base on InstructLab Command-Line Interface. Use this to chat with a model and execute the InstructLab workflow to train a model using custom taxonomy data. # InstructLab 🐶 (`ilab`)
## 📖 Contents
- [Welcome to the InstructLab CLI](#welcome-to-the-instructlab-cli)
- [❓ What is `ilab`](#-what-is-ilab)
- [📋 Requirements](#-requirements)
- [✅ Getting started](#-getting-started)
- [🧰 Installing `ilab`](#-installing-ilab)
- [Install with Apple Metal on M1/M2/M3 Macs](#install-with-apple-metal-on-m1m2m3-macs)
- [Install with no GPU acceleration and PyTorch without CUDA bindings](#install-using-pytorch-without-cuda-bindings-and-no-gpu-acceleration)
- [Install with AMD ROCm](#install-with-amd-rocm)
- [Install with Nvidia CUDA](#install-with-nvidia-cuda)
- [🏗️ Initialize `ilab`](#️-initialize-ilab)
- [📥 Download the model](#-download-the-model)
- [🍴 Serving the model](#-serving-the-model)
- [📣 Chat with the model (Optional)](#-chat-with-the-model-optional)
- [🚀 Upgrade InstructLab to latest version](#-upgrade-instructlab-to-latest-version)
- [💻 Creating new knowledge or skills and training the model](#-creating-new-knowledge-or-skills-and-training-the-model)
- [🎁 Contribute knowledge or compositional skills](#-contribute-knowledge-or-compositional-skills)
- [📜 List and validate your new data](#-list-and-validate-your-new-data)
- [🚀 Generate a synthetic dataset](#-generate-a-synthetic-dataset)
- [👩🏫 Training the model](#-training-the-model)
- [✋ Before you begin training](#-before-you-begin-training)
- [InstructLab training pipelines](#instructlab-model-training-pipelines)
- [Train the model locally](#train-the-model-locally)
- [Train the model locally on an M-Series Mac or on Linux using the full pipeline](#train-the-model-locally-on-an-m-series-mac-or-on-linux-using-the-full-pipeline)
- [Train the model locally on an M-Series Mac or on Linux using the simple pipeline](#train-the-model-locally-on-an-m-series-mac-or-on-linux-using-the-simple-pipeline)
- [Train the model locally with GPU acceleration](#train-the-model-locally-with-gpu-acceleration)
- [Train the model in the cloud](#train-the-model-in-the-cloud)
- [📜 Test the newly trained model](#-test-the-newly-trained-model)
- [🧪 Evaluate the newly trained model](#-evaluate-the-newly-trained-model)
- [🍴 Serve the newly trained model](#-serve-the-newly-trained-model)
- [📣 Chat with the new model (not optional this time)](#-chat-with-the-new-model-not-optional-this-time)
- [☁️ Upload the new model](#️-upload-the-new-model)
- [🎁 Submit your new knowledge or skills](#-submit-your-new-knowledge-or-skills)
- [📬 Contributing](#-contributing)
## Welcome to the InstructLab CLI
InstructLab 🐶 uses a novel synthetic data-based alignment tuning method for
Large Language Models (LLMs.) The "**lab**" in Instruct**Lab** 🐶 stands for
[**L**arge-Scale **A**lignment for Chat**B**ots](https://arxiv.org/abs/2403.01081) [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)
## ❓ What is `ilab`
`ilab` is a Command-Line Interface (CLI) tool that allows you to perform the following actions:
1. Download a pre-trained Large Language Model (LLM).
1. Chat with the LLM.
To add new knowledge and skills to the pre-trained LLM, add information to the companion [taxonomy](https://github.com/instructlab/taxonomy.git) repository.
After you have added knowledge and skills to the taxonomy, you can perform the following actions:
1. Use `ilab` to generate new synthetic training data based on the changes in your local `taxonomy` repository.
1. Re-train the LLM with the new training data.
1. Chat with the re-trained LLM to see the results.
```mermaid
graph TD;
download-->chat
chat[Chat with the LLM]-->add
add[Add new knowledge<br/>or skill to taxonomy]-->generate[generate new<br/>synthetic training data]
generate-->train
train[Re-train]-->|Chat with<br/>the re-trained LLM<br/>to see the results|chat
```
For an overview of the full workflow, see the [workflow diagram](./docs/workflow.png).
> [!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](https://huggingface.co/docs/transformers/model_doc/mixtral).
## 📋 Requirements
- **🍎 Apple M1/M2/M3 Mac or 🐧 Linux system** (tested on Fedora). We anticipate support for more operating systems in the future.
📋 When installing the `ilab` CLI on macOS, you may have to run the `xcode-select --install` command, installing the required packages listed.
- C++ compiler
- Python 3.10 or Python 3.11
📋 Python 3.12 is currently not supported. Some InstructLab dependencies don't work on Python 3.12, yet.
- Minimum 250GB disk space. Approximately 500GB disk space is recommended for the entire InstructLab end-to-end process.
## ✅ Getting started
- When installing on Fedora Linux, install C++, Python 3.10 or 3.11, and other necessary tools by running the following command:
```shell
sudo dnf install gcc gcc-c++ make git python3.11 python3.11-devel
```
Some Python version management tools that build Python (instead of using a pre-built binary) may not by default build libraries implemented in C, and CPython when installing a Python version. This can result in the following error when running the `ilab data generate` command: `ModuleNotFoundError: No module named '_lzma'`. This can be resolved by building CPython during the Python installation with the `--enable-framework`. For example for `pyenv` on MacOS: `PYTHON_CONFIGURE_OPTS="--enable-framework" pyenv install 3.x`. You may need to recreate your virtual environment after reinstalling Python.
> [!NOTE]
> The following steps in this document use [Python venv](https://docs.python.org/3/library/venv.html) for virtual environments. However, if you use another tool such as [pyenv](https://github.com/pyenv/pyenv) or [Conda Miniforge](https://github.com/conda-forge/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 your virtual environment.
### 🧰 Installing `ilab`
1. There are a few ways you can locally install the `ilab` CLI. Select your preferred installation method from the following instructions. You can then install `ilab` and activate your `venv` environment.
⚠️ The `python3` binary shown in the installation guides are the Python version that you installed in the above step. The command can also be `python3.11` or `python3.10` instead of `python3`. You can check Python's version by `python3 -V`.
⏳ `pip install` may take some time, depending on your internet connection. In case the installation fails with error ``unsupported instruction `vpdpbusd'``, append `-C cmake.args="-DLLAMA_NATIVE=off"` to `pip install` command.
See [the GPU acceleration documentation](./docs/gpu-acceleration.md) for how to to enable hardware acceleration for interaction and training on AMD ROCm, Apple Metal Performance Shaders (MPS), and Nvidia CUDA.
#### Install with Apple Metal on M1/M2/M3 Macs
- Install on Apple metal with:
```shell
python<version> -m venv --upgrade-deps venv
source venv/bin/activate
pip cache remove llama_cpp_python
pip install instructlab
```
📋 Make sure your system Python build is `Mach-O 64-bit executable arm64` by using `file -b $(command -v python)`, or if your system is setup with [pyenv](https://github.com/pyenv/pyenv) by using the `file -b $(pyenv which python)` command.
You can also quickly install using the [Bash script](https://github.com/instructlab/instructlab/blob/main/scripts/ilab-macos-installer.sh).
#### Install using PyTorch without CUDA bindings and no GPU acceleration
- Install on a standard Linux machine with:
```shell
python<version> -m venv --upgrade-deps venv
source venv/bin/activate
pip install instructlab
```
*Additional Build Argument for Intel Macs*
If you have a Mac with an Intel CPU, you must add a prefix of
`CMAKE_ARGS="-DLLAMA_METAL=off"` to the `pip install` command to ensure
that the build is done without Apple M-series GPU support.
`(venv) $ CMAKE_ARGS="-DLLAMA_METAL=off" pip install ...`
#### Install with AMD ROCm
- Install on AMD ROCm with:
```shell
python3 -m venv --upgrade-deps venv
source venv/bin/activate
pip cache remove llama_cpp_python
CMAKE_ARGS="-DLLAMA_HIPBLAS=on \
-DAMDGPU_TARGETS=all \
-DCMAKE_C_COMPILER=/opt/rocm/llvm/bin/clang \
-DCMAKE_CXX_COMPILER=/opt/rocm/llvm/bin/clang++ \
-DCMAKE_PREFIX_PATH=/opt/rocm \
-DLLAMA_NATIVE=off" \
pip install 'instructlab[rocm]' \
--extra-index-url https://download.pytorch.org/whl/rocm6.0
```
On Fedora 40+, use `-DCMAKE_C_COMPILER=clang-17` and `-DCMAKE_CXX_COMPILER=clang++-17`.
#### Install with Nvidia CUDA
- For the best CUDA experience, installing vLLM is necessary to serve Safetensors format models.
```shell
python3 -m venv --upgrade-deps venv
source venv/bin/activate
pip cache remove llama_cpp_python
CMAKE_ARGS="-DLLAMA_CUDA=on -DLLAMA_NATIVE=off" pip install 'instructlab[cuda]'
pip install vllm@git+https://github.com/opendatahub-io/
[email protected]
```
4. From your `venv` environment, verify `ilab` is installed correctly, by running the `ilab` command.
```shell
ilab
```
*Example output of the `ilab` command*
```shell
(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:
/home/user/.config/instructlab/config.yaml]
-v, --verbose Enable debug logging (repeat for even more verbosity)
--version Show the version and exit.
--help Show this message and exit.
Commands:
config Command Group for Interacting with the Config of InstructLab.
data Command Group for Interacting with the Data generated by...
model Command Group for Interacting with the Models in InstructLab.
system Command group for all system-related command calls
taxonomy Command Group for Interacting with the Taxonomy of InstructLab.
Aliases:
chat model chat
generate data generate
serve model serve
train model train
```
> [!IMPORTANT]
> Every `ilab` command needs to be run from within your Python virtual environment. You can enter the Python environment by running the `source venv/bin/activate` command.
5. *Optional:* You can enable tab completion for the `ilab` command.
#### Bash (version 4.4 or newer)
Enable tab completion in `bash` with the following command:
```sh
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`:
```sh
_ILAB_COMPLETE=bash_source ilab > ~/.ilab-complete.bash
echo ". ~/.ilab-complete.bash" >> ~/.bashrc
```
📋 To use Bash version 4.4 or higher on macOS (default is 3.2.57), ensure your login shell is set to the updated version. You can verify this with `echo $SHELL`. If you encounter the error `bash: complete: nosort: invalid option name`, check your terminal or configuration files (e.g., ~/.bash_profile, ~/.bashrc, ~/.profile) to see whether they are referencing the old version for login.
#### Zsh
Enable tab completion in `zsh` with the following command:
```sh
eval "$(_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`:
```sh
_ILAB_COMPLETE=zsh_source ilab > ~/.ilab-complete.zsh
echo ". ~/.ilab-complete.zsh" >> ~/.zshrc
```
#### Fish
Enable tab completion in `fish` with the following command:
```sh
_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`:
```sh
_ILAB_COMPLETE=fish_source ilab > ~/.config/fish/completions/ilab.fish
```
### 🏗️ Initialize `ilab`
1. Initialize `ilab` by running the following command:
```shell
ilab config init
```
2. When prompted, clone the `https://github.com/instructlab/taxonomy.git` repository into the current directory by typing **enter**
**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.
`ilab` will use the default configuration file unless otherwise specified. You can override this behavior with the `--config` parameter for any `ilab` command.
4. When prompted, provide the path to your default model. Otherwise, the default of a quantized [Granite](https://huggingface.co/instructlab/granite-7b-lab-GGUF) model is used.
*Example output of steps 1 - 3*
```shell
Path to your model [/home/user/.cache/instructlab/models/granite-7b-lab-Q4_K_M.gguf]: <ENTER>
```
You can download this model with `ilab model download` command as well.
5. When prompted, please choose a train profile. Train profiles are GPU specific profiles that enable accelerated training behavior. If you are on MacOS or a Linux machine without a dedicated GPU, please choose `No Profile (CPU, Apple Metal, AMD ROCm)` by hitting Enter. There are various flags you can utilize with individual `ilab` commands that allow you to utilize your GPU if applicable.
*Example output of selecting a training profile*
```shell
----------------------------------------------------
Welcome to the InstructLab CLI
This guide will help you to setup your environment
----------------------------------------------------
Please provide the following values to initiate the environment [press Enter for defaults]:
Path to taxonomy repo [/home/user/.local/share/instructlab/taxonomy]:
Path to your model [/home/user/.cache/instructlab/models/granite-7b-lab-Q4_K_M.gguf]:
You can download this model with `ilab model download` command as well.
4. The InstructLab CLI auto-detects your hardware and select the exact system profile that matches your machine. System profiles populate the `config.yaml` file with the proper parameter values based on your detected CPU/GPU types. This system is only applicable to Apple M-Series Chips, Nvidia GPUs, and Intel/AMD CPUs.
*Example output of profile auto-detection*
```shell
Generating config file and profiles:
/home/user/.config/instructlab/config.yaml
/home/user/.local/share/instructlab/internal/train_configuration/profiles
We have detected the AMD CPU profile as an exact match for your system.
--------------------------------------------
Initialization completed successfully!
You're ready to start using `ilab`. Enjoy!
--------------------------------------------
```
5. If there is not an exact match for your system, you can manually select a system profile when prompted. There are various flags you can utilize with individual `ilab` commands that allow you to utilize your GPU if applicable.
*Example output of selecting a system profile*
```shell
Please choose a system profile to use.
System profiles apply to all parts of the config file and set hardware specific defaults for each command.
First, please select the hardware vendor your system falls into
[1] APPLE
[2] INTEL
[3] AMD
[4] NVIDIA
Enter the number of your choice [0]: 1
You selected: APPLE
Next, please select the specific hardware configuration that most closely matches your system.
[0] No system profile
[1] APPLE M1 ULTRA
[2] APPLE M1 MAX
[3] APPLE M2 MAX
[4] APPLE M2 ULTRA
[5] APPLE M2 PRO
[6] APPLE M2
[7] APPLE M3 MAX
[8] APPLE M3 PRO
[9] APPLE M3
Enter the number of your choice [hit enter for hardware defaults] [0]: 8
You selected: /home/<user>/.local/share/instructlab/internal/system_profiles/apple/m3/m3_pro.yaml
--------------------------------------------
Initialization completed successfully!
You're ready to start using `ilab`. Enjoy!
--------------------------------------------
```
The GPU profiles are listed by GPU type and number of GPUs present. If you happen to have a GPU configuration with a similar amount of vRAM as any of the above profiles, feel free to try them out!
### `ilab` directory layout after initializing your system
After running `ilab config init` your directories will look like the following on a Linux system:
| **Directory** | **Description** |
|--------------------------------------------|---------------------------------------------------------------------------------|
| `~/.cache/instructlab/models/` | Contains all downloaded large language models, including the saved output of ones you generate with ilab.|
| `~/.local/share/instructlab/datasets/` | Contains data output from the SDG phase, built on modifications to the taxonomy repository. |
| `~/.local/share/instructlab/taxonomy/` | Contains the skill and knowledge data. |
| `~/.local/share/instructlab/checkpoints/` | Contains the output of the training process. |
| `~/.config/instructlab/config.yaml` | Contains the `config.yaml` file |
You can view your `config.yaml` file with the following command (use `-k` to show a specific config section and/or `-wc` to show without comments):
```shell
ilab config show
```
### 📥 Download the model
For detailed documentation on the InstructLab LLMs and their functions, see the [InstructLab LLM guide](docs/instructlab_models.md).
#### Downloading the default InstructLab models
- Run the `ilab model download` command to download a compact pre-trained version of the `granite-7b-lab-GGUF`, `merlinite-7b-lab-GGUF`, and `Mistral-7B-Instruct-v0.2-GGUF` models (~4.4G each) from HuggingFace.
```shell
ilab model download
```
*Example output*
```shell
Downloading model from Hugging Face:
Model: instructlab/granite-7b-lab-GGUF@main
Destination: /Users/<user>/.cache/instructlab/models
Downloading model from Hugging Face:
Model: instructlab/merlinite-7b-lab-GGUF@main
Destination: /Users/<user>/.cache/instructlab/models
Downloading model from Hugging Face:
Model: TheBloke/Mistral-7B-Instruct-v0.2-GGUF@main
Destination: /Users/<user>/.cache/instructlab/models
TheBloke/Mistral-7B-Instruct-v0.2-GGUF requires a HF Token to be set.
Please use '--hf-token' or 'export HF_TOKEN' to download all necessary models.
```
You may be prompted to use your Hugging Face token to download the `Mistral-7B-Instruct-v0.2-GGUF` model.
```shell
ilab model download --hf-token <your-huggingface-token>
```
Hugging Face `hf_transfer` is enabled by default for faster downloads (lacks proxy support), and can be disabled with `HF_HUB_ENABLE_HF_TRANSFER=0`. If you have issues connecting to Hugging Face, refer to the [Hugging Face discussion forum](https://discuss.huggingface.co/) for more details.
#### Downloading a specific model from a Hugging Face repository
- Specify a repository, model, and a Hugging Face token if necessary. More information about Hugging Face tokens can be found [here](https://huggingface.co/docs/hub/en/security-tokens)
```shell
ilab model download --repository instructlab/granite-7b-lab-GGUF --filename granite-7b-lab-Q4_K_M.gguf --hf-token <your-huggingface-token>
```
#### Downloading an entire Hugging Face repository (Safetensors Model)
- Specify a repository, and a Hugging Face token if necessary. For example:
```shell
ilab model download --repository instructlab/granite-7b-lab --hf-token <your-hugginface-token>
```
These types of models are useful for GPU-enabled systems or anyone looking to serve a model using vLLM. InstructLab provides Safetensor versions of our Granite models on HuggingFace.
#### Listing downloaded models
- All downloaded models can be seen with `ilab model list`.
```shell
ilab model list
```
*Example output of `ilab model list` after `ilab model download`*
```shell
(venv) $ ilab model list
+-------------------------------------+---------------------+--------+
| Model Name | Last Modified | Size |
+-------------------------------------+---------------------+--------+
| granite-7b-lab-Q4_K_M.gguf | 2024-08-01 15:05:48 | 4.1 GB |
| merlinite-7b-lab-Q4_K_M.gguf | 2024-08-01 15:05:48 | 4.1 GB |
| mistral-7b-instruct-v0.2.Q4_K_M.gguf| 2024-08-01 15:05:48 | 4.1 GB |
+-------------------------------------+---------------------+--------+
```
### 🍴 Serving the model
- Serve the model with the `ilab model serve` command:
```shell
ilab model serve
```
*Example output of a model that is served and ready*
```shell
(venv) $ ilab model serve
INFO 2024-03-02 02:21:11,352 lab.py:201 Using model 'models/ggml-granite-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.
```
📋 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.
#### Serving other models
- You can serve a non-default model (e.g. Mixtral-8x7B-Instruct-v0.1) with the following example command:
```shell
ilab model serve --model-path ~/.cache/instructlab/models/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
```
- You can serve a non-default Safetensors model (e.g. granite-7b-lab). This requires a GPU.
Verify vllm is installed:
```shell
pip show vllm
```
If it is not, please run:
```shell
pip install vllm@git+https://github.com/opendatahub-io/
[email protected]
```
Then you can serve a Safetensors model:
```shell
ilab model serve --model-path ~/.cache/instructlab/models/instructlab/granite-7b-lab
```
### 📣 Chat with the model (Optional)
- Because you're serving the model in one terminal window, you will have to create a new terminal window and re-activate your Python virtual environment to run `ilab model chat` command:
```shell
source venv/bin/activate
ilab model chat
```
- Chat with a non-default model (e.g. Mixtral-8x7B-Instruct-v0.1):
```shell
source venv/bin/activate
ilab model chat --model ~/.cache/instructlab/models/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf
```
> [!TIP]
> The usage of `--model` necessitates that the existing server has that model. If not, you must exit the server. `--model` in `ilab model chat` has the ability to start a server on your behalf with the specified model if one is not already running on the port.
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?`.
The model needs to be trained with the generated synthetic data to use the new skills or knowledge
*Example output of chatbot*
```shell
(venv) $ ilab model chat
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────── system ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Welcome to InstructLab Chat w/ GGML-GRANITE-7B-lab-Q4_K_M (type /h for help) │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
>>> what is the capital of Canada? [S][default]
╭────────────────────────────────────────────────────────────────────────────────────────────────────── ggml-granite-7b-lab-Q4_K_M ───────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 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 │
│ 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. │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── elapsed 12.008 seconds ─╯
>>> [S][default]
```
- You can also adjust the temperature settings of the model with the `--temperature` or `-t` flag. The default is 1.0.
```shell
ilab model chat --model ~/.cache/instructlab/models/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --temperature 0.5
```
## 🚀 Upgrade InstructLab to latest version
1. To upgrade InstructLab to the latest version, use the following command:
```shell
pip install instructlab --upgrade
```
2. It is recommended to reinitialize your InstructLab environment after upgrading to the latest version.
```shell
ilab config init
```
> [!NOTE]
> If you are having issues with your upgrade, you can attempt `pip install --force-reinstall --no-cache-dir instructlab` to force reinstallation of InstructLab without using the pip cache
## 💻 Creating new knowledge or skills and training the model
### 🎁 Contribute knowledge or compositional skills
1. Contribute new knowledge or compositional skills to your local [taxonomy](https://github.com/instructlab/taxonomy.git) repository.
Detailed contribution instructions can be found in the [taxonomy repository](https://github.com/instructlab/taxonomy/blob/main/README.md).
> [!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 and validate your new data
- You can use the `ilab taxonomy diff` command to ensure `ilab` is registering your new knowledge or skills and your contributions are properly formatted. This command displays any new or modified YAML files within your taxonomy tree. For example, the following is the expected result of a valid compositional skill contribution after adding a new skill called `foo-lang` to the freeform writing subdirectory:
```shell
(venv) $ ilab taxonomy diff
compositional_skills/writing/freeform/foo-lang/qna.yaml
Taxonomy in $HOME/.local/share/instructlab/taxonomy is valid :)
```
- You can also validate your entire taxonomy by performing a diff against an empty base by using the `--taxonomy-base=empty` argument:
```shell
(venv) $ ilab taxonomy diff --taxonomy-base=empty
compositional_skills/general/tables/empty/qna.yaml
compositional_skills/general/tables/editing/add_remove/qna.yaml
...
Taxonomy in $HOME/.local/share/instructlab/taxonomy is valid :)
```
### 🚀 Generate a synthetic dataset
Before following these instructions, ensure the existing model you are adding skills or knowledge to is still running. Alternatively, `ilab data generate` can start a server for you if you provide a fully qualified model path via `--model`.
> [!IMPORTANT]
> The `full` pipeline only works with Mixtral and Mistral Instruct Family models as the teacher model.
> The `simple` pipeline only works with Merlinite 7b Lab as the teacher model. This is due to only supporting specific model prompt templates.
1. To generate a synthetic dataset based on your newly added knowledge or skill set in [taxonomy](https://github.com/instructlab/taxonomy.git) repository, run the following command:
*Default SDG*
```shell
ilab data generate
```
*With GPU acceleration*
```shell
ilab data generate --pipeline full --gpus <NUM_OF_GPUS>
```
*Without GPU acceleration:*
```shell
ilab data generate --pipeline simple
```
*Using a non-default model (e.g. Mixtral-8x7B-Instruct-v0.1) to generate data:*
```shell
ilab data generate --model ~/.cache/instructlab/models/mistralai/mixtral-8x7b-instruct-v0.1 --pipeline full --gpus 4
```
⏳ This can take from 15 minutes to 1+ hours to complete, depending on your computing resources.
*Example output of `ilab data generate`*
```shell
(venv) $ ilab data generate
INFO 2024-07-30 19:57:44,093 numexpr.utils:161: NumExpr defaulting to 8 threads.
INFO 2024-07-30 19:57:44,452 datasets:58: PyTorch version 2.3.1 available.
Generating synthetic data using 'full' pipeline, 'mistral-7b-instruct-v0.2.Q4_K_M.gguf' model, './taxonomy' taxonomy, against http://localhost:8000/v1 server
INFO 2024-07-30 19:57:45,084 instructlab.sdg:375: Synthesizing new instructions. If you aren't satisfied with the generated instructions, interrupt training (Ctrl-C) and try adjusting your YAML files. Adding more examples may help.
INFO 2024-07-30 19:57:45,090 instructlab.sdg.pipeline:153: Running pipeline single-threaded
INFO 2024-07-30 19:57:47,820 instructlab.sdg.llmblock:51: LLM server supports batched inputs: False
INFO 2024-07-30 19:57:47,820 instructlab.sdg.pipeline:197: Running block: gen_skill_freeform
INFO 2024-07-30 19:57:47,820 instructlab.sdg.pipeline:198: Dataset({
features: ['task_description', 'seed_question', 'seed_response'],
num_rows: 5
})
INFO 2024-07-30 20:02:16,455 instructlab.sdg:411: Generated 1 samples
...
```
The synthetic data set will be two files in the newly created in the datasets directory: `~/.local/share/instructlab/datasets`. These files will be named `skills_train_msgs_*.jsonl` and `knowledge_train_msgs_*.jsonl`.
2. Verify the files have been created by running the `ilab data list` command.
```shell
(venv) $ ilab data list
mistral-7b-instruct-v0.2.Q4_K_M 2024-11-12 02:37:38
+--------------------------------------------------------------------+---------------------+-----------+
| Dataset | Created At | File size |
+--------------------------------------------------------------------+---------------------+-----------+
| knowledge_train_msgs_2024-11-12T02_37_38.jsonl | 2024-11-12 05:38:26 | 3.92 MB |
| messages_mistral-7b-instruct-v0.2.Q4_K_M_2024-11-12T02_37_38.jsonl | 2024-11-12 05:38:21 | 317.08 KB |
| node_datasets_2024-11-12T02_37_38/compositional_skills_valid.jsonl | 2024-11-12 03:07:58 | 139.00 KB |
| node_datasets_2024-11-12T02_37_38/knowledge_valid_p07.jsonl | 2024-11-12 05:38:21 | 3.84 MB |
| node_datasets_2024-11-12T02_37_38/knowledge_valid_p10.jsonl | 2024-11-12 05:38:21 | 7.25 MB |
| node_datasets_2024-11-12T02_37_38/mmlubench_knowledge_valid.jsonl | 2024-11-12 05:38:21 | 48.94 KB |
| skills_train_msgs_2024-11-12T02_37_38.jsonl | 2024-11-12 05:38:29 | 7.36 MB |
| test_mistral-7b-instruct-v0.2.Q4_K_M_2024-11-12T02_37_38.jsonl | 2024-11-12 02:37:38 | 27.25 KB |
| train_mistral-7b-instruct-v0.2.Q4_K_M_2024-11-12T02_37_38.jsonl | 2024-11-12 05:38:21 | 272.94 KB |
+--------------------------------------------------------------------+---------------------+-----------+
```
*Generating synthetic data on a different model:* 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. via [`ollama`](https://ollama.com/), [`LM Studio`](https://lmstudio.ai), etc.). Run the following command:
```shell
ilab data generate --endpoint-url http://localhost:8000/v1
```
*Generating synthetic data on the entire taxonomy repo:* You can generate a synthetic dataset based on the entire contents of the taxonomy repo using the `--taxonomy-base=empty` option:
```shell
ilab data generate --taxonomy-base=empty
```
### 👩🏫 Training the model
There are many options for training the model with your synthetic data-enhanced dataset.
### ✋ Before you begin training
There are a few models you need to download before running the InstructLab end-to-end workflow locally.
- Download the `granite-7b-lab` model for training:
```shell
ilab model download --repository instructlab/granite-7b-lab
```
- Download the `prometheus-8x7b-v2.0` for *multi-phase training* and *benchmark evaluation*. This model is not required for `simple` or `full` training.
```shell
ilab model download --repository prometheus-eval/prometheus-8x7b-v2.0 --hf-token <your-huggingface-token>
```
#### InstructLab model training pipelines
`ilab model train` has three pipelines: `simple`, `full`, and `accelerated`. The default is `full`.
1. `simple` uses an SFT Trainer on Linux and MLX on MacOS. This type of training takes roughly an hour and produces the lowest fidelity model but should indicate if your data is being picked up by the training process.
2. `full` uses a custom training loop and data processing functions for the granite family of models. This loop is optimized for CPU and MPS functionality. Please use `--pipeline=full` in combination with `--device=cpu` (Linux) or `--device=mps` (MacOS). You can also use `--device=cpu` on a MacOS machine. However, MPS is optimized for better performance on these systems.
3. `accelerated` uses the instructlab-training library which supports GPU accelerated and distributed training. The `full` loop and data processing functions are either pulled directly from or based off of the work in this library.
After running `ilab model train`, the output locations depend on the chosen pipeline or strategy:
| **Pipeline/Strategy** | **Operating System** | **Output Location/Details** |
|------------------------------------|----------------------|--------------------------------------------------------------------------------------------------------------------------|
| `simple` | Linux | Model saved in `models` directory as `ggml-model-f16.gguf`. |
| `simple` | MacOS | Model saved in `<model_name>-mlx-q` directory. |
| `full` | Linux & MacOS | `.bin` and `.gguf` models saved in `~/.local/share/instructlab/checkpoints/hf_format` directory. Two models in each `sample_*` directory: one quantized (`Q4-M-K` suffix) and one full precision. |
| `accelerated` | Linux | Models saved in `~/.local/share/instructlab/checkpoints`. Can be evaluated with `ilab model evaluate` to choose the best one. |
| `lab-multiphase` | Linux | Phase 1 models saved in `~/.local/share/instructlab/phased/phase1/checkpoints` (Knowledge training). Phase 2 models saved in `~/.local/share/instructlab/phased/phase2/checkpoints` (Skills training). Evaluation is run for phase 2 to identify the best checkpoint. |
| `lab-skills-only` | Linux | Phase 2 models saved in `~/.local/share/instructlab/phased/phase2/checkpoints` (Skills training). Evaluation is run for phase 2 to identify the best checkpoint. |
To limit training time, you can adjust the `num_epoch` paramater in the `config.yaml` file. The maximum number of epochs for running the InstructLab end-to-end workkflow is 10.
#### Train the model locally
- Train the model with your synthetic data with the `ilab model train` command:
```shell
ilab model train
```
⏳ This step can potentially take **several hours** to complete depending on your computing resources. Please stop `ilab model chat` and `ilab model serve` first to free resources.
When running multi phase training evaluation is run on each phase, we will tell you which checkpoint in this folder performs the best.
#### Train the model locally on an M-series Mac or on Linux using the full pipeline
- To train the model locally on your M-Series Mac using our full pipeline and MPS or on your Linux laptop/desktop using CPU:
```shell
ilab model train --pipeline full --device mps --data-path <path-to-sdg-dataset>
```
```shell
ilab model train --pipeline full --device cpu --data-path <path-to-sdg-dataset>
```
*Example command*
```shell
ilab model train --pipeline full --device cpu --data-path ~/.local/share/instructlab/datasets/knowledge_train_msgs_2024-10-23T09_14_44.jsonl
```
⏳ This process will take a while to complete. If you run for ~8 epochs it will take several hours.
`ilab model train` outputs a directory for each epoch that resembles the following structure:
```shell
$ ls ~/.local/share/instructlab/checkpoints/hf_format/samples_0/
├── added_tokens.json
├── config.json
├── pytorch_model.bin
├── pytorch_model.gguf
├── pytorch_model-Q4_K_M.gguf
├── special_tokens_map.json
├── tokenizer.json
├── tokenizer_config.json
└── tokenizer.model
```
This entire folder can be served on a system that supports vLLM using the `.bin` model. However, on most laptops you can serve either the full precision gguf: `pytorch_model.gguf` or the 4-bit-quantized one: `pytorch_model-Q4_K_M.gguf`.
#### Train the model locally on an M-series Mac or on Linux using the simple pipeline
- To train the model locally on your M-Series Mac using our simple pipeline and MLX or on your Linux laptop/desktop using an SFT Trainer:
```shell
ilab model train --pipeline simple
```
⏳ 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)
On a Mac `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:
```shell
(venv) $ ls instructlab-granite-7b-lab-mlx-q/
├── adapters-010.npz
├── adapters-020.npz
├── adapters-030.npz
├── adapters-040.npz
├── adapters-050.npz
├── adapters-060.npz
├── adapters-070.npz
├── adapters-080.npz
├── adapters-090.npz
├── adapters-100.npz
├── adapters.npz
├── added_tokens.json
├── config.json
├── model.safetensors
├── special_tokens_map.json
├── tokenizer.json
├── tokenizer.model
└── tokenizer_config.json
```
#### Train the model locally with GPU acceleration
- Training has support for GPU acceleration with Nvidia CUDA or AMD ROCm. Please see [the GPU acceleration documentation](./docs/gpu-acceleration.md) for more details. At present, hardware acceleration requires a data center GPU or high-end consumer GPU with at least 18 GB free memory.
```shell
ilab model train --pipeline accelerated --device cuda --data-path <path-to-sdg-data>
```
*Example command*
```shell
ilab model train --pipeline full --device cuda --data-path ~/.local/share/instructlab/datasets/knowledge_train_msgs_2024-10-23T09_14_44.jsonl
```
This version of `ilab model train` outputs brand-new models that can be served in the `~/.local/share/instructlab/checkpoints` directory. These models can be run through `ilab model evaluate` to choose the best one.
#### Train the model locally with multi-phase training and GPU acceleration
- `ilab model train` supports multi-phase training. This results in the following workflow:
1. We train the model on knowledge
2. Evaluate the trained model to find the best checkpoint
3. We train the model on skills
4. We evaluate the model to find the best overall checkpoint
```shell
ilab model train --strategy lab-multiphase --phased-phase1-data <knowledge train messages jsonl> --phased-phase2-data <skills train messages jsonl> -y
```
This command takes in two `.jsonl` files from your `datasets` directory, one is the knowledge jsonl and the other is a skills jsonl. The `-y` flag skips an interactive prompt asking the user if they are sure they want to run multi-phase training.
⏳ This command may take 3 or more hours depending on the size of the data and number of training epochs you run.
#### Train the model in the cloud
Follow the instructions in [Training](./notebooks/README.md).
⏳ 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.
### 📜 Test the newly trained model
- Run the following command to test the model:
```shell
ilab model test
```
The output from the command will consist of a series of outputs from the model before and after training.
### 🧪 Evaluate the newly trained model
You can use the `ilab model evaluate` command to evaluate the models you are training with several benchmarks. Currently, four benchmarks are supported.
| Benchmark | Measures | Full Name | Description | Reference |
| --- | --- | --- | --- | --- |
| MMLU | Knowledge | Massive Multitask Language Understanding | Tests a model against a standardized set of knowledge data and produces a score based on the model's performance | [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) |
| MMLUBranch | Knowledge | N/A | Tests your knowledge contributions against a base model and produces a score based on the difference in performance | N/A |
| MTBench | Skills | Multi-turn Benchmark | Tests a model's skill at applying its knowledge against a judge model and produces a score based on the model's performance | [MT-Bench (Multi-turn Benchmark)](https://klu.ai/glossary/mt-bench-eval) |
| MTBenchBranch | Skills | N/A | Tests your skill contributions against a judge model and produces a score based on the difference in performance | N/A |
> [!NOTE]
> Evaluation must be used with local models (safetensors or GGUF format). Using models directly from Hugging Face without downloading them is unsupported.
> GGUF models are not yet supported for mmlu and mmlu_branch evaluations
> MTBench and MTBenchBranch use [prometheus-8x7b-v2.0](https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0) as the judge model by
default. While you do not need to use this model as your judge, it is strongly recommended to do so if you have the necessary hardware
resources. You can download it via `ilab model download`.
#### Running MMLU
- Example of running MMLU with a local safetensors model directory:
```bash
$ export ILAB_MODELS_DIR=$HOME/.local/share/instructlab/models
$ ilab model evaluate --benchmark mmlu --model $ILAB_MODELS_DIR/instructlab/granite-7b-test
...
# KNOWLEDGE EVALUATION REPORT
## MODEL (SCORE)
/home/user/.cache/instructlab/models/instructlab/granite-7b-test (0.52/1.0)
### SCORES (0.0 to 1.0):
mmlu_abstract_algebra - 0.31
mmlu_anatomy - 0.46
mmlu_astronomy - 0.52
mmlu_business_ethics - 0.55
mmlu_clinical_knowledge - 0.57
mmlu_college_biology - 0.56
mmlu_college_chemistry - 0.38
mmlu_college_computer_science - 0.46
mmlu_college_mathematics - 0.34
mmlu_college_medicine - 0.49
mmlu_college_physics - 0.27
mmlu_computer_security - 0.66
mmlu_conceptual_physics - 0.38
mmlu_econometrics - 0.39
mmlu_electrical_engineering - 0.48
mmlu_elementary_mathematics - 0.3
...
```
The output of MMLU displays a much longer list of subjects.
#### Running MMLUBranch
- Example of running MMLUBranch with a local safetensors model directory:
```bash
$ export ILAB_MODELS_DIR=$HOME/.local/share/instructlab/models
$ ilab model evaluate --benchmark mmlu_branch --model $ILAB_MODELS_DIR/instructlab/granite-7b-test --base-model $ILAB_MODELS_DIR/instructlab/granite-7b-lab
...
# KNOWLEDGE EVALUATION REPORT
## BASE MODEL (SCORE)
/home/user/.cache/instructlab/models/instructlab/granite-7b-lab (0.74/1.0)
## MODEL (SCORE)
/home/user/.cache/instructlab/models/instructlab/granite-7b-test (0.78/1.0)
### IMPROVEMENTS (0.0 to 1.0):
1. tonsils: 0.74 -> 0.78 (+0.04)
```
#### Running MTBench
- Example of running MTBench with a local safetensors model directory:
```bash
$ export ILAB_MODELS_DIR=$HOME/.local/share/instructlab/models
$ ilab model evaluate --benchmark mt_bench --model $ILAB_MODELS_DIR/instructlab/granite-7b-test
...
# SKILL EVALUATION REPORT
## MODEL (SCORE)
/home/user/.cache/instructlab/models/instructlab/granite-7b-test (7.27/10.0)
### TURN ONE (0.0 to 10.0):
7.48
### TURN TWO (0.0 to 10.0):
7.05
```
#### Running MTBenchBranch
- Below is an example of running MTBenchBranch with a local safetensors model directory:
```bash
$ export ILAB_MODELS_DIR=$HOME/.local/share/instructlab/models
$ export ILAB_TAXONOMY_DIR=$HOME/.local/share/instructlab/taxonomy
$ ilab model evaluate --benchmark mt_bench_branch \
--model $ILAB_MODELS_DIR/instructlab/granite-7b-test \
--base-model $ILAB_MODELS_DIR/instructlab/granite-7b-lab \
--taxonomy-path $ILAB_TAXONOMY_DIR \
--branch rc \
--base-branch main
...
# SKILL EVALUATION REPORT
## BASE MODEL (SCORE)
/home/user/.cache/instructlab/models/instructlab/granite-7b-lab (5.78/10.0)
## MODEL (SCORE)
/home/user/.cache/instructlab/models/instructlab/granite-7b-test (6.00/10.0)
### IMPROVEMENTS (0.0 to 10.0):
1. foundational_skills/reasoning/linguistics_reasoning/object_identification/qna.yaml: 4.0 -> 6.67 (+2.67)
2. foundational_skills/reasoning/theory_of_mind/qna.yaml: 3.12 -> 4.0 (+0.88)
3. foundational_skills/reasoning/linguistics_reasoning/logical_sequence_of_words/qna.yaml: 9.33 -> 10.0 (+0.67)
### REGRESSIONS (0.0 to 10.0):
1. foundational_skills/reasoning/unconventional_reasoning/lower_score_wins/qna.yaml: 5.67 -> 4.0 (-1.67)
2. foundational_skills/reasoning/mathematical_reasoning/qna.yaml: 7.33 -> 6.0 (-1.33)
3. foundational_skills/reasoning/temporal_reasoning/qna.yaml: 5.67 -> 4.67 (-1.0)
### NO CHANGE (0.0 to 10.0):
1. foundational_skills/reasoning/linguistics_reasoning/odd_one_out/qna.yaml (9.33)
2. compositional_skills/grounded/linguistics/inclusion/qna.yaml (6.5)
```
### 🍴 Serve the newly trained model
If you have a server running, stop the server you have running by entering `ctrl+c` keys in the terminal running the server.
*🍎 Mac only:* Before serving the newly trained model you must convert it to work with
the `ilab` cli. The `ilab model convert` command converts the new model into quantized [GGUF](https://medium.com/@sandyeep70/ggml-to-gguf-a-leap-in-language-model-file-formats-cd5d3a6058f9) format which is required by the server to host the model in the `ilab model serve` command.
- Convert the newly trained model by running the following command:
```shell
ilab model convert
```
1. Serve the newly trained model locally via `ilab model serve` command with the `--model-path`
argument to specify your new model:
```shell
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 suffix `trained`. For example:
`instructlab-granite-7b-lab-trained/instructlab-granite-7b-lab-Q4_K_M.gguf`.
## 📣 Chat with the new model (not optional this time)
- 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:
```shell
ilab model chat -m <New model path>
```
If you are interested in optimizing the quality of the model's responses, please see [`TROUBLESHOOTING.md`](./TROUBLESHOOTING.md#model-fine-tuning-and-response-optimization)
## ☁️ Upload the new model
If you are happy with your newly-trained model and wish to upload it to a supported endpoint, you can do so with the `ilab model upload` command.
InstructLab currently supports uploading to [Hugging Face](https://huggingface.co/), with plans to support OCI and S3 uploads as well.
You can upload safetensors or a GGUF to any destination you have access to - see `ilab model upload --help` for more info.
## 🎁 Submit your new knowledge or skills
Of course, the final step is, if you've improved the model, to open a pull-request in the [taxonomy repository](https://github.com/instructlab/taxonomy) that includes the files (e.g. `qna.yaml`) with your improved data.
## 📬 Contributing
Check out our [contributing](CONTRIBUTING/CONTRIBUTING.md) guide to learn how to contribute.
", Assign "at most 3 tags" to the expected json: {"id":"9984","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"