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Fireworks: FireLLaVA 13B

FireLLaVA 13B: Fireworks’ Open-Weight Multimodal Model for Text+Image AI via API

Context: 4 000 tokens
Output: 4 000 tokens
Modality:
Image
Text
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Fireworks’ Open-Weight Vision-Language Model for Multimodal AI via API


FireLLaVA 13B is Fireworks AI’s open-weight multimodal LLM, built on the LLaVA (Large Language and Vision Assistant) architecture with 13B parameters. Designed for both text and image understanding, FireLLaVA enables developers to build applications that combine natural language reasoning with visual comprehension - ideal for enterprise AI, research, and multimodal assistants.

Available via AnyAPI.ai, FireLLaVA 13B gives developers production-ready access to multimodal AI without the complexity of managing infrastructure.

Key Features of FireLLaVA 13B

Multimodal Input (Text + Vision)

Processes images, diagrams, and screenshots alongside text prompts.

13B Parameter Model

Balances performance and efficiency, suitable for real-time and research applications.

Instruction-Tuned for Conversational AI

Fine-tuned for chat, grounded Q&A, and structured outputs.

Extended Context Support (up to 8k Tokens)

Capable of handling medium-length documents and multimodal reasoning workflows.

Open-Weight Flexibility

Released with open weights for private deployment, research, and fine-tuning.

Use Cases for FireLLaVA 13B

Document Intelligence

Parse PDFs, scanned documents, and visual-heavy reports with image+text inputs.

Multimodal RAG Assistants

Build retrieval-augmented generation systems that leverage both textual and visual context.

Education and Training Tools

Support multimodal tutoring with visual explanations and text-based reasoning.

Accessibility Applications

Enable text descriptions of images for visually impaired users.

Creative Media Workflows

Assist in annotation, content generation, and design ideation across text and image formats.

Comparison with other LLMs

Model
Context Window
Multimodal
Latency
Strengths
Model
Fireworks: FireLLaVA 13B
Context Window
Multimodal
Latency
Strengths
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