Vicuna 7B 16K on GCP Vertex AI

Name: Vicuna 7B 16K on GCP Vertex AI
Brand: LMSYS Org

Vicuna · LMSYS Org

Serverless

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

Why use Vicuna 7B 16K on GCP Vertex AI?

GCP Vertex AI offers Vicuna 7B 16K 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-16k

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-16k")
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 16K

Vicuna-7B-v1.5-16k is a large language model (LLM) designed as an advanced chat assistant, developed by LMSYS. It's built on a transformer architecture and fine-tuned from Llama 2, with a notable feature being its 16k context window achieved using linear RoPE scaling. This allows the model to process much longer sequences of text, making it highly effective for comprehensive conversations. Trained on approximately 125,000 conversations from ShareGPT.com, Vicuna demonstrates strong capabilities in handling open-ended dialogues, responding to questions, and supporting various natural language tasks. Despite its strengths, it shares common limitations with other LLMs, like potential biases and performance variability across tasks and languages. Its inference speed and computational requirements are significant due to its 7-billion parameter size. The model is available under the Llama 2 Community License Agreement with various quantized versions for optimized performance.