quickllm 0.1.1

A library for quick fine-tuning and interaction with popular language models

QuickLLM: Fast and Easy Fine-tuning for Popular Language Models

QuickLLM is a Python library that simplifies the process of fine-tuning and interacting with popular language models. With QuickLLM, you can quickly adapt pre-trained models to your specific tasks and chat with them effortlessly.

Features

• Fine-tune popular language models on custom datasets
• Optimize models for specific tasks like chat, code generation, or domain-specific conversations
• Visualize model parameters and training progress
• Interactive chat interface with fine-tuned models
• Support for a wide range of popular language models

Installation

You can install QuickLLM using pip:

pip install quickllm

Quick Start

Here's a simple example to get you started with QuickLLM:

from quickllm import QuickLLM

# Initialize QuickLLM
llm = QuickLLM(model_name="gpt2", input_file="path/to/your/data.csv", output_dir="path/to/output")

# Fine-tune the model
llm.finetune(objective="chat", epochs=3, learning_rate=2e-5)

# Chat with the fine-tuned model
response = llm.chat("Hello, how are you?")
print(response)

# Visualize the model
llm.visualize()

# Start an interactive chat session
from quickllm.chat import start_chat_interface
start_chat_interface(llm.finetuned_model)

Supported Models

QuickLLM supports a wide range of popular language models. Here's a list of currently available models:

1. GPT Family:

   • gpt2
   • gpt2-medium
   • gpt2-large
   • gpt2-xl

2. LLaMA Family:

   • llama
   • llama2
   • llama2-7b
   • llama2-13b
   • llama2-70b

3. BERT Family:

   • bert-base-uncased
   • bert-large-uncased
   • roberta-base
   • roberta-large

4. T5 Family:

   • t5-small
   • t5-base
   • t5-large

5. BART Family:

   • facebook/bart-base
   • facebook/bart-large

6. GPT-Neo Family:

   • EleutherAI/gpt-neo-125M
   • EleutherAI/gpt-neo-1.3B
   • EleutherAI/gpt-neo-2.7B

7. GPT-J Family:

   • EleutherAI/gpt-j-6B

8. OPT Family:

   • facebook/opt-125m
   • facebook/opt-350m
   • facebook/opt-1.3b

9. BLOOM Family:

   • bigscience/bloom-560m
   • bigscience/bloom-1b1
   • bigscience/bloom-1b7

10. Other Models:

    • microsoft/DialoGPT-medium
    • facebook/blenderbot-400M-distill

You can use any of these models by specifying the model name when initializing QuickLLM. More comming soon

Fine-tuning Objectives

QuickLLM supports different fine-tuning objectives to optimize the model for specific tasks:

• chat: General conversational fine-tuning
• code: Optimize for code generation tasks
• specific_chat: Fine-tune for domain-specific conversations based on your input data

Visualization

QuickLLM provides built-in visualization tools to help you understand your fine-tuned model:

• Model architecture visualization
• Parameter size distribution
• Training loss curves (if available)

Contributing

We welcome contributions to QuickLLM! Please feel free to submit issues, fork the repository and send pull requests!

License

QuickLLM is released under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions, feel free to reach out to me at [e-mail] or open an issue on our GitHub repository.

Happy fine-tuning!