Model reference · open weights
Llama-3-VILA1.5 is an open-weight language model from Efficient-Large-Model. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | Efficient-Large-Model |
|---|---|
| Type | Language models |
| Task | Text gen |
| Runs with | transformers |
| Released | 2024-04-30 |
| Popularity | 3k downloads / month |
| Licence | Commercial licence needed |
About
Model type: VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
Model date: VILA1.5-40b was trained in May 2024.
Paper or resources for more information: https://github.com/NVLabs/VILA
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Where to send questions or comments about the model: https://github.com/NVLabs/VILA/issues
Primary intended uses: The primary use of VILA is research on large multimodal models and chatbots.
Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Architecture Type: Transformer Network Architecture: siglip, Llama3
Input Type: Image, Video, Text Input Format: Red, Green, Blue; MP4 ;String Input Parameters: 2D, 3D
Output Type: Text Output Format: String
Supported Hardware Microarchitecture Compatibility:
[Preferred/Supported] Operating System(s): Linux
See Dataset Preparation for more details.
** Data Collection Method by dataset
** Labeling Method by dataset
Properties (Quantity, Dataset Descriptions, Sensor(s)): 53 million image-text pairs or interleaved image text content.
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
Engine: [Tensor(RT), Triton, Or List Other Here]
Test Hardware:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
This model uses weights from meta-llama/Meta-Llama-3-8B. Llama 3 is licensed under the LLAMA 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys llama-3-vila1-5 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llama-3-vila1-5 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"llama-3-vila1-5","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.