Bakllava

Bakllava 7B - Details

Last update on 2025-05-19

Bakllava 7B is a large language model developed by Llava Hugging Face, a company known for its contributions to open-source AI. With 7b parameters, it leverages the capabilities of Mistral 7B and integrates LLaVA to enable multimodal processing. The model's license details are not explicitly specified, but it is designed to handle diverse tasks requiring both text and visual understanding.

Description of Bakllava 7B

BakLLaVA is a large language model derived from the original LLaVA architecture, leveraging Mistral-7b as its text backbone. It demonstrates that Mistral 7B outperforms Llama 2 13B on multiple benchmarks, showcasing strong performance. The model is fully open-source but trained on data from LLaVA's corpus, which has non-commercial licensing restrictions. It supports multi-image and multi-prompt generation, enabling complex multimodal tasks. Optimizations like 4-bit quantization and Flash-Attention 2 enhance its efficiency and scalability.

Parameters & Context Length of Bakllava 7B

7b 4k

Bakllava 7B is a large language model with 7b parameters, placing it in the small to mid-scale range, offering efficient performance for tasks requiring moderate complexity while balancing resource usage. Its 4k context length supports short to moderate-length inputs, making it suitable for tasks like dialogue or concise text analysis but less effective for extended or highly detailed content. The model’s design emphasizes accessibility and optimization, leveraging Mistral-7b’s efficiency while maintaining flexibility for multimodal applications.

  • Name: Bakllava 7B
  • Parameter Size: 7b
  • Context Length: 4k
  • Implications: Efficient for simple tasks, limited to short texts, balances performance and resource use.

Possible Intended Uses of Bakllava 7B

visual question answering multi modal instruction following

Bakllava 7B is a large language model designed for multimodal tasks, with possible uses including image captioning, visual question answering, and multi-modal instruction following. These possible applications could enable interactions where text and visual data are combined, such as generating descriptions for images or responding to queries that involve both text and visuals. However, the possible effectiveness of these uses depends on factors like training data, model optimization, and specific task requirements, which would need further investigation. The model’s design suggests it could support possible scenarios where users request actions based on both textual and visual inputs, but the possible limitations of its 7b parameter size and 4k context length might affect performance in complex or extended tasks.

  • Name: Bakllava 7B
  • Intended Uses: image captioning, visual question answering, multi-modal instruction following
  • Purpose: multimodal interaction and processing
  • Important Info: potential applications require thorough exploration and validation.

Possible Applications of Bakllava 7B

image captioning collaborative workflows text to image interaction diagram analysis mixed media interpretation

Bakllava 7B is a large language model with possible applications in areas such as image captioning, visual question answering, and multi-modal instruction following, which could enable possible interactions between text and visual data. Possible uses might include generating descriptive text for images, answering queries that combine textual and visual elements, or following instructions that involve both modalities. Possible scenarios could extend to tasks like analyzing diagrams, interpreting mixed-media content, or supporting collaborative workflows where text and visuals are integrated. However, these possible applications require thorough evaluation to ensure alignment with specific needs and constraints, as the model’s 7b parameter size and 4k context length may influence its suitability for certain tasks.

  • Name: Bakllava 7B
  • Possible Applications: image captioning, visual question answering, multi-modal instruction following
  • Important Info: Each application must be thoroughly evaluated and tested before deployment.

Quantized Versions & Hardware Requirements of Bakllava 7B

8 vram fp16 q4 q8 q2

Bakllava 7B’s medium q4 version requires a GPU with at least 8GB VRAM for efficient operation, making it suitable for mid-range hardware while maintaining a balance between precision and performance. This quantized version reduces memory usage compared to full-precision models, allowing deployment on systems with limited resources. However, users should verify their GPU’s VRAM and compatibility to ensure smooth execution. The model’s 7b parameter size and q4 quantization make it accessible for general-purpose tasks, but specific performance may vary based on system configuration.

  • Quantized Versions: fp16, q2, q3, q4, q5, q6, q8
  • Name: Bakllava 7B
  • Important Info: Hardware requirements depend on quantization level and system specifications.

Conclusion

Bakllava 7B is a large language model developed by Llava Hugging Face, combining Mistral 7B with LLaVA for multimodal capabilities, featuring 7b parameters and optimizations like 4-bit quantization and Flash-Attention 2. It supports multi-image and multi-prompt generation but is trained on non-commercial LLaVA data, requiring careful consideration of its open-source nature and technical constraints.

References

Huggingface Model Page
Ollama Model Page

Model
Maintainer
Parameters & Context Length
  • Parameters: 7b
  • Context Length: 4K
Statistics
  • Huggingface Likes: 53
  • Huggingface Downloads: 12K
Intended Uses
  • Image Captioning
  • Visual Question Answering
  • Multi-Modal Instruction Following
Languages
  • English