If you're using large language models (LLMs) and care about speed and hardware limitations, GGUF and K-quants are about to be your new best friends. Here's what you need to know:
- What is it? A powerful quantization format replacing GGML. Faster inference on CPUs, seamless GPU acceleration, and better future-proofing for LLM development.
- Why it matters: GGUF puts all metadata in one place (no extra files needed) and paves the way for new features without breaking existing models. Think streamlined LLM usage and long-term compatibility.
- Key Feature: CPU-based inference with optional GPU acceleration
- GGML is a C++ Tensor library designed for machine learning, facilitating the running of LLMs either on a CPU alone or in tandem with a GPU.
- GGUF (new)
- GGML (old)
- Llama.cpp has dropped support for the GGML format and now only supports GGUF
- GGUF (GPT-Generated Unified Format), introduced as a successor to GGML (GPT-Generated Model Language), was released on the 21st of August, 2023
K-Quants: Smart Weight Compression
- What is it? A technique for making models smaller without sacrificing much performance. Weights are divided into blocks, with the most important ones getting higher precision storage.
- Why it matters: Faster inference, less memory needed. Look for models named like "q3_K_S" to identify K-quant models.
- Key Feature: Fine-grained control over how model weights are stored for optimal efficiency.
GGUF vs. GPTQ: Not the Same Thing!
- GGUF/GGML and GPTQ are both quantization methods, but they're built differently.
- GPTQ focuses on compressing existing models by reducing the number of bits per weight.
- GGUF/GGML offer more flexibility in how models are built and how they use both CPUs and GPUs.
The bitsandbytes library quantizes on the fly (to 8-bit or 4-bit) which is also knows as dynamic quantization
AWQ: Activation-aware Weight Quantization - which is a quantization method similar to GPTQ. It protects salient weights by observing activations rather than the weights themselves. AWQ achieves excellent quantization performance, especially for instruction-tuned LMs and multi-modal LMs. There are several differences between AWQ and GPTQ as methods but the most important one is that AWQ assumes that not all weights are equally important for an LLM’s performance. For AWQ, best to use the vLLM package
- GGUF/GGML: These are closely related. GGUF is the new format replacing GGML but built on the same principles. They are methods for quantizing and running LLMs efficiently.
- K-Quants: This is a valid quantization strategy that prioritizes certain weights for higher precision, leading to smaller models with minimal accuracy loss.
- GPTQ: Absolutely, this is a well-known post-training quantization method.
- AWQ: Activation-aware Weight Quantization: Accurate! It smartly adapts quantization based on activation patterns for better performance.
- QAT (Quantization-Aware Training): A significant technique where quantization is simulated during training, making the model more resilient to quantization effects during actual deployment.
- PTQ (Post-Training Quantization): Crucial method where a pre-trained model is quantized. Often easier to implement than QAT, but accuracy loss can be more pronounced.
GGUF and K-quants are making LLMs more accessible. Expect:
- Faster LLMs, even on consumer hardware
- New, innovative models that wouldn't fit on your device before
- More flexibility for fine-tuning and tweaking existing models
Excited about the possibilities? Questions about how these apply to your work? Share your thoughts below! ??
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