1 unstable release
0.1.0 | Sep 8, 2024 |
---|
#444 in Machine learning
22 downloads per month
63KB
830 lines
NeuralAssimilator
NeuralAssimilator is a Rust crate for fine-tuning Language Learning Models (LLMs) from unstructured text.
Features
- Generate prompts based on specified use cases
- Create instruction-response pairs for fine-tuning
- Output results in JSONL format
- Perform training on your LLM provider with the generated dataset
Installation
Add this to your Cargo.toml
:
[dependencies]
neuralassimilator = "0.1.0"
Usage
Command-line Interface
NeuralAssimilator can be used via its command-line interface:
neuralassimilator --input ./input_folder --output ./output_folder --chunk-size 10000 --model gpt-4o-mini-2024-07-18 --use-case "Creative writing"
Arguments
--input
or-i
: Input directory path (default: "./input")--output
or-o
: Output file or directory path (optional)--chunk-size
: Size of text chunks to process (default: 10000)--model
: LLM model to use (default: "gpt-4o-mini-2024-07-18")--use-case
: Specific use case for prompt generation (default: "Creative writing")
How it Works
- Input Processing: The crate reads input files from the specified directory and chunks them into manageable sizes.
- Prompt Tuning: Based on the given use case, it generates appropriate prompts for the LLM.
- Instruction Generation: For each chunk-prompt pair, it generates instruction-response pairs using the specified LLM.
- Output: The resulting pairs are written to a JSONL file in the specified output location.
- Fine-tuning: The generated dataset can then be used to fine-tune the LLM.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License.
Dependencies
~15–28MB
~418K SLoC