exxa 0.2.5

Exa - Pytorch

Exa

Boost your GPU's LLM performance by 300% on everyday GPU hardware, as validated by renowned developers, in just 5 minutes of setup and with no additional hardware costs.

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Principles

• Radical Simplicity (Utilizing super-powerful LLMs with as minimal lines of code as possible)
• Ultra-Optimizated Peformance (High Performance code that extract all the power from these LLMs)
• Fludity & Shapelessness (Plug in and play and re-architecture as you please)

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🤝 Schedule a 1-on-1 Session

Book a 1-on-1 Session with Kye, the Creator, to discuss any issues, provide feedback, or explore how we can improve Exa for you.

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📦 Installation 📦

You can install the package using pip

pip install exxa

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Usage

Inference

Generate text using pretrained models with optional quantization with minimal configuration and straightforward usage.

• Load specified pre-trained models with device flexibility (CPU/CUDA).
• Set a default maximum length for the generated sequences.
• Choose to quantize model weights for faster inference.
• Use a custom configuration for quantization as needed.
• Generate text through either a direct call or the run method.
• Simple usage for quick text generation based on provided prompts.

from exa import Inference

model = Inference(
    model_id="georgesung/llama2_7b_chat_uncensored",
    quantized=True
)

model.run("What is your name")

GPTQ Inference

Efficiently generate text using quantized GPT-like models built for HuggingFace's pre-trained models with optional quantization and only a few lines of code for instantiation and generation.

• Load specified pre-trained models with an option for quantization.
• Define custom bit depth for the quantization (default is 4 bits).
• Fine-tune quantization parameters using specific datasets.
• Set maximum length for generated sequences to maintain consistency.
• Tokenize prompts and generate text based on them seamlessly.

# !pip install exxa
from exa import GPTQInference

model_id = "gpt2-medium"
inference = GPTQInference(
    model_id, 
    quantization_config_bits=2, 
    max_length=400, 
    quantization_config_dataset='c4'
)
output_text = inference.run("The future of AI is")
print(output_text)

Quantize

Achieve smaller model sizes and faster inference by utilizing a unified interface tailored to HuggingFace's framework and only a simple class instantiation with multiple parameters is needed.

• Efficiently quantize HuggingFace's pretrained models with specified bits (default is 4 bits).
• Set custom thresholds for quantization for precision management.
• Ability to skip specific modules during quantization for sensitive model parts.
• Offload parts of the model to CPU in FP32 format for GPU memory management.
• Specify if model weights are already in FP16 format.
• Choose from multiple quantization types like "fp4", "int8", and more.
• Option to enable double quantization for more compression.
• Verbose logging for a detailed understanding of the quantization process.
• Seamlessly push to and load models from the HuggingFace model hub.
• In-built logger initialization tailored for quantization logs.
• Log metadata for state and settings introspection.

from exa import Quantize

#usage
quantize = Quantize(
     model_id="bigscience/bloom-1b7",
     bits=8,
     enable_fp32_cpu_offload=True,
)

quantize.load_model()
quantize.push_to_hub("my model")
quantize.load_from_hub('my model')

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🎉 Features 🎉

• World-Class Quantization: Get the most out of your models with top-tier performance and preserved accuracy! 🏋️‍♂️

• Automated PEFT: Simplify your workflow! Let our toolkit handle the optimizations. 🛠️

• LoRA Configuration: Dive into the potential of flexible LoRA configurations, a game-changer for performance! 🌌

• Seamless Integration: Designed to work seamlessly with popular models like LLAMA, Falcon, and more! 🤖

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💌 Feedback & Contributions 💌

We're excited about the journey ahead and would love to have you with us! For feedback, suggestions, or contributions, feel free to open an issue or a pull request. Let's shape the future of fine-tuning together! 🌱

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Benchmarks

The following is what we benchmark for according to the 🤗 LLM-Perf Leaderboard 🏋️ benchmarks

• https://huggingface.co/spaces/optimum/llm-perf-leaderboard
• https://github.com/huggingface/optimum-benchmark

Metrics

• Backend 🏭
• Dtype 📥
• Optimizations 🛠️
• Quantization 🗜️
• Class 🏋️
• Type 🤗
• Memory (MB) ⬇️
• Throughput (tokens/s) ⬆️
• Energy (tokens/kWh) ⬇️
• Best Score (%) ⬆️
• Best Scored LLM 🏆

License

MIT

Todo

• Setup utils logger classes for metric logging with useful metadata such as token inference per second, latency, memory consumption
• Add cuda c++ extensions for radically optimized classes for high performance quantization + inference on the edge