LMFlow - Fast and Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Some recommends LMFlow, a fast and extensible toolkit for finetuning and inference of large foundation models. It just takes 5 hours on a 3090 GPU for fine-tuning llama-7B.

LMFlow is a powerful toolkit designed to streamline the process of finetuning and performing inference with large foundation models. It provides efficient and scalable solutions for handling large-scale language models. With LMFlow, you can easily experiment with different data sets, training parameters, and architectures to achieve optimal performance.

For more information, you can visit the official LMFlow GitHub repository.

Alpaca LoRA - Efficient Training of Language Models

To train large language models effectively, you can utilize Alpaca LoRA. It is a framework that enables efficient and cost-effective training of language models on GPUs.

With Alpaca LoRA, you can leverage the power of multiple GPUs to accelerate the training process. It optimizes GPU memory consumption and reduces the training time, allowing you to train models like LLaMA 7B in a cost-efficient manner.

For more information and detailed usage instructions, you can visit the Alpaca LoRA GitHub repository.

Tags: LORA, LMFlow, fine-tuning, foundation models, Alpaca LoRA, LLaMA, GitHub repository

Biased or Censored Completions - Early ChatGPT vs Current Behavior

I've been exploring various AI models recently, especially with the anticipation of building a new PC. While waiting, I've compiled a list of models I plan to download and try:

WizardLM
Vicuna
WizardVicuna
Manticore
Falcon
Samantha
Pygmalion
GPT4-x-Alpaca

However, given the large file sizes, I need to be selective about the models I download, as LLama 65b is already consuming … click here to read

Unlocking GPU Inferencing Power with GGUF, GPTQ/AWQ, and EXL2

If you are into the fascinating world of GPU inference and exploring the capabilities of different models, you might have encountered the tweet by turboderp_ showcasing some 3090 inference on EXL2. The discussion that followed revealed intriguing insights into GGUF, GPTQ/AWQ, and the efficient GPU inferencing powerhouse - EXL2.

GGUF, described as the container of LLMs (Large Language Models), resembles the .AVI or .MKV of the inference world. Inside this container, it supports various quants, including traditional ones (4_0, 4_1, 6_0, … click here to read

Building Language Models for Low-Resource Languages

As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this paper, the researchers introduce the Sabiá: Portuguese Large Language Models and demonstrate that monolingual pretraining on the target language significantly improves models already extensively trained on diverse corpora. Few-shot evaluations … click here to read

Bringing Accelerated LLM to Consumer Hardware

MLC AI, a startup that specializes in creating advanced language models, has announced its latest breakthrough: a way to bring accelerated Language Model (LLM) training to consumer hardware. This development will enable more accessible and affordable training of advanced LLMs for companies and organizations, paving the way for faster and more efficient natural language processing.

The MLC team has achieved this by optimizing its training process for consumer-grade hardware, which typically lacks the computational power of high-end data center infrastructure. This optimization … click here to read

Model Benchmarking: Unveiling Insights into Language Models

Recently, the language model community has been buzzing with discussions about the performance of various models. A particular model that caught our attention is Beyonder , which, in casual testing, seems to be one of the rare non-broken Mixture of Experts (MoEs). It incorporates openchat-3.5 , a model previously benchmarked by the community.

But what's the best inference engine? This question often arises, and it's crucial to consider the source code … click here to read

Automated Reasoning with Language Models

Automated reasoning with language models is a fascinating field that can test reasoning skills. Recently, a model named Supercot showed accidental proficiency in prose/story creation. However, it's essential to use original riddles or modify existing ones to ensure that the models are reasoning and not merely spewing out existing knowledge on the web.

Several models have been tested in a series of reasoning tasks, and Vicuna-1.1-Free-V4.3-13B-ggml-q5_1 has been tested among others. It performed well, except for two coding points. Koala performed slightly better … click here to read

Re-Pre-Training Language Models for Low-Resource Languages

Language models are initially pre-trained on a huge corpus of mostly-unfiltered text in the target languages, then they are made into ChatLLMs by fine-tuning on a prompt dataset. The pre-training is the most expensive part by far, and if existing LLMs can't do basic sentences in your language, then one needs to start from that point by finding/scraping/making a huge dataset. One can exhaustively go through every available LLM and check its language abilities before investing in re-pre-training. There are surprisingly many of them … click here to read

Stack Llama and Vicuna-13B Comparison

Stack Llama, available on the TRL Library, is a RLHF model that works well with logical tasks, similar to the performance of normal Vicuna-13B 1.1 in initial testing. However, it requires about 25.2GB of dedicated GPU VRAM and takes approximately 12 seconds to load.

The Stack Llama model was trained using the StableLM training method, which aims to improve the stability of the model's training and make it more robust to the effects of noisy data. The model was also trained on a … click here to read

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