GCP Vertex AI exposes Vicuna 7B 16K through model ID vicuna-7b-16k. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-06-15. Next refresh: weekly.
Quick Start
- 1
- 2Use the GCP Vertex AI SDK or REST API to call
vicuna-7b-16k— see the documentation for request format.
Code Examples
pip install google-cloud-aiplatformGOOGLE_CLOUD_PROJECTvicuna-7b-16kFor 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".
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)Pricing on GCP Vertex AI
Capabilities
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.