Llava Phi3 3.8B - Details

Llava Phi3 3.8B is a large language model developed by the community-driven project Xtuner, featuring 3.8 billion parameters. It is designed for multimodal tasks, integrating a visual encoder to enhance image understanding. The model's license details are not specified.

Llava Phi3 3.8B is a large language model developed by XTuner with 3.8 billion parameters. It is a LLaVA model fine-tuned from microsoft/Phi-3-mini-4k-instruct and CLIP-ViT-Large-patch14-336, leveraging datasets like ShareGPT4V-PT and InternVL-SFT. The model is available in GGUF format and is optimized for visual and language tasks through specialized preprocessing and training strategies. Its design emphasizes multimodal capabilities, integrating a visual encoder for enhanced image understanding.

Llava Phi3 3.8B is a large language model with 3.8b parameters and a 4k context length, positioning it as a small-scale model optimized for efficiency and simplicity. The 3.8b parameter size ensures faster inference and lower resource demands, making it suitable for tasks requiring moderate complexity without heavy computational overhead. Its 4k context length supports short to moderate-length inputs, ideal for scenarios where brevity and speed are prioritized over handling extended sequences. These choices reflect a balance between performance and accessibility, catering to applications where resource constraints or task scope limit the need for larger scales.

• Parameter Size: 3.8b
• Context Length: 4k

Llava Phi3 3.8B is a large language model designed for multimodal tasks, with possible applications in areas like image description generation, visual question answering, and multimodal data analysis. Its integration of a visual encoder suggests possible use cases where text and image understanding intersect, such as analyzing visual content alongside textual data. However, these possible uses require thorough exploration to confirm their effectiveness and suitability for specific scenarios. The model’s focus on visual and language tasks makes it possible to adapt for creative or analytical workflows, but further testing is needed to validate its performance in real-world contexts. The intended uses listed here are possible directions for research or experimentation, not guaranteed outcomes.

• image description generation
• visual question answering
• multimodal data analysis

Llava Phi3 3.8B is a large language model with possible applications in areas like image description generation, visual question answering, and multimodal data analysis. Its design suggests possible uses for tasks requiring integration of visual and textual information, such as creating descriptive summaries of images or interpreting visual content alongside text. Possible applications could extend to interactive storytelling, where the model generates narratives based on visual prompts, or analyzing datasets that combine images and text for insights. However, these possible uses require thorough evaluation to ensure they align with specific needs and constraints. The model’s focus on multimodal tasks makes it possible to adapt for creative or analytical workflows, but each possible application must be rigorously tested before deployment.

• image description generation
• visual question answering
• multimodal data analysis
• interactive storytelling

Llava Phi3 3.8B in its medium q4 version requires a GPU with at least 12GB VRAM for efficient operation, making it suitable for mid-range hardware. This quantized version balances precision and performance, reducing memory usage compared to higher-precision formats like fp16. Users should ensure their system has 32GB RAM and adequate cooling to handle the workload. While possible applications for this version include tasks requiring moderate computational resources, the exact hardware needs may vary based on implementation.

• fp16, q4

Llava Phi3 3.8B is a large language model developed by XTuner with 3.8 billion parameters, designed for multimodal tasks like image understanding and visual question answering. It supports fp16 and q4 quantized versions, making it adaptable for different hardware requirements while maintaining performance for visual and language integration.

Huggingface Model Page
Ollama Model Page