{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-bakllava","slug":"bakllava","name":"BakLLaVA (7B, 13B)","type":"model","url":"https://ollama.com/library/bakllava","page_url":"https://unfragile.ai/bakllava","categories":["image-generation"],"tags":["ollama","open-source","vision","SkunkworksAI"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-bakllava__cap_0","uri":"capability://image.visual.image.to.text.visual.question.answering.with.multimodal.reasoning","name":"image-to-text visual question answering with multimodal reasoning","description":"Processes images and natural language questions together through a unified Transformer architecture that fuses visual features from image encoders with Mistral 7B/13B language model embeddings. The LLaVA architecture projects image patches into the language model's token space, enabling the model to reason jointly over visual and textual context to generate coherent answers about image content. Supports both CLI and HTTP API interfaces with base64-encoded image inputs.","intents":["I need to ask questions about image content and get natural language answers without sending images to external APIs","I want to build a local vision-language chatbot that understands both text and images in a single inference pass","I need to analyze screenshots, diagrams, or photos and extract information through conversational prompts","I want to run vision-language inference on edge devices or air-gapped systems without cloud dependencies"],"best_for":["developers building privacy-first document analysis tools","teams deploying vision-language models on-premises or edge infrastructure","researchers prototyping multimodal reasoning systems with open-source models","solo developers needing lightweight VQA without cloud API costs"],"limitations":["Single image per request — cannot process multiple images in parallel or compare across images in one inference","32K token context window is fixed and cannot be extended — limits length of conversation history or detailed image descriptions","No documented performance benchmarks on standard VQA datasets (VQA v2, GQA, TextVQA) — actual accuracy unknown relative to closed-source alternatives","Inference latency not documented — 7B/13B models typically require 2-8 seconds per image on consumer GPUs, but actual speed unknown","No explicit support for image formats or resolution limits — may fail on unusual formats or very high-resolution images","Last updated 2 years ago — potential staleness in vision-language alignment techniques compared to recent models like LLaVA 1.6 or GPT-4V"],"requires":["Ollama 0.1.15 or later","8-16GB GPU VRAM minimum (inferred from 7B model size; 13B variant requires ~16-24GB)","Python 3.7+ with ollama package OR JavaScript runtime with ollama npm package OR CLI access to Ollama daemon","Image file in supported format (JPEG, PNG inferred but not explicitly documented)"],"input_types":["image (base64-encoded in API, file path in CLI)","text (natural language question or prompt)"],"output_types":["text (natural language response)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_1","uri":"capability://tool.use.integration.local.http.api.inference.for.vision.language.tasks","name":"local http api inference for vision-language tasks","description":"Exposes a RESTful HTTP endpoint at `http://localhost:11434/api/generate` that accepts JSON payloads containing model name, text prompts, and base64-encoded images, returning streaming or non-streaming text responses. Built on Ollama's unified API layer that abstracts model loading, VRAM management, and inference scheduling, enabling programmatic access without CLI overhead.","intents":["I want to integrate vision-language inference into a web application or microservice without managing model loading myself","I need to build a backend service that accepts image uploads and returns VQA responses via standard HTTP","I want to orchestrate multiple inference requests across different models using a single HTTP interface","I need to scale inference across multiple Ollama instances with load balancing"],"best_for":["backend developers building REST APIs that need vision capabilities","teams deploying Ollama as a shared inference service across applications","DevOps engineers containerizing vision-language inference for Kubernetes or Docker Compose","full-stack developers prototyping multimodal web applications"],"limitations":["HTTP overhead adds ~50-200ms latency per request compared to direct Python/JavaScript library calls","No built-in request queuing or priority scheduling — concurrent requests may timeout if GPU is saturated","Streaming responses require client-side handling of chunked transfer encoding — not all HTTP clients handle this transparently","No authentication or rate limiting in base Ollama API — requires reverse proxy (nginx, Caddy) for production security","Base64 encoding of images increases payload size by ~33% compared to binary transmission","Single Ollama instance per machine — horizontal scaling requires manual load balancer setup"],"requires":["Ollama daemon running and accessible on network (default localhost:11434)","HTTP client library (curl, requests, fetch, axios, etc.)","Image data pre-encoded as base64 string","JSON serialization support in client language"],"input_types":["JSON payload with fields: model (string), prompt (string), images (array of base64 strings)"],"output_types":["JSON response with field: response (string, streaming or complete)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_2","uri":"capability://tool.use.integration.python.and.javascript.sdk.integration.for.vision.language.inference","name":"python and javascript sdk integration for vision-language inference","description":"Provides native language bindings through the `ollama` Python package and JavaScript npm package that wrap the HTTP API with idiomatic syntax, automatic base64 encoding of images, and streaming response handling. Developers call `ollama.chat(model='bakllava', messages=[...])` or equivalent JavaScript syntax, abstracting HTTP details and enabling seamless integration into Python data pipelines or Node.js applications.","intents":["I want to call BakLLaVA from Python without writing HTTP boilerplate or base64 encoding logic","I need to integrate vision-language inference into a Node.js/Express backend with native async/await syntax","I want to chain vision-language calls with other Python libraries (PIL, OpenCV, pandas) in a single script","I need to prototype a multimodal chatbot in JavaScript with minimal dependencies"],"best_for":["Python data scientists and ML engineers building vision pipelines","Node.js/JavaScript developers adding vision capabilities to existing backends","researchers prototyping multimodal systems in Jupyter notebooks","full-stack developers using Python/JavaScript across frontend and backend"],"limitations":["Python SDK requires Python 3.7+ — no support for Python 2.x or older 3.x versions","JavaScript SDK requires Node.js 14+ — browser-based usage not supported (Ollama daemon must be local or network-accessible)","Streaming responses in Python require manual iteration over response chunks — no built-in async generator support","Image input must be file path or bytes object — no direct URL fetching (developer must download images first)","No built-in retry logic or exponential backoff — network failures require manual error handling","SDK versions may lag behind Ollama daemon releases — version mismatches can cause subtle API incompatibilities"],"requires":["Python 3.7+ with `pip install ollama` OR Node.js 14+ with `npm install ollama`","Ollama daemon running on localhost:11434 (or custom OLLAMA_HOST environment variable)","Image file accessible as local path or loaded into memory as bytes"],"input_types":["Python: messages list with dicts containing 'role', 'content', 'images' keys; images as file paths or base64 strings","JavaScript: messages array with objects containing role, content, images properties"],"output_types":["Python: dict with 'message' key containing response text, or async generator for streaming","JavaScript: Promise resolving to object with message property"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_3","uri":"capability://text.generation.language.cli.based.interactive.vision.language.chat.with.image.input","name":"cli-based interactive vision-language chat with image input","description":"Provides a command-line interface (`ollama run bakllava`) that launches an interactive REPL where users type prompts and image file paths inline (e.g., 'What's in this image? /path/to/image.png'), with responses streamed to stdout. The CLI automatically loads the model into GPU memory, handles image file I/O, and manages the conversation context across multiple turns.","intents":["I want to quickly test BakLLaVA on local images without writing code","I need to analyze a batch of screenshots or photos interactively from the terminal","I want to debug vision-language model behavior by asking questions about specific images in real-time","I need a simple tool for non-developers to ask questions about images without a GUI"],"best_for":["developers debugging model behavior during development","researchers exploring model capabilities without writing scripts","DevOps engineers testing model deployment on new machines","non-technical users who prefer terminal interfaces"],"limitations":["Single-turn or multi-turn conversation context is not explicitly documented — unclear if conversation history persists across prompts","Image path must be absolute or relative to current working directory — no support for URLs or clipboard images","No batch processing — must run one image at a time, making it inefficient for analyzing many images","Streaming output cannot be easily captured or parsed programmatically — better to use SDK for automation","No built-in image preview or validation — user must manually verify image paths exist","Terminal output may be slow for large responses — no progress indicators or token-per-second metrics shown"],"requires":["Ollama CLI installed and in PATH","Ollama daemon running (started automatically by `ollama run` if not already running)","Image file accessible on local filesystem","Terminal/shell with standard input/output"],"input_types":["text (natural language prompt typed into REPL)","file path (image path provided inline with prompt)"],"output_types":["text (streamed to stdout, one token at a time)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_4","uri":"capability://image.visual.lightweight.7b.and.13b.parameter.model.variants.for.hardware.constrained.deployment","name":"lightweight 7b and 13b parameter model variants for hardware-constrained deployment","description":"Offers two parameter-efficient variants (7B with ~4.7GB footprint, 13B with larger footprint) based on Mistral language models, enabling deployment on consumer-grade GPUs (8-16GB VRAM for 7B, 16-24GB for 13B) and edge devices. The 7B variant trades some reasoning capacity for faster inference and lower memory overhead, while 13B provides improved accuracy for complex visual reasoning tasks.","intents":["I need to deploy vision-language inference on a laptop or edge device with limited GPU VRAM","I want to run multiple inference instances on a single GPU by using smaller models","I need to minimize latency for real-time vision applications like video frame analysis","I want to reduce cloud inference costs by running models locally on consumer hardware"],"best_for":["edge device developers (Jetson, mobile, embedded systems)","researchers comparing model size vs. accuracy tradeoffs","teams deploying vision inference on cost-constrained infrastructure","developers optimizing for latency-sensitive applications"],"limitations":["7B variant may struggle with complex visual reasoning or dense text recognition compared to larger models like LLaVA 13B or GPT-4V","13B variant requires 16-24GB GPU VRAM — not suitable for most consumer laptops or edge devices with <16GB VRAM","No quantized variants documented (e.g., 4-bit, 8-bit) — both models appear to be full precision, limiting further memory optimization","Inference speed not benchmarked — actual latency on different hardware unknown","No model distillation or pruning variants available — cannot trade accuracy for speed beyond the two provided sizes","Context window (32K tokens) is fixed for both variants — cannot be reduced to save memory"],"requires":["GPU with 8-16GB VRAM for 7B variant (RTX 3060, RTX 4060, M1/M2 Pro/Max, etc.)","GPU with 16-24GB VRAM for 13B variant (RTX 3080, RTX 4080, A100, etc.)","Ollama framework to manage model loading and quantization"],"input_types":["image (any format supported by underlying vision encoder)","text (natural language prompt)"],"output_types":["text (natural language response)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_5","uri":"capability://memory.knowledge.32k.token.context.window.for.extended.multimodal.conversations","name":"32k token context window for extended multimodal conversations","description":"Supports a fixed 32K token context window that allows developers to maintain conversation history across multiple image-and-text exchanges, enabling the model to reference previous images and questions within a single session. The context is managed by Ollama's inference engine, which tracks token usage and truncates or slides the window when limits are approached.","intents":["I want to ask follow-up questions about an image without re-sending the image each time","I need to compare multiple images by asking questions that reference previous images in the conversation","I want to build a document analysis chatbot that maintains context across multiple pages or screenshots","I need to debug model behavior by asking clarifying questions without losing prior context"],"best_for":["developers building multimodal chatbots with conversation history","document analysis applications requiring multi-page context","researchers studying how context window size affects vision-language reasoning","teams building interactive image annotation or analysis tools"],"limitations":["32K token limit is fixed and cannot be extended — no dynamic context window resizing","Token counting for images is not documented — unclear how many tokens each image consumes, making it hard to predict context exhaustion","Context sliding/truncation strategy not documented — unclear whether oldest messages are dropped or summarized when limit is reached","No explicit conversation memory management — developers must manually track which images/questions are in context","Conversation state is not persisted — restarting Ollama daemon loses all context","No cost/token accounting — developers cannot see how many tokens have been consumed in a conversation"],"requires":["Ollama 0.1.15+ with context window support","Sufficient GPU VRAM to hold model + full context (typically 2-4GB additional for 32K tokens)","SDK or API client that supports multi-turn message format"],"input_types":["messages array with multiple turns of text and images"],"output_types":["text (response conditioned on full conversation context)"],"categories":["memory-knowledge","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_6","uri":"capability://tool.use.integration.ollama.framework.integration.for.unified.model.management.and.inference.scheduling","name":"ollama framework integration for unified model management and inference scheduling","description":"BakLLaVA runs within Ollama's model management layer, which handles model downloading, quantization format selection, GPU memory allocation, and inference scheduling across multiple concurrent requests. Ollama abstracts away model format details (GGUF, safetensors, etc.) and provides a unified interface for loading, unloading, and switching between models without restarting the daemon.","intents":["I want to switch between different vision-language models without restarting my application","I need to manage GPU memory efficiently when running multiple models on the same hardware","I want to download and cache models automatically without manual setup","I need a framework that handles model format compatibility across different architectures"],"best_for":["developers building multi-model inference systems","teams deploying diverse open-source models on shared infrastructure","researchers comparing model performance without managing model loading code","DevOps engineers standardizing model deployment across teams"],"limitations":["Ollama abstracts model format details — developers cannot directly control quantization or optimization strategies","No built-in model versioning — only latest version of each model is cached, making it hard to pin specific versions","GPU memory management is automatic but not transparent — no visibility into memory allocation or eviction policies","Single Ollama daemon per machine — no distributed inference across multiple machines without custom orchestration","Model switching incurs GPU unload/load overhead (~1-5 seconds) — not suitable for sub-second model switching","No built-in A/B testing or canary deployment features — requires external tooling for gradual model rollouts"],"requires":["Ollama daemon installed and running","Network access to Ollama model registry (ollama.com) for model downloads","Sufficient disk space for model caching (7B model: ~4.7GB, 13B: ~8-10GB estimated)"],"input_types":["model name (string, e.g., 'bakllava')"],"output_types":["model loaded into GPU memory, ready for inference"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_7","uri":"capability://data.processing.analysis.base64.encoded.image.input.for.api.and.sdk.based.inference","name":"base64-encoded image input for api and sdk-based inference","description":"Accepts images as base64-encoded strings in the `images` array parameter of HTTP API and SDK calls, eliminating the need for file uploads or multipart form data. The model decodes the base64 string, passes it to the vision encoder, and processes it alongside text prompts in a single forward pass.","intents":["I want to send images from a web browser or mobile app to a local Ollama instance without file uploads","I need to embed images directly in JSON payloads for easier API integration","I want to process images from URLs by downloading and encoding them in my application","I need to pass images through a pipeline without writing them to disk"],"best_for":["web developers building frontend-to-backend vision pipelines","API designers standardizing on JSON-only payloads","developers processing images from URLs or memory buffers","teams avoiding multipart form data complexity"],"limitations":["Base64 encoding increases payload size by ~33% compared to binary transmission — impacts network bandwidth and API latency","No explicit image format validation — API may silently fail or produce garbage output for unsupported formats","No image preprocessing or resizing — large images are encoded as-is, potentially exceeding payload size limits","Single image per request — cannot pass multiple images in one call (though `images` array suggests future multi-image support)","No streaming image input — entire image must be encoded before sending request","Decoding base64 adds CPU overhead on the server side — not ideal for high-throughput inference"],"requires":["Image data available as bytes or file","Base64 encoding library (built-in to most languages: Python base64, JavaScript Buffer, etc.)","JSON serialization support"],"input_types":["base64-encoded string (image data)"],"output_types":["image processed by vision encoder and passed to language model"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-bakllava__cap_8","uri":"capability://text.generation.language.streaming.text.response.generation.for.real.time.output","name":"streaming text response generation for real-time output","description":"Streams model responses token-by-token to the client via chunked HTTP transfer encoding (in API mode) or line-by-line output (in CLI mode), allowing users to see partial results before the full response is generated. The streaming mechanism reduces perceived latency and enables cancellation of long-running inferences.","intents":["I want to display model responses in real-time as they are generated, not wait for the full response","I need to cancel inference if the model is generating irrelevant or incorrect output","I want to build interactive chatbots that feel responsive with streaming output","I need to reduce perceived latency in user-facing applications by showing partial results"],"best_for":["web developers building real-time chatbot UIs","teams building interactive vision analysis tools","developers optimizing for perceived performance in user-facing applications","researchers studying how streaming affects user experience"],"limitations":["Streaming requires client-side handling of chunked transfer encoding — not all HTTP clients support this transparently","Token-by-token streaming adds overhead compared to batch response generation — may increase total latency","No built-in token counting in stream — developers cannot predict response length or cost","Cancellation mid-stream may leave GPU in inconsistent state — unclear if partial inference is properly cleaned up","Streaming responses cannot be easily cached or replayed — each request must be streamed fresh","No backpressure mechanism — client cannot slow down token generation if it cannot keep up with processing"],"requires":["HTTP client with chunked transfer encoding support (most modern clients: fetch, requests, axios, etc.)","Event handling or callback mechanism to process tokens as they arrive","Ollama 0.1.15+ with streaming support"],"input_types":["model, prompt, images (same as non-streaming)"],"output_types":["stream of JSON objects, each containing a partial response token"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Ollama 0.1.15 or later","8-16GB GPU VRAM minimum (inferred from 7B model size; 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