LLaVA: Large Language and Vision Assistant

The paper presents the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, the authors introduce LLaVA, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.

LLaVA demonstrates impressive multimodel chat abilities and yields an 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. The authors make GPT-4 generated visual instruction tuning data, their model and code base publicly available.

One interesting comment suggests the possibility of hooking LLaVA up to a webcam to provide additional context on who the chatbot is talking to and how the responses are being received. Another comment questions the meaningful differences between LLaVA and MiniGPT-4, and cites Bing's analysis of the two models. A third comment expresses excitement about the potential of LLaVA.

The paper represents an important step in the exploration of instruction tuning in the multimodal field, and the results demonstrate the promise of LLaVA for general-purpose visual and language understanding. Entities mentioned in the comments include MiniGPT-4, Bing, Vicuna, and CLIP ViT-L/14.

Links:

LLaVA model and code base

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

Developing a Comprehensive Home Assistant Pipeline

When it comes to smart home assistant development, various pipelines can be utilized to enhance user experience. One such framework consists of a series of steps: Wake Word Detection (WWD) -> Voice Activity Detection (VAD) -> Automatic Speech Recognition (ASR) -> Intent Classification -> Event Handler -> Text-to-Speech (TTS). For more details, you can refer to the open-source project rhasspy .

Generally, a distilbert-based intent classification neural network can handle most home assistant tasks. However, for certain … click here to read

Transforming LLMs with Externalized World Knowledge

The concept of externalizing world knowledge to make language models more efficient has been gaining traction in the field of AI. Current LLMs are equipped with enormous amounts of data, but not all of it is useful or relevant. Therefore, it is important to offload the "facts" and allow LLMs to focus on language and reasoning skills. One potential solution is to use a vector database to store world knowledge.

However, some have questioned the feasibility of this approach, as it may … click here to read

Meta's Fairseq: A Giant Leap in Multilingual Model Speech Recognition

AI and language models have witnessed substantial growth in their capabilities, particularly in the realm of speech recognition. Spearheading this development is Facebook's AI team with their Multilingual Model Speech Recognition (MMS) , housed under the Fairseq framework.

Fairseq, as described on its GitHub repository , is a general-purpose sequence-to-sequence library. It offers full support for developing and training custom models, not just for speech recognition, … 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

Exploration of Language Learning Models (LLMs)

For advanced Language Learning Models, consider Flan-UL2 . This model requires significant VRAM but provides excellent results with <2s inference speed. It's great for zero-shot tasks and prevents hallucinations.

Proper formatting and instruction tuning are key to maximizing your model's performance. You may find useful information on system, user, and special character formatting for messages on promptingguide.ai . Tools like Langchain or Transformer Agents can help abstract this process.

Be … click here to read

Extending Context Size in Language Models

Language models have revolutionized the way we interact with artificial intelligence systems. However, one of the challenges faced is the limited context size that affects the model's understanding and response capabilities.

In the realm of natural language processing, attention matrices play a crucial role in determining the influence of each token within a given context. This cross-correlation matrix, often represented as an NxN matrix, affects the overall model size and performance.

One possible approach to overcome the context size limitation … click here to read

Improving Llama.cpp Model Output for Agent Environment with WizardLM and Mixed-Quantization Models

Llama.cpp is a powerful tool for generating natural language responses in an agent environment. One way to speed up the generation process is to save the prompt ingestion stage to cache using the --session parameter and giving each prompt its own session name. Furthermore, using the impressive and fast WizardLM 7b (q5_1) and comparing its results with other new fine tunes like TheBloke/wizard-vicuna-13B-GGML could also be useful, especially when prompt-tuning. Additionally, adding the llama.cpp parameter --mirostat has been … click here to read

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