The Kaitchup – AI on a Budget

The Kaitchup – AI on a Budget focuses on strategies for fine-tuning, running, and serving large language models (LLMs) on consumer-grade hardware. It covers advancements in model compression, efficiency techniques, custom model creation, and benchmarks alongside tutorials for practical application.

Fine-tuning Techniques Model Compression and Quantization Hardware Optimization Model Performance and Benchmarks Custom Language Model Creation Efficiency in Large Language Models

The hottest Substack posts of The Kaitchup – AI on a Budget

And their main takeaways

The Weekly Kaitchup #65

59 implied HN points • 01 Nov 24
🕹 Technology AI Models Machine Learning Natural Language Text-to-Speech Data science
  1. SmolLM2 offers alternatives to popular models like Qwen2.5 and Llama 3.2, showing good performance with various versions available.
  2. The Layer Skip method improves the speed and efficiency of Llama models by processing some layers selectively, making them faster without losing accuracy.
  3. MaskGCT is a new text-to-speech model that generates high-quality speech without needing text alignment, providing better results across different benchmarks.

The Impact of the Calibration Dataset for AutoRound and AWQ Quantization

39 implied HN points • 31 Oct 24
🕹 Technology AI Data science Machine Learning Quantization Model optimization
  1. Quantization helps reduce the size of large language models, making them easier to run, especially on consumer GPUs. For instance, using 4-bit quantization can shrink a model's size by about a third.
  2. Calibration datasets are crucial for improving the accuracy of quantization methods like AWQ and AutoRound. The choice of the dataset impacts how well the quantization performs.
  3. Most quantization tools use a default English-language dataset, but results can vary with different languages and datasets. Testing various options can lead to better outcomes.

bitnet.cpp: Efficient Inference with 1-Bit LLMs on your CPU

179 implied HN points • 28 Oct 24
🕹 Technology Artificial Intelligence Software Development Machine Learning Open Source Data science
  1. BitNet is a new type of AI model that uses very little memory by representing each parameter with just three values. This means it uses only 1.58 bits instead of the usual 16 bits.
  2. Despite using lower precision, these '1-bit LLMs' still work well and can compete with more traditional models, which is pretty impressive.
  3. The software called 'bitnet.cpp' allows users to run these AI models on normal computers easily, making advanced AI technology more accessible to everyone.

Generate Videos on Your Computer with Pyramid Flow

99 implied HN points • 24 Oct 24
🕹 Technology AI Video Software Hardware Generative
  1. Pyramid Flow is a new model that lets you generate videos quickly on your computer. It supports 768p resolution and works at 24 frames per second.
  2. You can create videos using either text prompts or a mix of text and image prompts, making it flexible for different projects.
  3. A consumer GPU, like the RTX 3090, is good enough for making these videos, and there's a notebook available with all the steps to help you get started.

Fixing Faulty Gradient Accumulation: Understanding the Issue and Its Resolution

159 implied HN points • 21 Oct 24
🕹 Technology AI Machine Learning Data science Model Training Computing
  1. Gradient accumulation helps train large models on limited GPU memory. It simulates larger batch sizes by summing gradients from several smaller batches before updating model weights.
  2. There has been a problem with how gradients were summed during gradient accumulation, leading to worse model performance. This was due to incorrect normalization in the calculation of loss, especially when varying sequence lengths were involved.
  3. Hugging Face and Unsloth AI have fixed the gradient accumulation issue. With this fix, training results are more consistent and effective, which might improve the performance of future models built using this technique.
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The Weekly Kaitchup #64

59 implied HN points • 25 Oct 24
🕹 Technology AI Machine Learning Software Data science Cloud Computing
  1. Qwen2.5 models have been improved and now come in a 4-bit version, making them efficient for different hardware. They perform better than previous models on many tasks.
  2. Google's SynthID tool can add invisible watermarks to AI-generated text, helping to identify it without changing the text's quality. This could become a standard practice to distinguish AI text from human writing.
  3. Cohere has launched Aya Expanse, new multilingual models that outperform many existing models. They took two years to develop, involving thousands of researchers, enhancing language support and performance.

Train and Serve an AI Chatbot Based on Llama 3.2

179 implied HN points • 17 Oct 24
🕹 Technology AI Chatbots Machine Learning Data science Software Development
  1. You can create a custom AI chatbot easily and cheaply now. New methods make it possible to train smaller models like Llama 3.2 without spending much money.
  2. Fine-tuning a chatbot requires careful preparation of the dataset. It's important to learn how to format your questions and answers correctly.
  3. Avoiding common mistakes during training is crucial. Understanding these pitfalls will help ensure your chatbot works well after it's trained.

Fast Speculative Decoding with Llama 3.2 and vLLM

219 implied HN points • 14 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Computing Software Development Data processing
  1. Speculative decoding is a method that speeds up language model processes by using a smaller model for suggestions and a larger model for validation.
  2. This approach can save time if the smaller model provides mostly correct suggestions, but it may slow down if corrections are needed often.
  3. The new Llama 3.2 models may work well as draft models to enhance the performance of the larger Llama 3.1 models in this decoding process.

The Weekly Kaitchup #63

119 implied HN points • 18 Oct 24
🕹 Technology AI Machine Learning Deep Learning Software Development Robotics
  1. There's a new fix for gradient accumulation in training language models. This issue had been causing problems in how models were trained, but it's now addressed by Unsloth and Hugging Face.
  2. Several new language models have been released recently, including Llama 3.1 Nemotron 70B and Zamba2 7B. These models are showing different levels of performance across various benchmarks.
  3. Consumer GPUs are being tracked for price drops, making them a more affordable option for fine-tuning models. This week highlights several models for those interested in AI training.

Fine-tuning LLMs with 32-bit, 8-bit, and Paged AdamW Optimizers

259 implied HN points • 07 Oct 24
🕹 Technology AI Machine Learning Optimization Data processing Programming
  1. Using 8-bit and paged AdamW optimizers can save a lot of memory when training large models. This means you can run more complex models on cheaper, lower-memory GPUs.
  2. The 8-bit optimizer is almost as effective as the 32-bit version, showing similar results in training. You can get great performance with less memory required.
  3. Paged optimizers help manage memory efficiently by moving data only when needed. This way, you can keep training even if you don't have enough GPU memory for everything.

The Weekly Kaitchup #62

159 implied HN points • 11 Oct 24
🕹 Technology AI Machine Learning Hardware Software Data science
  1. Avoid using small batch sizes with gradient accumulation. It often leads to less accurate results compared to using larger batch sizes.
  2. Creating better document embeddings is important for retrieving information effectively. Including neighboring documents in embeddings can really help improve the accuracy of results.
  3. Aria is a new model that processes multiple types of inputs. It's designed to be efficient but note that it has a higher number of parameters, which means it might take up more memory.

Generate Synthetic Data from Personas to Train AI Chatbots

139 implied HN points • 10 Oct 24
🕹 Technology AI Chatbots Data science Machine Learning
  1. Creating a good training dataset is key to making AI chatbots work well. Without quality data, the chatbot might struggle to perform its tasks effectively.
  2. Generating your own dataset using large language models can save time instead of collecting data from many different sources. This way, the data is tailored to what your chatbot really needs.
  3. Using personas can help you create specific question-and-answer pairs for the chatbot. It makes the training process more focused and relevant to various topics.

The Weekly Kaitchup #61

139 implied HN points • 04 Oct 24
🕹 Technology AI Models Machine Learning Computational efficiency Software Development Tech industry
  1. NVIDIA's new NVLM-D-72B model is a large language model that works well with both text and images. It has special features that make it good at understanding and processing high-quality visuals.
  2. OpenAI's new Whisper Large V3 Turbo model is significantly faster than its previous versions. While it has fewer parameters, it maintains good accuracy for most languages.
  3. Liquid AI introduced new models called Liquid Foundation Models, which are very efficient and can handle complex tasks. They use a unique setup to save memory and improve performance.

The Unreasonable Impact of Gradient Checkpointing for Fine-tuning LLMs

79 implied HN points • 03 Oct 24
🕹 Technology AI Machine Learning Data science Programming Computing
  1. Gradient checkpointing helps to reduce memory usage during fine-tuning of large language models by up to 70%. This is really important because managing large amounts of memory can be tough with big models.
  2. Activations, which are crucial for training models, can take up over 90% of the memory needed. Keeping track of these is essential for successfully updating the model's weights.
  3. Even though gradient checkpointing helps save memory, it might slow down training a bit since some activations need to be recalculated. It's a trade-off to consider when choosing methods for model training.

Phi-2: A Small Model Easy to Fine-tune on Your GPU

299 implied HN points • 01 Jan 24
  1. Phi-2 is a small language model created by Microsoft, with improved performance over Phi-1.5 by doubling parameters to 2.7 billion and extending training data.
  2. Phi-2 is a pre-trained model and a good student of GPT-4, utilizing synthetic and curated web data for training, with evaluations showing improved behavior in toxicity and bias.
  3. Fine-tuning Phi-2 is made easier and affordable on consumer hardware, requiring at least 5.4 GB GPU VRAM for loading, with options for quantization and inference optimizations.

Maixtchup: Make Your Own Mixture of Experts with Mergekit

219 implied HN points • 18 Jan 24
  1. MoE models are gaining interest for their simplicity and flexibility in creating custom mixtures of experts.
  2. Many custom MoE models are created by combining already fine-tuned LLMs using mergekit.
  3. Readers can learn how to create their own mixture of experts using a specific process outlined in the article.

Mixtral-8x7B: Understanding and Running the Sparse Mixture of Experts by Mistral AI

219 implied HN points • 12 Dec 23
  1. Mixtral-8x7B is a sparse mixture of 8 expert models that can efficiently run on consumer hardware and outperform other models of similar size.
  2. A sparse mixture of experts architecture improves efficiency by activating only a subset of experts, reducing computational and parameter costs, and encouraging generalization for diverse inputs.
  3. To run or fine-tune Mixtral on consumer hardware, quantization can reduce the memory footprint, but you will need at least two GPUs for smooth operation.

The Weekly Kaitchup #24

139 implied HN points • 19 Jan 24
  1. DeepSeekMoE introduces segmented and shared experts to enhance performance and efficiency in MoE models.
  2. KTO outperforms DPO and IPO alignment techniques, with careful selection of the Beta hyperparameter being crucial for optimal results.
  3. Research suggests that easy training data may be effective in training LLMs to solve hard problems, reducing the need for expensive hard training data.

TinyLlama: Pre-training a Small Llama 2 from Scratch

119 implied HN points • 07 Feb 24
  1. TinyLlama project pre-trained a 1.1B parameter Llama 2 model on 3 trillion tokens with well-documented process details.
  2. TinyLlama is faster and has lower memory usage compared to Llama 2 7B but underperforms in downstream tasks.
  3. Despite being smaller, TinyLlama project required significant resources for pre-training, highlighting the cost of training large language models.

From 16-bit to 2-bit: Finding the Best Trade-off Between Memory-Efficiency and Accuracy

119 implied HN points • 01 Feb 24
  1. Quantization can reduce the size of large language models for consumer hardware, but it sacrifices accuracy.
  2. It's easier to quantize larger language models with minimal accuracy loss compared to smaller language models.
  3. Experimenting with various quantization levels can help find the optimal balance between memory efficiency and accuracy.

unsloth: Faster and Memory-Efficient QLoRA Fine-tuning

139 implied HN points • 28 Dec 23
  1. With methods like QLoRA, fine-tuning large language models is faster and memory-efficient.
  2. Unsloth optimizations make fine-tuning LLMs up to 5x faster and reduce memory consumption by 60%.
  3. Intelligent weight upcasting, Pytorch's scaled dot product attention, and better use of bfloat16 are key optimizations behind Unsloth.

Optimum-Benchmark: How Fast and Memory-Efficient Is Your LLM?

119 implied HN points • 16 Jan 24
  1. Optimize cost by understanding hardware requirements for large language models (LLMs)
  2. Utilize optimum-benchmark framework for assessing LLMs efficiency with memory usage, latency, and throughput metrics
  3. Benchmark different quantization methods like GPTQ, BNB's NF4, AWQ, and compare their performance for decision-making

The Weekly Kaitchup #22

119 implied HN points • 05 Jan 24
  1. SPIN introduces a method for improving LLMs without additional data
  2. Tricksy optimizes CPU-GPU data transfer by exploiting sparsity in large language models
  3. Microsoft achieves efficient text embeddings through quick training with synthetic data

LQ-LoRA: Jointly Fine-tune and Quantize Large Language Models

139 implied HN points • 30 Nov 23
  1. LQ-LoRA decomposes pre-trained LLM into quantized parameters and a LoRA adapter.
  2. QA-LoRA is an alternative to QLoRA for fine-tuning, but its official implementation lacked support for recent LLMs.
  3. There is a need for another alternative method apart from QLoRA and QA-LoRA.

Combine Multiple LoRA Adapters for Llama 2

139 implied HN points • 27 Nov 23
  1. Fine-tuning a pre-trained language model with LoRA adapters is cost-effective.
  2. Adapters can be fine-tuned for different tasks and combined to create a multi-task adapter.
  3. When combining adapters, consider using different prompt formats for optimal performance.

The Weekly Kaitchup #20

119 implied HN points • 22 Dec 23
  1. Gemini Pro outperformed GPT-3.5 in a new evaluation by NeuLab
  2. Different results were obtained in evaluations of models based on prompts and hyperparameters
  3. Apple's research explores efficient inference with flash memory for LLMs

Use FlashAttention-2 for Faster Fine-tuning and Inference

139 implied HN points • 16 Nov 23
  1. FlashAttention is a method to speed up attention computation in Transformers.
  2. FlashAttention-2 is a newer version with additional optimizations.
  3. Using FlashAttention-2 can significantly accelerate fine-tuning for large language models.

The Weekly Kaitchup #27

79 implied HN points • 09 Feb 24
  1. ALiBi method removes the need for position embedding in Transformer models.
  2. FLAN-T5 is not necessarily better than larger models like Llama 2 70B for summarization tasks.
  3. Apple recommends using generic training corpora, importance sampling, and asymmetric models for cheap inference from limited domain data.

Llama 2 MT: Turn Llama 2 into a Translation System with QLoRA

139 implied HN points • 02 Nov 23
  1. Llama 2 can be fine-tuned into a translation system for various languages.
  2. Fine-tuning Llama 2 for translation tasks requires careful preprocessing of training data.
  3. QLoRA technique enables fine-tuning Llama 2 with LoRA adapters on consumer hardware for accurate machine translation.

GPTQ or bitsandbytes: Which Quantization Method to Use for LLMs — Examples with Llama 2

179 implied HN points • 22 Aug 23
  1. Quantization is an effective compression technique for reducing the memory size of large language models by lowering the precision of weights.
  2. GPTQ and bitsandbytes are two popular quantization methods for LLMs, each with its own advantages and disadvantages.
  3. The choice between GPTQ and bitsandbytes depends on specific use cases, with differences in memory usage, inference speed, and performance.

Falcon 180B: Can It Run on Your Computer?

102 HN points • 11 Sep 23
  1. Falcon 180B can run on your computer if you have enough CPU RAM.
  2. Falcon 180B has huge memory requirements, but they can be reduced using techniques like device mapping and quantization.
  3. Quantization reduces Falcon 180B's memory usage, making it more feasible to run on affordable hardware.

The Weekly Kaitchup #26

39 implied HN points • 02 Feb 24
  1. TRL's IPO performance improved after fixing a bug
  2. Activation Beacon method extends LLMs context efficiently
  3. AI2 released new open-source LLMs with comprehensive resources

The Weekly Kaitchup #25

19 implied HN points • 26 Jan 24
  1. NVIDIA released a new powerful RTX 4070 Ti SUPER GPU with 16 GB memory
  2. MEDUSA introduces a method to speed up LLM inference with multiple concurrent heads
  3. AirLLM enables running large LLMs on low-memory hardware through layered inference