| Name | llm-finetuning-hub JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | LLM Finetuning resource hub + toolkit |
| upload_time | 2024-03-28 14:47:59 |
| maintainer | None |
| docs_url | None |
| author | Benjamin Ye |
| requires_python | <=3.12,>=3.9 |
| license | Apache 2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
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|
# LLM Finetuning Toolkit
<p align="center">
<img src="assets/toolkit-animation.gif" width="900" />
</p>
## Overview
LLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM finetuning experiments on your data and gathering their results. From one single `yaml` config file, control all elements of a typical experimentation pipeline - **prompts**, **open-source LLMs**, **optimization strategy** and **LLM testing**.
<p align="center">
<img src="assets/overview_diagram.png" width="900" />
</p>
## Installation
### Clone Repository
```shell
git clone https://github.com/georgian-io/LLM-Finetuning-Hub.git
cd LLM-Finetuning-Hub/
```
### [Option 1] Docker (recommended)
```shell
docker build -t llm-toolkit
```
#### CPU Only
```shell
docker run -it llm-toolkit
```
#### With GPU
```shell
docker run -it --gpus all llm-toolkit
```
### [Option 2] Poetry (recommended)
See poetry documentation page for poetry [installation instructions](https://python-poetry.org/docs/#installation)
```shell
poetry install
```
### [Option 3] pip
```shell
pip install -r requirements.txt
```
### [Option 4] Conda
```shell
conda create --name llm-toolkit python=3.11
conda activate llm-toolkit
pip install -r requirements.txt
```
## Quick Start
This guide contains 3 stages that will enable you to get the most out of this toolkit!
* __Basic__: Run your first LLM fine-tuning experiment
* __Intermediate__: Run a custom experiment by changing the componenets of the YAML configuration file
* __Advanced__: Launch series of fine-tuning experiments across different prompt templates, LLMs, optimization techniques -- all through **one** YAML configuration file
### Basic
```python
python toolkit.py --config ./config.yml
```
This command initiates the fine-tuning process using the settings specified in the default YAML configuration file `config.yaml`.
### Intermediate
The configuration file is the central piece that defines the behavior of the toolkit. It is written in YAML format and consists of several sections that control different aspects of the process, such as data ingestion, model definition, training, inference, and quality assurance. We highlight some of the critical sections.
#### Data Ingestion
An example of what the data ingestion may look like:
```yaml
data:
file_type: "huggingface"
path: "yahma/alpaca-cleaned"
prompt:
### Instruction: {instruction}
### Input: {input}
### Output:
prompt_stub:
{output}
test_size: 0.1 # Proportion of test as % of total; if integer then # of samples
train_size: 0.9 # Proportion of train as % of total; if integer then # of samples
train_test_split_seed: 42
```
* While the above example illustrates using a public dataset from Hugging Face, the config file can also ingest your own data.
```yaml
file_type: "json"
path: "<path to your data file>
```
```yaml
file_type: "csv"
path: "<path to your data file>
```
* The prompt fields help create instructions to fine-tune the LLM on. It reads data from specific columns, mentioned in {} brackets, that are present in your dataset. In the example provided, it is expected for the data file to have column names: `instruction`, `input` and `output`.
* The prompt fields use both `prompt` and `prompt_stub` during fine-tuning. However, during testing, **only** the `prompt` section is used as input to the fine-tuned LLM.
#### LLM Definition
```yaml
model:
hf_model_ckpt: "NousResearch/Llama-2-7b-hf"
quantize: true
bitsandbytes:
load_in_4bit: true
bnb_4bit_compute_dtype: "bf16"
bnb_4bit_quant_type: "nf4"
# LoRA Params -------------------
lora:
task_type: "CAUSAL_LM"
r: 32
lora_dropout: 0.1
target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- up_proj
- down_proj
- gate_proj
```
* While the above example showcases using Llama2 7B, in theory, any open-source LLM supported by Hugging Face can be used in this toolkit.
```yaml
hf_model_ckpt: "mistralai/Mistral-7B-v0.1"
```
```yaml
hf_model_ckpt: "tiiuae/falcon-7b"
```
* The parameters for LoRA, such as the rank `r` and dropout, can be altered.
```yaml
lora:
r: 64
lora_dropout: 0.25
```
#### Quality Assurance
```yaml
qa:
llm_tests:
- length_test
- word_overlap_test
```
* To ensure that the fine-tuned LLM behaves as expected, you can add tests that check if the desired behaviour is being attained. Example: for an LLM fine-tuned for a summarization task, we may want to check if the generated summary is indeed smaller in length than the input text. We would also like to learn the overlap between words in the original text and generated summary.
#### Artifact Outputs
This config will run finetuning and save the results under directory `./experiment/[unique_hash]`. Each unique configuration will generate a unique hash, so that our tool can automatically pick up where it left off. For example, if you need to exit in the middle of the training, by relaunching the script, the program will automatically load the existing dataset that has been generated under the directory, instead of doing it all over again.
After the script finishes running you will see these distinct artifacts:
```shell
/dataset # generated pkl file in hf datasets format
/model # peft model weights in hf format
/results # csv of prompt, ground truth, and predicted values
/qa # csv of test results: e.g. vector similarity between ground truth and prediction
```
Once all the changes have been incorporated in the YAML file, you can simply use it to run a custom fine-tuning experiment!
```python
python toolkit.py --config <path to custom YAML file>
```
### Advanced
Fine-tuning workflows typically involve running ablation studies across various LLMs, prompt designs and optimization techniques. The configuration file can be altered to support running ablation studies.
* Specify different prompt templates to experiment with while fine-tuning.
```yaml
data:
file_type: "huggingface"
path: "yahma/alpaca-cleaned"
prompt:
- >-
This is the first prompt template to iterate over
### Input: {input}
### Output:
- >-
This is the second prompt template
### Instruction: {instruction}
### Input: {input}
### Output:
prompt_stub:
{output}
test_size: 0.1 # Proportion of test as % of total; if integer then # of samples
train_size: 0.9 # Proportion of train as % of total; if integer then # of samples
train_test_split_seed: 42
```
* Specify various LLMs that you would like to experiment with.
```yaml
model:
hf_model_ckpt: ["NousResearch/Llama-2-7b-hf", mistralai/Mistral-7B-v0.1", "tiiuae/falcon-7b"]
quantize: true
bitsandbytes:
load_in_4bit: true
bnb_4bit_compute_dtype: "bf16"
bnb_4bit_quant_type: "nf4"
```
* Specify different configurations of LoRA that you would like to ablate over.
```yaml
lora:
r: [16, 32, 64]
lora_dropout: [0.25, 0.50]
```
## Extending
The toolkit provides a modular and extensible architecture that allows developers to customize and enhance its functionality to suit their specific needs. Each component of the toolkit, such as data ingestion, finetuning, inference, and quality assurance testing, is designed to be easily extendable.
## Contributing
If you would like to contribute to this project, we recommend following the "fork-and-pull" Git workflow.
1. **Fork** the repo on GitHub
2. **Clone** the project to your own machine
3. **Commit** changes to your own branch
4. **Push** your work back up to your fork
5. Submit a **Pull request** so that we can review your changes
NOTE: Be sure to merge the latest from "upstream" before making a pull request!
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"description": "# LLM Finetuning Toolkit \n\n<p align=\"center\">\n <img src=\"assets/toolkit-animation.gif\" width=\"900\" />\n</p>\n\n## Overview\n\nLLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM finetuning experiments on your data and gathering their results. From one single `yaml` config file, control all elements of a typical experimentation pipeline - **prompts**, **open-source LLMs**, **optimization strategy** and **LLM testing**.\n\n<p align=\"center\">\n<img src=\"assets/overview_diagram.png\" width=\"900\" />\n</p>\n\n\n\n## Installation\n\n### Clone Repository\n\n```shell\n git clone https://github.com/georgian-io/LLM-Finetuning-Hub.git\n cd LLM-Finetuning-Hub/\n```\n\n### [Option 1] Docker (recommended)\n\n```shell\n docker build -t llm-toolkit \n``` \n\n#### CPU Only\n\n```shell\n docker run -it llm-toolkit\n```\n\n#### With GPU\n\n```shell\n docker run -it --gpus all llm-toolkit\n```\n\n### [Option 2] Poetry (recommended)\n\nSee poetry documentation page for poetry [installation instructions](https://python-poetry.org/docs/#installation)\n\n```shell\n poetry install\n```\n\n### [Option 3] pip\n\n```shell\n pip install -r requirements.txt\n```\n\n### [Option 4] Conda\n\n```shell\n conda create --name llm-toolkit python=3.11\n conda activate llm-toolkit\n pip install -r requirements.txt\n```\n\n\n## Quick Start\n\nThis guide contains 3 stages that will enable you to get the most out of this toolkit!\n\n* __Basic__: Run your first LLM fine-tuning experiment \n* __Intermediate__: Run a custom experiment by changing the componenets of the YAML configuration file \n* __Advanced__: Launch series of fine-tuning experiments across different prompt templates, LLMs, optimization techniques -- all through **one** YAML configuration file \n\n\n\n### Basic \n\n```python\n python toolkit.py --config ./config.yml\n```\n\nThis command initiates the fine-tuning process using the settings specified in the default YAML configuration file `config.yaml`.\n\n\n### Intermediate\n\nThe configuration file is the central piece that defines the behavior of the toolkit. It is written in YAML format and consists of several sections that control different aspects of the process, such as data ingestion, model definition, training, inference, and quality assurance. We highlight some of the critical sections. \n\n#### Data Ingestion\n\nAn example of what the data ingestion may look like:\n\n```yaml\ndata:\n file_type: \"huggingface\"\n path: \"yahma/alpaca-cleaned\"\n prompt:\n ### Instruction: {instruction}\n ### Input: {input}\n ### Output:\n prompt_stub:\n {output}\n test_size: 0.1 # Proportion of test as % of total; if integer then # of samples\n train_size: 0.9 # Proportion of train as % of total; if integer then # of samples\n train_test_split_seed: 42\n```\n\n* While the above example illustrates using a public dataset from Hugging Face, the config file can also ingest your own data. \n\n```yaml\n file_type: \"json\"\n path: \"<path to your data file>\n```\n\n```yaml\n file_type: \"csv\"\n path: \"<path to your data file>\n```\n\n* The prompt fields help create instructions to fine-tune the LLM on. It reads data from specific columns, mentioned in {} brackets, that are present in your dataset. In the example provided, it is expected for the data file to have column names: `instruction`, `input` and `output`. \n\n* The prompt fields use both `prompt` and `prompt_stub` during fine-tuning. However, during testing, **only** the `prompt` section is used as input to the fine-tuned LLM.\n\n\n#### LLM Definition\n \n```yaml\nmodel:\n hf_model_ckpt: \"NousResearch/Llama-2-7b-hf\"\n quantize: true\n bitsandbytes:\n load_in_4bit: true\n bnb_4bit_compute_dtype: \"bf16\"\n bnb_4bit_quant_type: \"nf4\"\n\n# LoRA Params -------------------\nlora:\n task_type: \"CAUSAL_LM\"\n r: 32\n lora_dropout: 0.1\n target_modules:\n - q_proj\n - v_proj\n - k_proj\n - o_proj\n - up_proj\n - down_proj\n - gate_proj\n```\n\n* While the above example showcases using Llama2 7B, in theory, any open-source LLM supported by Hugging Face can be used in this toolkit.\n\n```yaml\n hf_model_ckpt: \"mistralai/Mistral-7B-v0.1\"\n```\n\n```yaml\n hf_model_ckpt: \"tiiuae/falcon-7b\"\n```\n\n* The parameters for LoRA, such as the rank `r` and dropout, can be altered.\n\n```yaml\n lora:\n r: 64\n lora_dropout: 0.25\n```\n \n#### Quality Assurance\n\n```yaml\nqa:\n llm_tests:\n - length_test \n - word_overlap_test\n```\n\n* To ensure that the fine-tuned LLM behaves as expected, you can add tests that check if the desired behaviour is being attained. Example: for an LLM fine-tuned for a summarization task, we may want to check if the generated summary is indeed smaller in length than the input text. We would also like to learn the overlap between words in the original text and generated summary.\n\n\n#### Artifact Outputs\n\nThis config will run finetuning and save the results under directory `./experiment/[unique_hash]`. Each unique configuration will generate a unique hash, so that our tool can automatically pick up where it left off. For example, if you need to exit in the middle of the training, by relaunching the script, the program will automatically load the existing dataset that has been generated under the directory, instead of doing it all over again.\n\nAfter the script finishes running you will see these distinct artifacts:\n```shell\n /dataset # generated pkl file in hf datasets format\n /model # peft model weights in hf format\n /results # csv of prompt, ground truth, and predicted values\n /qa # csv of test results: e.g. vector similarity between ground truth and prediction\n```\n\nOnce all the changes have been incorporated in the YAML file, you can simply use it to run a custom fine-tuning experiment! \n\n```python\n python toolkit.py --config <path to custom YAML file>\n```\n\n### Advanced \n\n\nFine-tuning workflows typically involve running ablation studies across various LLMs, prompt designs and optimization techniques. The configuration file can be altered to support running ablation studies.\n\n* Specify different prompt templates to experiment with while fine-tuning.\n\n```yaml\ndata:\n file_type: \"huggingface\"\n path: \"yahma/alpaca-cleaned\"\n prompt:\n - >-\n This is the first prompt template to iterate over\n ### Input: {input}\n ### Output: \n - >-\n This is the second prompt template\n ### Instruction: {instruction}\n ### Input: {input}\n ### Output:\n prompt_stub:\n {output}\n test_size: 0.1 # Proportion of test as % of total; if integer then # of samples\n train_size: 0.9 # Proportion of train as % of total; if integer then # of samples\n train_test_split_seed: 42\n```\n\n\n* Specify various LLMs that you would like to experiment with.\n\n```yaml\nmodel:\n hf_model_ckpt: [\"NousResearch/Llama-2-7b-hf\", mistralai/Mistral-7B-v0.1\", \"tiiuae/falcon-7b\"]\n quantize: true\n bitsandbytes:\n load_in_4bit: true\n bnb_4bit_compute_dtype: \"bf16\"\n bnb_4bit_quant_type: \"nf4\"\n```\n\n* Specify different configurations of LoRA that you would like to ablate over.\n\n```yaml\n lora:\n r: [16, 32, 64]\n lora_dropout: [0.25, 0.50]\n```\n\n## Extending\n\nThe toolkit provides a modular and extensible architecture that allows developers to customize and enhance its functionality to suit their specific needs. Each component of the toolkit, such as data ingestion, finetuning, inference, and quality assurance testing, is designed to be easily extendable.\n\n\n## Contributing\n\nIf you would like to contribute to this project, we recommend following the \"fork-and-pull\" Git workflow.\n\n 1. **Fork** the repo on GitHub\n 2. **Clone** the project to your own machine\n 3. **Commit** changes to your own branch\n 4. **Push** your work back up to your fork\n 5. Submit a **Pull request** so that we can review your changes\n\nNOTE: Be sure to merge the latest from \"upstream\" before making a pull request!\n\n\n\n",
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