1 versions
A LLaVA model fine-tuned from Llama 3 Instruct with better scores in several benchmarks.
Install our magic
package manager:
curl -ssL https://magic.modular.com/ | bash
Then run the source
command that's printed in your terminal.
Install Max Pipelines in order to run this model.
magic global install max-pipelines
Start a local endpoint for llava-llama3/8b:
max-serve serve --huggingface-repo-id xtuner/llava-llama-3-8b-v1_1-gguf
The endpoint is ready when you see the URI printed in your terminal:
Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
Now open another terminal to send a request using curl
:
curl -N http://0.0.0.0:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "llava-llama3/8b",
"stream": true,
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who won the World Series in 2020?"}
]
}' | grep -o '"content":"[^"]*"' | sed 's/"content":"//g' | sed 's/"//g' | tr -d '
' | sed 's/\n/
/g'
🎉 Hooray! You’re running Generative AI. Our goal is to make this as easy as possible.
llava-llama3
is a LLaVA model fine-tuned from Llama 3 Instruct and CLIP-ViT-Large-patch14-336 using datasets like ShareGPT4V-PT and InternVL-SFT. It integrates foundational advancements in both language and vision models to achieve a state-of-the-art blend of multimodal understanding and reasoning. The model leverages Llama 3 as its base, paired with CLIP's visual backbone, enabling it to process and reason about text and visual inputs in a synergistic manner.
This fine-tuning process employs XTuner, a framework designed to optimize model performance on multimodal tasks through advanced training techniques. The inclusion of high-quality datasets such as ShareGPT4V-PT and InternVL-SFT ensures robust performance across diverse scenarios, making it effective for applications requiring advanced vision-language alignment.
DETAILS
MODULAR GITHUB
ModularCREATED BY
xtuner
MODEL
xtuner/llava-llama-3-8b-v1_1-gguf
TAGS
@ Copyright - Modular Inc - 2024