server. This will require additional resources on the host machine.
Because you're serving the model in one terminal window, you will have to create a new window and re-activate your Python virtual environment to run ilab model chat
command:
source venv/bin/activate
ilab model chat
Chat with a non-default model (e.g. Mixtral-8x7B-Instruct-v0.1):
source venv/bin/activate
ilab model chat --model models/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
Before you start adding new skills and knowledge to your model, you can check its baseline performance by asking it a question such as what is the capital of Canada?
.
NOTE: the model needs to be trained with the generated synthetic data to use the new skills or knowledge
(venv) $ ilab model chat
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ system โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ Welcome to InstructLab Chat w/ GGML-MERLINITE-7B-lab-Q4_K_M (type /h for help) โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
>>b> what is the capital of Canada [S][default]
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ggml-merlinite-7b-lab-Q4_K_M โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ The capital city of Canada is Ottawa. It is located in the province of Ontario, on the southern banks of the Ottawa River in the eastern portion of southern Ontario. The city serves as the political center for Canada, as it is home to โ
โ Parliament Hill, which houses the House of Commons, Senate, Supreme Court, and Cabinet of Canada. Ottawa has a rich history and cultural significance, making it an essential part of Canada's identity. โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ elapsed 12.008 seconds โโฏ
>>> [S][default]
- Contribute new knowledge or compositional skills to your local taxonomy repository.
Detailed contribution instructions can be found in the taxonomy repository.
Important
There is a limit to how much content can exist in the question/answer pairs for the model to process. Due to this, only add a maximum of around 2300 words to your question and answer seed example pairs in the qna.yaml
file.
-
List your new data by running the following command:
ilab taxonomy diff
-
To ensure
ilab
is registering your new knowledge or skills, you can run theilab taxonomy diff
command. The following is the expected result after adding the new compositional skillfoo-lang
:(venv) $ ilab taxonomy diff compositional_skills/writing/freeform/foo-lang/foo-lang.yaml Taxonomy in /taxonomy/ is valid :)
Before following these instructions, ensure the existing model you are adding skills or knowledge to is still running.
-
To generate a synthetic dataset based on your newly added knowledge or skill set in taxonomy repository, run the following command:
ilab data generate
Use a non-default model (e.g. Mixtral-8x7B-Instruct-v0.1) to generate data, run the following command:
ilab data generate --model models/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
NOTE: โณ This can take from 15 minutes to 1+ hours to complete, depending on your computing resources.
Example output of
ilab data generate
(venv) $ ilab data generate INFO 2024-02-29 19:09:48,804 lab.py:250 Generating model 'ggml-merlinite-7b-lab-Q4_K_M' using 10 CPUs, taxonomy: '/home/username/instructlab/taxonomy' and seed 'seed_tasks.json' 0%|##########| 0/100 Cannot find prompt.txt. Using default prompt. 98%|##########| 98/100 INFO 2024-02-29 20:49:27,582 generate_data.py:428 Generation took 5978.78s
The synthetic data set will be three files in the newly created
generated
directory namedgenerated*.json
,test*.jsonl
, andtrain*.jsonl
.
Note
If you want to pickup from where a failed or canceled ilab data generate
left off, you can copy the
generated*.json
file into a file named regen.json
. regen.json
will be picked up at the start of lab generate
when available. You should remove it when the process is completed.
-
Verify the files have been created by running the
ls generated
command.(venv) $ ls generated/ 'generated_ggml-merlinite-7b-lab-0226-Q4_K_M_2024-02-29T19 09 48.json' 'train_ggml-merlinite-7b-lab-0226-Q4_K_M_2024-02-29T19 09 48.jsonl' 'test_ggml-merlinite-7b-lab-0226-Q4_K_M_2024-02-29T19 09 48.jsonl'
Optional: It is also possible to run the generate step against a different model via an OpenAI-compatible API. For example, the one spawned by
ilab model serve
or any remote or locally hosted LLM (e.g. viaollama
,LM Studio
, etc.). Run the following command:ilab data generate --endpoint-url http://localhost:8000/v1
There are many options for training the model with your synthetic data-enhanced dataset.
Note: Every
ilab
command needs to run from within your Python virtual environment.
ilab model train
NOTE: โณ This step can potentially take several hours to complete depending on your computing resources. Please stop
ilab model chat
andilab model serve
first to free resources.
ilab model train
outputs a brand-new model that can be served in the models
directory called ggml-model-f16.gguf
.
(venv) $ ls models
ggml-merlinite-7b-lab-Q4_K_M.gguf ggml-model-f16.gguf
To train the model locally on your M-Series Mac is as easy as running:
ilab model train
Note: โณ This process will take a little while to complete (time can vary based on hardware and output of
ilab data generate
but on the order of 5 to 15 minutes)
ilab model train
outputs a brand-new model that is saved in the <model_name>-mlx-q
directory called adapters.npz
(in Numpy
compressed array format). For example:
(venv) $ ls instructlab-merlinite-7b-lab-mlx-q
adapters-010.npz adapters-050.npz adapters-090.npz config.json tokenizer.model
adapters-020.npz adapters-060.npz adapters-100.npz model.safetensors tokenizer_config.json
adapters-030.npz adapters-070.npz adapters.npz special_tokens_map.json
adapters-040.npz adapters-080.npz added_tokens.json tokenizer.jso
Training has experimental support for GPU acceleration with Nvidia CUDA or AMD ROCm. Please see the GPU acceleration documentation for more details. At present, hardware acceleration requires a data center GPU or high-end consumer GPU with at least 18 GB free memory.
ilab model train --device=cuda
Follow the instructions in Training.
โณ Approximate amount of time taken on each platform:
- Google Colab: 5-10 minutes with a T4 GPU
- Kaggle: ~30 minutes with a P100 GPU.
After that's done, you can play with your model directly in the Google Colab or Kaggle notebook. Model trained on the cloud will be saved on the cloud. The model can also be downloaded and served locally.
-
Run the following command to test the model:
ilab model test
NOTE: ๐ This step is only implemented for macOS with M-series chips (for now)
The output from the command will consist of a series of outputs from the model before and after training.
-
Stop the server you have running by entering
ctrl+c
keys in the terminal running the server.IMPORTANT:
-
๐ This step is only implemented for macOS with M-series chips (for now).
-
Before serving the newly trained model you must convert it to work with the
ilab
cli. Theilab model convert
command converts the new model into quantized GGUF format which is required by the server to host the model in theilab model serve
command.
-
-
Convert the newly trained model by running the following command:
ilab model convert
-
Serve the newly trained model locally via
ilab model serve
command with the--model-path
argument to specify your new model:ilab model serve --model-path <new model path>
Which model should you select to serve? After running the
ilab model convert
command, some files and a directory are generated. The model you will want to serve ends with an extension of.gguf
and exists in a directory with the suffixtrained
. For example:instructlab-merlinite-7b-lab-trained/instructlab-merlinite-7b-lab-Q4_K_M.gguf
.
-
Try the fine-tuned model out live using the chat interface, and see if the results are better than the untrained version of the model with chat by running the following command:
ilab model chat -m <New model name>
If you are interested in optimizing the quality of the model's responses, please see
TROUBLESHOOTING.md
-
To upgrade InstructLab to the latest version, use the following command:
pip install instructlab --upgrade
Of course, the final step is, if you've improved the model, to open a pull-request in the taxonomy repository that includes the files (e.g. qna.yaml
) with your improved data.
Check out our contributing guide to learn how to contribute.