3 versions
General use chat model based on Llama and Llama 2 with 2K to 16K context sizes.
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 vicuna/7b:
max-serve serve --huggingface-repo-id lmsys/vicuna-7b-v1.5
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": "vicuna/7b",
"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'
๐ Hooray! Youโre running Generative AI. Our goal is to make this as easy as possible.
Vicuna is an advanced chat assistant model available in three distinctive variants, each designed for specific use cases. The v1.3 variant is fine-tuned from the Llama model and supports a maximum context size of 2048 tokens. Meanwhile, v1.5 is a refinement based on Llama 2, retaining the same context size of 2048 tokens. For applications requiring a larger working memory, the v1.5-16k variant, also fine-tuned from Llama 2, expands the context size substantially to 16,000 tokens.
All three versions are trained on conversational data derived from ShareGPT, ensuring a foundation built on diverse, user-interactive dialogues. These models target tasks ranging from casual question answering to deeper engagements, such as complex explanations or multi-paragraph elaborations.
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DETAILS
MAX Models are extremely optimized inference pipelines to run SOTA performance for that model on both CPU and GPU. For many of these models, they are the fastest version of this model in the world.
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lmsys
MODEL
lmsys/vicuna-7b-v1.5
TAGS
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