<div align="center">
<img width="400" height="350" src="/img/cyphertune-logo.webp">
</div>
<h5 align="center">
⚠️<em>Under Active Development</em> ⚠️
</h5>
**CypherTune** is an encapsulated Python library designed to fine-tune large language models (LLMs) specifically for text-2-Cypher tasks. [Cypher](https://neo4j.com/developer/cypher/), the graph query language utilized by Neo4j, is renowned for its efficiency in data retrieval from knowledge graphs. Its user-friendly nature, drawing parallels with SQL, is attributed to an intuitive syntax that resonates well with those familiar with traditional programming languages.
This repository takes inspiration from Neo4j's recent [initiative](https://bratanic-tomaz.medium.com/crowdsourcing-text2cypher-dataset-e65ba51916d4) to create their inaugural open-source text-2-Cypher dataset. Our goal with CypherTune is to simplify the process of fine-tuning LLMs, making it more accessible, especially for individuals who are new to the realm of AI. We aim to lower the barrier of entry for Neo4j users to fine-tune LLMs using the text-2-Cypher dataset once it's released to the public.
Contributions and participation in this crowdsourcing effort is welcomed! If you're interested in being a part of this exciting initiative, feel free to join and contribute to Neo4j's [application](https://text2cypher.vercel.app/) 🚀🚀.
# Features <img align="center" width="30" height="29" src="https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExOTBqaWNrcGxnaTdzMGRzNTN0bGI2d3A4YWkxajhsb2F5MW84Z2dxaCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/26tOZ42Mg6pbTUPHW/giphy.gif">
CypherTune streamlines the training process by abstracting several libraries from the Hugging Face ecosystem. It currently offers the following features:
- **LLM Fine-Tuning**: Fine-tunes LLMs using HF's `Trainer` for custom text-to-Cypher datasets stored in the [Hub](https://huggingface.co/datasets).
- **Bits and Bytes**: Loads model with 4-bit quantization.
- **PEFT**: Uses [LoRA](https://arxiv.org/pdf/2106.09685.pdf) under the hood, a popular and lightweight training technique that significantly reduces the number of trainable parameters. We combine 4-bit quantization lowering the barrier for the amount of memory required during training ([QLoRA](https://arxiv.org/abs/2305.14314)).
- **Dataset Preprocessing**: Converts dataset into a prompt template for fine-tuning.
- **Weights & Biases Integration**: Track and log your experiments using wandb.
TODO
> - Table storing hyperparameter configurations for select training job environments (i.e. depending on dataset size, model type/size and amount/type of compute).
> - Model eval functionality post-training.
> - `Max length` to be determined after dataset is released.
> - Make dataset headers dynamic, right now they are static.
> - Provide inference snippet for testing after training.
> - Fully Sharded Data Parallel (FSDP): Utilizes efficient training across distributed systems.
# Install <img align="center" width="30" height="29" src="https://media.giphy.com/media/sULKEgDMX8LcI/giphy.gif">
```
pip install cyphertune
```
# Start Training <img align="center" width="30" height="29" src="https://media.giphy.com/media/QLcCBdBemDIqpbK6jA/giphy.gif">
To start training, the only requirements are a `project name`, your Hugging Face `model`/`dataset` stubs and the path to your YAML `config_file`. This file includes the essential LoRA and training arguments for fine-tuning. Before beginning the training process, ensure you download the YAML file from this repository using either the curl or wget commands to access its contents.
```bash
curl -o config.yml https://raw.githubusercontent.com/InquestGeronimo/cyphertune/main/cyphertune/config.yml
```
```bash
wget -O config.yml https://raw.githubusercontent.com/InquestGeronimo/cyphertune/main/cyphertune/config.yml
```
The trainer expects to ingest a `train` and `validation` split from your dataset prior to training with a specific format. Here is an [example](https://huggingface.co/datasets/zeroshot/text-2-cypher) of a placeholder dataset, designed to demonstrate the expected format.
```py
from cyphertune import CypherTuner
tuner = CypherTuner(
project_name="cyphertune-training-run1",
model_id="codellama/CodeLlama-7b-Instruct-hf",
dataset_id="zeroshot/text-2-cypher",
config_file="path/to/config.yml"
)
tuner.train()
```
After training completes, the adapter will be saved in your output directory. The pre-trained model will not be saved.
# HyperParameter Knowledge <img align="center" width="30" height="29" src="https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExMXV1bWFyMWxkY3JocjE1ZDMxMWZ5OHZtejFkbHpuZXdveTV3Z3BiciZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/bGgsc5mWoryfgKBx1u/giphy.gif">
Configuring hyperparameters and LoRA settings can be a complex task, especially for those new to AI engineering. Our repository is designed to lessen the dependence on hyperparameters. For instance, once the Text-2-Cypher dataset is fully crowdsourced, we will undertake multiple training sessions. This will help us establish a set of baseline hyperparameters known to yield good results, and we will save them to this repository, thereby streamlining the fine-tuning process for Neo4j users. However, having a thorough understanding of these hyperparameters is still advantageous, particularly if you intend to modify them yourself.
Three key factors affect hyperparameters during training:
1. The type and size of the model.
2. The type and quantity of hardware.
3. The size of the dataset.
For accessing CypherTune's hyperparameters, you can refer to the [config_file](https://github.com/InquestGeronimo/cyphertune/blob/main/cyphertune/config.yml). At the time of writing, the Text-2-Cypher dataset is not publicly available, so the parameters in the file serve as placeholders.
The first set of parameters pertains to the [LoRA](https://huggingface.co/docs/peft/en/package_reference/lora) settings:
```py
# LoRA configuration settings
r=8, # The size of the LoRA's rank. Opting for a higher rank could negate the efficiency benefits of using LoRA. The higher the rank the largar the checkpoint file is.
lora_alpha=16, # This is the scaling factor for LoRA. It controls the magnitude of the adjustments made by LoRA.
target_modules=[ # Specifies the parts of the model where LoRA is applied. These can be components of the transformer architecture.
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
"lm_head",
],
bias="none", # Indicates that biases are not adapted as part of the LoRA process.
lora_dropout=0.05, # The dropout rate for LoRA layers. It's a regularization technique to prevent overfitting.
task_type="CAUSAL_LM" # Specifies the type of task. Here, it indicates the model is for causal language modeling.
```
For further details, refer to the PEFT [documentation](https://huggingface.co/docs/peft/en/package_reference/lora) or read the blogs found at the end of this README.
The following set pertains specifically to the training arguments:
```py
# Trainer configuration settings
output_dir="./output", # Directory where the training outputs and model checkpoints will be written.
warmup_steps=1, # Number of steps to perform learning rate warmup.
per_device_train_batch_size=32, # Batch size per device during training.
gradient_accumulation_steps=1, # Number of updates steps to accumulate before performing a backward/update pass.
gradient_checkpointing=True, # Enables gradient checkpointing to save memory at the expense of slower backward pass.
max_steps=1000, # Total number of training steps to perform.
learning_rate=1e-4, # Initial learning rate for the optimizer.
bf16=True, # Use bfloat16 mixed precision training instead of the default fp32.
optim="paged_adamw_8bit", # The optimizer to use, here it's a variant of AdamW optimized for 8-bit computing.
logging_dir="./logs", # Directory to store logs.
save_strategy="steps", # Strategy to use for saving a model checkpoint ('steps' means saving at every specified number of steps).
save_steps=25, # Number of steps to save a checkpoint after.
evaluation_strategy="steps", # Strategy to use for evaluation ('steps' means evaluating at every specified number of steps).
eval_steps=50, # Number of training steps to perform evaluation after.
do_eval=True, # Whether to run evaluation on the validation set.
report_to="wandb", # Tool to use for logging and tracking (Weights & Biases in this case).
remove_unused_columns=True, # Whether to remove columns not used by the model when using a dataset.
run_name="run-name", # Name of the experiment run, usually containing the project name and timestamp.
```
The provided parameters, while not comprehensive, cover the most critical ones for fine-tuning. Particularly, `per_device_train_batch_size` and `learning_rate` are the most sensitive and influential during this process.
# Resources
- [Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)](https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms?utm_source=substack&utm_campaign=post_embed&utm_medium=web)
- [Easily Train a Specialized LLM: PEFT, LoRA, QLoRA, LLaMA-Adapter, and More](https://cameronrwolfe.substack.com/p/easily-train-a-specialized-llm-peft#:~:text=LoRA%20leaves%20the%20pretrained%20layers,of%20the%20model%3B%20see%20below.&text=Rank%20decomposition%20matrix.,the%20dimensionality%20of%20the%20input.)
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"description": "\n<div align=\"center\">\n <img width=\"400\" height=\"350\" src=\"/img/cyphertune-logo.webp\">\n</div>\n\n<h5 align=\"center\">\n \u26a0\ufe0f<em>Under Active Development</em> \u26a0\ufe0f\n</h5>\n\n**CypherTune** is an encapsulated Python library designed to fine-tune large language models (LLMs) specifically for text-2-Cypher tasks. [Cypher](https://neo4j.com/developer/cypher/), the graph query language utilized by Neo4j, is renowned for its efficiency in data retrieval from knowledge graphs. Its user-friendly nature, drawing parallels with SQL, is attributed to an intuitive syntax that resonates well with those familiar with traditional programming languages.\n\nThis repository takes inspiration from Neo4j's recent [initiative](https://bratanic-tomaz.medium.com/crowdsourcing-text2cypher-dataset-e65ba51916d4) to create their inaugural open-source text-2-Cypher dataset. Our goal with CypherTune is to simplify the process of fine-tuning LLMs, making it more accessible, especially for individuals who are new to the realm of AI. We aim to lower the barrier of entry for Neo4j users to fine-tune LLMs using the text-2-Cypher dataset once it's released to the public.\n\nContributions and participation in this crowdsourcing effort is welcomed! If you're interested in being a part of this exciting initiative, feel free to join and contribute to Neo4j's [application](https://text2cypher.vercel.app/) \ud83d\ude80\ud83d\ude80.\n\n# Features <img align=\"center\" width=\"30\" height=\"29\" src=\"https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExOTBqaWNrcGxnaTdzMGRzNTN0bGI2d3A4YWkxajhsb2F5MW84Z2dxaCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/26tOZ42Mg6pbTUPHW/giphy.gif\">\n\nCypherTune streamlines the training process by abstracting several libraries from the Hugging Face ecosystem. It currently offers the following features:\n\n- **LLM Fine-Tuning**: Fine-tunes LLMs using HF's `Trainer` for custom text-to-Cypher datasets stored in the [Hub](https://huggingface.co/datasets).\n- **Bits and Bytes**: Loads model with 4-bit quantization.\n- **PEFT**: Uses [LoRA](https://arxiv.org/pdf/2106.09685.pdf) under the hood, a popular and lightweight training technique that significantly reduces the number of trainable parameters. We combine 4-bit quantization lowering the barrier for the amount of memory required during training ([QLoRA](https://arxiv.org/abs/2305.14314)).\n- **Dataset Preprocessing**: Converts dataset into a prompt template for fine-tuning.\n- **Weights & Biases Integration**: Track and log your experiments using wandb.\n\nTODO\n\n> - Table storing hyperparameter configurations for select training job environments (i.e. depending on dataset size, model type/size and amount/type of compute).\n> - Model eval functionality post-training.\n> - `Max length` to be determined after dataset is released.\n> - Make dataset headers dynamic, right now they are static.\n> - Provide inference snippet for testing after training.\n> - Fully Sharded Data Parallel (FSDP): Utilizes efficient training across distributed systems.\n\n# Install <img align=\"center\" width=\"30\" height=\"29\" src=\"https://media.giphy.com/media/sULKEgDMX8LcI/giphy.gif\">\n\n```\npip install cyphertune\n```\n\n# Start Training <img align=\"center\" width=\"30\" height=\"29\" src=\"https://media.giphy.com/media/QLcCBdBemDIqpbK6jA/giphy.gif\">\n\nTo start training, the only requirements are a `project name`, your Hugging Face `model`/`dataset` stubs and the path to your YAML `config_file`. This file includes the essential LoRA and training arguments for fine-tuning. Before beginning the training process, ensure you download the YAML file from this repository using either the curl or wget commands to access its contents.\n\n```bash\ncurl -o config.yml https://raw.githubusercontent.com/InquestGeronimo/cyphertune/main/cyphertune/config.yml\n```\n\n```bash\nwget -O config.yml https://raw.githubusercontent.com/InquestGeronimo/cyphertune/main/cyphertune/config.yml\n```\n\nThe trainer expects to ingest a `train` and `validation` split from your dataset prior to training with a specific format. Here is an [example](https://huggingface.co/datasets/zeroshot/text-2-cypher) of a placeholder dataset, designed to demonstrate the expected format.\n\n```py\nfrom cyphertune import CypherTuner\n\ntuner = CypherTuner(\n project_name=\"cyphertune-training-run1\",\n model_id=\"codellama/CodeLlama-7b-Instruct-hf\",\n dataset_id=\"zeroshot/text-2-cypher\",\n config_file=\"path/to/config.yml\"\n)\n\ntuner.train()\n```\nAfter training completes, the adapter will be saved in your output directory. The pre-trained model will not be saved.\n\n# HyperParameter Knowledge <img align=\"center\" width=\"30\" height=\"29\" src=\"https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExMXV1bWFyMWxkY3JocjE1ZDMxMWZ5OHZtejFkbHpuZXdveTV3Z3BiciZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/bGgsc5mWoryfgKBx1u/giphy.gif\">\n\n\nConfiguring hyperparameters and LoRA settings can be a complex task, especially for those new to AI engineering. Our repository is designed to lessen the dependence on hyperparameters. For instance, once the Text-2-Cypher dataset is fully crowdsourced, we will undertake multiple training sessions. This will help us establish a set of baseline hyperparameters known to yield good results, and we will save them to this repository, thereby streamlining the fine-tuning process for Neo4j users. However, having a thorough understanding of these hyperparameters is still advantageous, particularly if you intend to modify them yourself.\n\nThree key factors affect hyperparameters during training:\n\n1. The type and size of the model.\n2. The type and quantity of hardware.\n3. The size of the dataset.\n\nFor accessing CypherTune's hyperparameters, you can refer to the [config_file](https://github.com/InquestGeronimo/cyphertune/blob/main/cyphertune/config.yml). At the time of writing, the Text-2-Cypher dataset is not publicly available, so the parameters in the file serve as placeholders.\n\nThe first set of parameters pertains to the [LoRA](https://huggingface.co/docs/peft/en/package_reference/lora) settings:\n\n```py\n # LoRA configuration settings\n r=8, # The size of the LoRA's rank. Opting for a higher rank could negate the efficiency benefits of using LoRA. The higher the rank the largar the checkpoint file is.\n lora_alpha=16, # This is the scaling factor for LoRA. It controls the magnitude of the adjustments made by LoRA.\n target_modules=[ # Specifies the parts of the model where LoRA is applied. These can be components of the transformer architecture.\n \"q_proj\", \n \"k_proj\",\n \"v_proj\",\n \"o_proj\",\n \"gate_proj\",\n \"up_proj\", \n \"down_proj\",\n \"lm_head\",\n ],\n bias=\"none\", # Indicates that biases are not adapted as part of the LoRA process.\n lora_dropout=0.05, # The dropout rate for LoRA layers. It's a regularization technique to prevent overfitting.\n task_type=\"CAUSAL_LM\" # Specifies the type of task. Here, it indicates the model is for causal language modeling.\n```\n\nFor further details, refer to the PEFT [documentation](https://huggingface.co/docs/peft/en/package_reference/lora) or read the blogs found at the end of this README.\n\nThe following set pertains specifically to the training arguments:\n\n```py\n # Trainer configuration settings\n output_dir=\"./output\", # Directory where the training outputs and model checkpoints will be written.\n warmup_steps=1, # Number of steps to perform learning rate warmup.\n per_device_train_batch_size=32, # Batch size per device during training.\n gradient_accumulation_steps=1, # Number of updates steps to accumulate before performing a backward/update pass.\n gradient_checkpointing=True, # Enables gradient checkpointing to save memory at the expense of slower backward pass.\n max_steps=1000, # Total number of training steps to perform.\n learning_rate=1e-4, # Initial learning rate for the optimizer.\n bf16=True, # Use bfloat16 mixed precision training instead of the default fp32.\n optim=\"paged_adamw_8bit\", # The optimizer to use, here it's a variant of AdamW optimized for 8-bit computing.\n logging_dir=\"./logs\", # Directory to store logs.\n save_strategy=\"steps\", # Strategy to use for saving a model checkpoint ('steps' means saving at every specified number of steps).\n save_steps=25, # Number of steps to save a checkpoint after.\n evaluation_strategy=\"steps\", # Strategy to use for evaluation ('steps' means evaluating at every specified number of steps).\n eval_steps=50, # Number of training steps to perform evaluation after.\n do_eval=True, # Whether to run evaluation on the validation set.\n report_to=\"wandb\", # Tool to use for logging and tracking (Weights & Biases in this case).\n remove_unused_columns=True, # Whether to remove columns not used by the model when using a dataset.\n run_name=\"run-name\", # Name of the experiment run, usually containing the project name and timestamp.\n```\nThe provided parameters, while not comprehensive, cover the most critical ones for fine-tuning. Particularly, `per_device_train_batch_size` and `learning_rate` are the most sensitive and influential during this process.\n\n# Resources\n\n- [Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)](https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms?utm_source=substack&utm_campaign=post_embed&utm_medium=web)\n- [Easily Train a Specialized LLM: PEFT, LoRA, QLoRA, LLaMA-Adapter, and More](https://cameronrwolfe.substack.com/p/easily-train-a-specialized-llm-peft#:~:text=LoRA%20leaves%20the%20pretrained%20layers,of%20the%20model%3B%20see%20below.&text=Rank%20decomposition%20matrix.,the%20dimensionality%20of%20the%20input.)\n\n",
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