# cambrian-mllm/cambrian

Cambrian-1 is a family of multimodal LLMs with a vision-centric design.

Repository: https://github.com/cambrian-mllm/cambrian
Canonical: https://ross.abutalabs.com/products/cambrian
Homepage: https://cambrian-mllm.github.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: chatbot, clip, computer-vision, dino, instruction-tuning, large-language-models, llms, mllm, multimodal-large-language-models, representation-learning
Last push: 2025-11-07T03:26:25+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 51, release rhythm 35, longevity 57
- inputs: {"age_days": 807, "days_push": 299, "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 2013, forks 140 (observed 2026-08-28T04:06:05.342533+00:00)

## What it is
Cambrian-1 is a fully open family of vision-centric multimodal large language models (MLLMs) from NYU's VISIONx group, with training and evaluation toolkits. It includes released model weights, instruction-tuning datasets (Cambrian 10M), and a 26-benchmark MLLM evaluation suite.

## Use cases
- train a multimodal LLM with vision-centric encoders
- fine-tune an MLLM on image instruction-tuning data
- evaluate multimodal LLMs across 26 benchmarks
- run image question answering with an open vision-language model
- study how vision representations like CLIP and DINO affect MLLM performance
- download open multimodal instruction-tuning datasets

## When to choose
- you need fully open weights, data, and code for multimodal LLM research
- you want to experiment with vision encoder combinations for MLLMs
- you need a reproducible MLLM benchmarking suite on HPC clusters

## When to avoid
- you need a production-ready multimodal API or hosted service
- you only need text-only LLMs
- you need video-native multimodal models rather than image-based ones

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-training, chatbot, image-processing
- domain: large-language-models, computer-vision, machine-learning, artificial-intelligence
- platform: python
- tags: multimodal-llm, vision-language-model, instruction-tuning, clip, dino, mllm, research, model-training, evaluation-suite, gpu, linux

## Member repositories
- cambrian-mllm/cambrian (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:05.342533+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 2026-08-30T03:00:54.594753+00:00, confidence not recorded.
  - readme: https://github.com/cambrian-mllm/cambrian (fetched 2026-08-28T04:06:05.342533+00:00, sha c21c80ed7547)
  - homepage: https://cambrian-mllm.github.io/ (fetched 2026-08-29T10:40:52.266568+00:00, sha 76d75cd57c17)
- Data as of 2026-08-30T08:39:29.467469+00:00.