+
+

Related Products

  • Gemini Enterprise Agent Platform
    999 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Google AI Studio
    30 Ratings
    Visit Website
  • ClickLearn
    67 Ratings
    Visit Website
  • Google Cloud BigQuery
    2,023 Ratings
    Visit Website
  • Datasite Diligence Virtual Data Room
    692 Ratings
    Visit Website
  • SKU Science
    16 Ratings
    Visit Website
  • Partful
    20 Ratings
    Visit Website
  • AI Video Cut
    1 Rating
    Visit Website
  • Concord
    237 Ratings
    Visit Website

About

LLaVA (Large Language-and-Vision Assistant) is an innovative multimodal model that integrates a vision encoder with the Vicuna language model to facilitate comprehensive visual and language understanding. Through end-to-end training, LLaVA exhibits impressive chat capabilities, emulating the multimodal functionalities of models like GPT-4. Notably, LLaVA-1.5 has achieved state-of-the-art performance across 11 benchmarks, utilizing publicly available data and completing training in approximately one day on a single 8-A100 node, surpassing methods that rely on billion-scale datasets. The development of LLaVA involved the creation of a multimodal instruction-following dataset, generated using language-only GPT-4. This dataset comprises 158,000 unique language-image instruction-following samples, including conversations, detailed descriptions, and complex reasoning tasks. This data has been instrumental in training LLaVA to perform a wide array of visual and language tasks effectively.

About

Muse Glimmer is a 30-billion-parameter open-weights model from Meta Superintelligence Labs, optimized for always-on local agent workflows. Small enough to run on a Mac or PC with a single consumer GPU, it is designed for local agents, function calling, coding, and LLM-as-a-judge evaluation without depending on cloud infrastructure or network access. The model combines long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following. It can complete end-to-end agentic tasks, sustain multi-step reasoning across extended workflows, recover from failed or unexpected tool calls, and accept interleaved text and images through a dedicated perception encoder for interpreting screenshots, charts, and documents. Muse Glimmer works with OpenClaw and other agentic orchestration patterns, supports controllable reasoning effort, and is trained on data from more than 100 languages.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Researchers and anyone wanting a solution to generate and improve their AI-generated content

Audience

Developers building private, always-on AI agents that need strong reasoning, tool use, and multimodal capabilities on local hardware

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 5.0 / 5

Pros & Cons from Real Users

Pros

  • Muse Glimmer is exciting because it brings serious AI capability closer to the device. An open-weight model that can run on a laptop or desktop is a big deal for developers, builders, and AI power users who want more control. I like that it is focused on agentic tasks, not just basic chat. If it can handle reasoning, coding help, workflow automation, and local experimentation well, it could be really useful for private projects and always-on agents. The open-weight angle is the biggest win. Being able to download, modify, and run the model locally makes Muse Glimmer feel much more flexible than a closed API-only model.

Cons

  • I would still want to test it hard before trusting it for serious work. Smaller local models can be impressive, but they still need to prove themselves on coding, tool use, long tasks, and messy real-world prompts. Running locally also means the experience depends on your hardware. Even if it works on consumer devices, performance, speed, and setup may vary a lot.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

LLaVA
llava-vl.github.io

Company Information

Meta
Founded: 2004
United States
meta.ai/

Alternatives

PaliGemma 2

PaliGemma 2

Google

Alternatives

Grok 4.6

Grok 4.6

SpaceXAI
Qwen3.5

Qwen3.5

Alibaba
Falcon 2

Falcon 2

Technology Innovation Institute (TII)
Inkling

Inkling

Thinking Machines Lab

Categories

Categories

Integrations

ExecuTorch
GPT-4
Hermes Agent
Hugging Face
LLaMA-Factory
LM Studio
Meta AI
Muse Code
Ollama
OpenClaw
OpenCode
Unsloth

Integrations

ExecuTorch
GPT-4
Hermes Agent
Hugging Face
LLaMA-Factory
LM Studio
Meta AI
Muse Code
Ollama
OpenClaw
OpenCode
Unsloth
Claim LLaVA and update features and information
Claim LLaVA and update features and information
Claim Muse Glimmer and update features and information
Claim Muse Glimmer and update features and information