Vicuna 7B on GCP Vertex AI

Name: Vicuna 7B on GCP Vertex AI
Brand: LMSYS Org

Vicuna · LMSYS Org

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Last refreshed 2026-05-11. Next refresh: weekly.

Why use Vicuna 7B on GCP Vertex AI?

GCP Vertex AI offers Vicuna 7B with competitive pricing. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.

Input / 1M

Output / 1M

Cache

Not sourced

Batch

Not sourced

Setup recipe

Python + curl

Install

pip install google-cloud-aiplatform

Auth

export GOOGLE_CLOUD_PROJECT=...

Call

import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")

Model ID

vicuna-7b

Request example

import os
import vertexai
from vertexai.generative_models import GenerativeModel

# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("vicuna-7b")
response = model.generate_content("Hello")
print(response.text)

Gotchas

For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.

Capabilities

Structured Outputs

About Vicuna 7B

Vicuna-7B is an open-source language model crafted by LMSYS, fine-tuning the LLaMA model using around 125,000 user conversations from ShareGPT. It's designed for natural and fluent dialogues, effectively addressing a wide array of queries and generating text on diverse subjects. However, while it performs well, it may sometimes produce incorrect or biased responses due to its training limitations. Aimed primarily at research, it comes in various versions and quantizations to cater to different computational needs. Although helpful and polite, its performance is slightly lower compared to larger models like Vicuna-13B or Vicuna-33B 125.