# lyuchenyang/Macaw-LLM

Macaw-LLM: Multi-Modal Language Modeling with Image, Video, Audio, and Text Integration

Repository: https://github.com/lyuchenyang/Macaw-LLM
Canonical: https://ross.abutalabs.com/products/macaw-llm
Language: Python
License: Apache-2.0
License Family: permissive
Topics: language-model, multi-modal-learning, natural-language-processing, deep-learning, machine-learning, neural-networks
Last push: 2025-01-01T15:08:04+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 85
- inputs: {"age_days": 1198, "days_push": 609, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1591, forks 130 (observed 2026-08-28T04:05:08.680120+00:00)

## What it is
Macaw-LLM is a multi-modal language modeling framework that integrates image, video, audio, and text data, built on CLIP, Whisper, and LLaMA. It provides code, datasets, and pretrained models for research into multi-modal instruction-following language models.

## Use cases
- build a multimodal LLM that understands images, video, audio and text
- experiment with combining CLIP, Whisper and LLaMA into one model
- train a language model on multi-modal instruction data
- research multi-modal alignment strategies for LLMs
- run a demo answering questions about images or audio
- fine-tune a multimodal model on custom datasets

## When to choose
- you need a research codebase for multi-modal language modeling across image, video, audio and text
- you want to build on CLIP, Whisper, and LLaMA with an existing alignment pipeline
- you are exploring multi-modal instruction tuning and need a starting point

## When to avoid
- you need a production-ready, well-maintained multimodal assistant
- you only need single-modality text generation
- you require commercial support or extensive documentation

## Facets
- artifact type: library
- maturity: experimental
- function: machine-learning, deep-learning, llm-training, nlp, image-processing, audio-processing, video-processing
- domain: large-language-models, machine-learning, deep-learning, artificial-intelligence
- platform: python
- tags: multimodal, llm, clip, whisper, llama, research, natural-language-processing

## Member repositories
- lyuchenyang/Macaw-LLM (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:08.680120+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:54:44.852739+00:00, confidence not recorded.
  - readme: https://github.com/lyuchenyang/Macaw-LLM (fetched 2026-08-28T04:05:08.680120+00:00, sha 9f5ea970e0e6)
- Data as of 2026-08-30T08:39:29.467469+00:00.