Using LLaVA 1.6 Vicuna 13B on Replicate API
Implementation guide · LLaVA 1.6 · Haotian Liu
Replicate API exposes LLaVA 1.6 Vicuna 13B through model ID yorickvp/llava-v1.6-vicuna-13b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-05-19. Next refresh: weekly.
Quick Start
- 1
- 2Use the Replicate API SDK or REST API to call
yorickvp/llava-v1.6-vicuna-13b— see the documentation for request format. - 3
Code Examples
pip install replicateREPLICATE_API_TOKENyorickvp/llava-v1.6-vicuna-13bReplicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
import replicate
# reads REPLICATE_API_TOKEN from env
# yorickvp/llava-v1.6-vicuna-13b format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"yorickvp/llava-v1.6-vicuna-13b",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Pricing on Replicate API
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.10 |
| Output tokens | $0.50 |
Capabilities
No model capability flags are currently sourced.
About LLaVA 1.6 Vicuna 13B
LLaVA 1.6 Vicuna 13B is a sophisticated multimodal language model designed to handle multimodal chatbot tasks, integrating text and image processing seamlessly. It features a pre-trained LLM, Vicuna-13B, and a likely CLIP ViT-L/14 vision encoder, linked using a trainable projection matrix, allowing it to comprehend both textual and visual content efficiently. The model offers capabilities such as image captioning, visual question answering, and enhanced reasoning and OCR, with the added advantage of processing high-resolution images up to 672x672 pixels.