{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dbrx","slug":"dbrx","name":"DBRX","type":"model","url":"https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm","page_url":"https://unfragile.ai/dbrx","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dbrx__cap_0","uri":"capability://text.generation.language.fine.grained.mixture.of.experts.language.generation.with.36b.active.parameters","name":"fine-grained mixture-of-experts language generation with 36b active parameters","description":"DBRX implements a 16-expert MoE architecture with 4 experts active per token, routing tokens through a learned gating mechanism to select the most relevant expert combination from 65x more possible expert combinations than coarser 8-expert designs. This fine-grained routing enables 36B active parameters (27% of 132B total) to achieve performance parity with much larger dense models while maintaining 2x inference speed advantage over LLaMA2-70B. The architecture uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA) to optimize both training and inference efficiency.","intents":["Deploy a high-performance open LLM that matches GPT-3.5 capability while using 40% fewer parameters than Grok-1","Generate text with 2x faster inference latency than LLaMA2-70B without sacrificing quality","Train or fine-tune a mixture-of-experts model with 4x better compute efficiency than previous-generation dense models","Leverage fine-grained expert routing for better quality than coarse 2-expert-active designs in specialized domains"],"best_for":["Teams deploying open-source LLMs at scale seeking inference speed and parameter efficiency trade-offs","Researchers studying mixture-of-experts architectures and fine-grained routing mechanisms","Organizations with GPU infrastructure seeking to self-host competitive alternatives to GPT-3.5","Databricks customers building custom LLM applications with access to training infrastructure"],"limitations":["Only 36B of 132B parameters active per token — full model must be loaded into VRAM even though only 27% is used per inference step","Fine-grained MoE architecture adds routing overhead and complexity compared to dense models; exact latency per routing decision not documented","Hardware requirements for 132B model inference not explicitly specified; likely requires multi-GPU setup (A100/H100 class)","No documented support for quantization (GGUF, int8, int4) — full precision inference may be required","32K context window is fixed and not extensible; smaller than some competing models (Claude 3 supports 200K)"],"requires":["GPU with sufficient VRAM to load 132B parameters (estimated 264GB in float16, likely requires 8x A100 80GB or equivalent)","PyTorch 2.0+ or compatible inference framework (vLLM, TGI, or Databricks Model Serving)","Hugging Face transformers library or compatible inference engine","For fine-tuning: Databricks workspace or self-hosted training infrastructure with distributed training support"],"input_types":["text prompts (up to 32K tokens)","multi-turn conversation context","code snippets for in-context learning"],"output_types":["generated text (streaming or batch)","code generation output","structured text (JSON, SQL, etc.)"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_1","uri":"capability://code.generation.editing.code.generation.and.programming.task.completion","name":"code generation and programming task completion","description":"DBRX Instruct surpasses CodeLLaMA-70B on programming benchmarks (HumanEval) through instruction-tuning on code-specific tasks. The model processes code context up to 32K tokens, enabling multi-file code understanding and generation. Inference is optimized to 150 tokens/second per user on Databricks Model Serving, making real-time code completion feasible. The model combines general language understanding with specialized code patterns learned during pretraining on mixed text and code data.","intents":["Generate production-quality code snippets and functions from natural language descriptions","Complete partial code implementations with context awareness across multiple files (up to 32K tokens)","Refactor or optimize existing code by understanding full codebase context","Solve programming challenges and algorithm problems (HumanEval-style tasks)"],"best_for":["Development teams integrating code generation into IDEs or development workflows","Developers building code-focused AI assistants or pair-programming tools","Organizations seeking open-source alternative to GitHub Copilot with self-hosting capability","Researchers evaluating code generation capabilities of open models"],"limitations":["Benchmark performance (HumanEval scores) not numerically specified — only relative comparison to CodeLLaMA-70B provided","No documented support for language-specific optimizations or syntax validation","Context window of 32K tokens limits multi-file understanding to smaller codebases","Fine-tuning methodology for code tasks not documented; unclear if specialized code fine-tuning is required or if base model suffices","No built-in integration with language servers or linters for real-time syntax checking"],"requires":["DBRX Instruct model weights from Hugging Face or Databricks","Inference framework supporting 32K context (vLLM, TGI, or Databricks Model Serving)","GPU with sufficient VRAM (estimated 264GB float16 for full model)","Optional: IDE plugin or API wrapper for integration with development tools"],"input_types":["natural language code descriptions","partial code with context (up to 32K tokens)","multi-file code context","programming problem statements"],"output_types":["generated code (Python, JavaScript, SQL, etc.)","code completions","refactored code","algorithm implementations"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_10","uri":"capability://tool.use.integration.databricks.ecosystem.integration.for.sql.analytics.and.genai.workflows","name":"databricks ecosystem integration for sql, analytics, and genai workflows","description":"DBRX is natively integrated into Databricks GenAI products, enabling seamless SQL generation, analytics assistance, and LLM-powered workflows within the Databricks platform. Integration includes Vector Search for RAG, Model Serving for inference, and SQL Assistant for query generation. Customers can access DBRX through Databricks APIs without managing separate inference infrastructure. Integration enables end-to-end workflows combining data processing, retrieval, and generation within a single platform.","intents":["Generate SQL queries from natural language within Databricks SQL Assistant","Build RAG systems using Databricks Vector Search and DBRX inference","Implement LLM-powered analytics and business intelligence features","Integrate DBRX into Databricks notebooks and workflows for data science tasks"],"best_for":["Databricks customers seeking to add LLM capabilities to existing workflows","Organizations using Databricks for data engineering and analytics","Teams building AI-powered analytics and business intelligence features","Enterprises with Databricks infrastructure seeking to avoid multi-vendor complexity"],"limitations":["Integration is Databricks-specific; DBRX capability is not available through standard Databricks APIs for non-customers","Pricing and access model for DBRX within Databricks not documented","Integration features (SQL Assistant, Vector Search integration) are early rollouts; full feature set and stability not guaranteed","Requires Databricks workspace and associated infrastructure costs","No documented API for custom integration beyond Databricks-provided features"],"requires":["Databricks workspace (paid subscription)","Access to Databricks GenAI products (may require specific tier or early access)","Databricks SQL or notebooks for integration","Vector Search cluster (for RAG integration)","Model Serving cluster (for inference, if using custom endpoints)"],"input_types":["natural language queries (SQL Assistant)","documents for RAG (Vector Search)","data in Databricks tables","custom prompts and instructions"],"output_types":["SQL queries (SQL Assistant)","RAG-generated answers (Vector Search integration)","analytics insights and visualizations","notebook outputs and results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_11","uri":"capability://automation.workflow.hugging.face.and.github.model.distribution","name":"hugging face and github model distribution","description":"Distributes DBRX Base and Instruct model weights through Hugging Face Model Hub and GitHub repository, enabling direct download and integration into standard ML workflows. Models available in safetensors format (inferred) compatible with Hugging Face transformers library. Interactive demo available on Hugging Face Spaces for testing Instruct variant without local deployment.","intents":["Download and deploy DBRX locally using standard Hugging Face transformers workflows","Test DBRX Instruct capabilities through interactive web demo without GPU access","Integrate DBRX into existing ML pipelines using Hugging Face ecosystem tools"],"best_for":["Developers familiar with Hugging Face transformers and standard ML workflows","Teams evaluating DBRX before committing to deployment","Organizations with existing Hugging Face infrastructure"],"limitations":["Model format (safetensors, PyTorch, etc.) not explicitly documented; assumed safetensors based on Hugging Face standard","Download bandwidth and storage requirements not specified (estimated 250-300GB for fp16 weights)","Hugging Face Spaces demo may have rate limiting or availability constraints","No documented integration with other model hubs (Ollama, ModelScope, etc.)"],"requires":["Hugging Face account (free) for model access","Hugging Face transformers library 4.30+","Sufficient disk storage for 132B model weights (250-300GB estimated)","Git LFS for cloning GitHub repository"],"input_types":["model identifiers","download requests"],"output_types":["model weights","tokenizer files","configuration files"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_12","uri":"capability://automation.workflow.databricks.model.serving.api.with.150.tokens.second.throughput","name":"databricks model serving api with 150 tokens/second throughput","description":"Provides managed inference API through Databricks Model Serving platform, enabling production deployment without managing infrastructure. Achieves 150 tokens/second/user throughput on Databricks infrastructure, with automatic scaling and monitoring. API integrates with Databricks GenAI products for SQL generation and other specialized tasks, supporting both real-time and batch inference patterns.","intents":["Deploy DBRX in production without managing GPU infrastructure or scaling","Integrate DBRX inference into Databricks data platforms for SQL generation and analytics","Scale inference throughput automatically based on demand"],"best_for":["Databricks customers seeking managed inference without infrastructure overhead","Teams building Databricks-integrated AI applications","Organizations prioritizing operational simplicity over cost optimization"],"limitations":["Databricks Model Serving API pricing not disclosed; cost comparison vs self-hosted unknown","Throughput of 150 tokens/second/user is Databricks-specific; performance on other infrastructure unknown","API latency, availability SLA, and rate limiting not documented","Integration with non-Databricks systems not documented"],"requires":["Databricks workspace and account","Appropriate workspace tier supporting Model Serving (Pro or higher, inferred)","API authentication credentials"],"input_types":["text prompts","API requests"],"output_types":["text completions","API responses"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_2","uri":"capability://code.generation.editing.sql.generation.and.database.query.synthesis","name":"sql generation and database query synthesis","description":"DBRX achieves competitive performance with GPT-4 Turbo and surpasses GPT-3.5 Turbo on SQL generation tasks through early rollouts in Databricks GenAI products. The model understands database schemas, natural language intent, and generates syntactically correct SQL queries. Integration with Databricks SQL products enables real-time query generation with schema context. The fine-grained MoE architecture routes tokens through specialized experts for SQL syntax and semantic understanding.","intents":["Convert natural language questions into executable SQL queries against known database schemas","Generate complex SQL joins, aggregations, and window functions from English descriptions","Optimize existing SQL queries or suggest alternative implementations","Enable non-technical users to query databases through natural language interfaces"],"best_for":["Databricks customers building natural language SQL interfaces for data exploration","Data teams automating SQL query generation from business requirements","Organizations deploying open-source SQL assistants with competitive GPT-4 Turbo-level performance","Analytics platforms integrating AI-powered query builders"],"limitations":["SQL generation performance (exact accuracy metrics) not numerically specified — only relative comparison to GPT-3.5/GPT-4 Turbo provided","No documented support for database-specific dialects (PostgreSQL, MySQL, T-SQL, BigQuery SQL differences)","Schema context must fit within 32K token window; very large schemas may require truncation or retrieval","No built-in query validation or execution feedback loop — generated queries may be syntactically correct but semantically incorrect","Fine-tuning approach for SQL tasks not documented; unclear if specialized SQL fine-tuning is applied"],"requires":["DBRX model (Base or Instruct) from Hugging Face or Databricks","Database schema context (DDL or schema description)","Inference framework supporting 32K context","GPU with sufficient VRAM (estimated 264GB float16)","Optional: Databricks workspace for native SQL generation integration"],"input_types":["natural language questions (e.g., 'Show me total revenue by region for Q4')","database schema definitions (DDL or structured schema metadata)","existing SQL queries for optimization","multi-turn conversation context for iterative query refinement"],"output_types":["SQL SELECT statements","CREATE/ALTER/DROP DDL","complex queries with JOINs, CTEs, window functions","query explanations in natural language"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_3","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.with.long.context.understanding","name":"retrieval-augmented generation (rag) with long context understanding","description":"DBRX achieves leading performance among open models on RAG tasks through 32K token context window and instruction-tuning for information synthesis. The model processes retrieved documents, maintains coherence across long contexts, and generates answers grounded in provided sources. The fine-grained MoE architecture enables efficient processing of dense retrieved context without quality degradation. Integration with Databricks Vector Search and retrieval systems enables end-to-end RAG pipelines.","intents":["Build RAG systems that retrieve documents and generate answers grounded in retrieved context","Process long documents (up to 32K tokens) and synthesize information across multiple sources","Implement question-answering systems over proprietary knowledge bases or document collections","Generate citations and source attribution for RAG-generated answers"],"best_for":["Teams building enterprise knowledge base Q&A systems with open-source models","Organizations seeking RAG performance competitive with GPT-3.5 Turbo without API costs","Databricks customers integrating RAG into data analytics and business intelligence workflows","Researchers evaluating RAG capabilities of open models"],"limitations":["RAG benchmark performance (exact scores) not numerically specified — only relative comparison to GPT-3.5 Turbo provided","No documented retrieval integration — RAG capability assumes external retrieval system provides context","32K context window limits document volume; very large knowledge bases require chunking and multiple retrievals","No built-in citation mechanism or source attribution tracking — requires post-processing to extract and verify citations","Hallucination rates and failure modes on out-of-context questions not documented"],"requires":["DBRX Instruct model for RAG tasks","External retrieval system (vector database, BM25 search, or Databricks Vector Search)","Inference framework supporting 32K context (vLLM, TGI, or Databricks Model Serving)","GPU with sufficient VRAM (estimated 264GB float16)","Document chunking and embedding pipeline (e.g., LangChain, LlamaIndex)"],"input_types":["user questions or queries","retrieved document context (up to 32K tokens total)","multi-turn conversation history","metadata about retrieved documents"],"output_types":["generated answers grounded in retrieved context","source citations (document IDs or references)","confidence scores or uncertainty indicators","follow-up question suggestions"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_4","uri":"capability://text.generation.language.instruction.tuned.conversational.interaction.with.multi.turn.context","name":"instruction-tuned conversational interaction with multi-turn context","description":"DBRX Instruct variant is fine-tuned for instruction-following and conversational tasks, enabling natural multi-turn dialogue with coherent context management across up to 32K tokens. The model follows explicit instructions, maintains conversation state, and adapts tone/style based on user intent. Instruction-tuning methodology is not documented, but the variant demonstrates superior performance on MMLU and other benchmarks compared to base model. Inference throughput reaches 150 tokens/second per user on Databricks Model Serving.","intents":["Deploy conversational AI assistants that understand and follow user instructions across multi-turn dialogues","Build chatbots that maintain context and coherence over extended conversations (up to 32K tokens)","Create instruction-following agents that can reason about tasks and break them into steps","Implement customer support or knowledge base assistants with natural conversational ability"],"best_for":["Teams building conversational AI products with open-source models","Organizations seeking instruction-following capability competitive with GPT-3.5 without API dependency","Developers integrating LLMs into chatbot frameworks or conversational interfaces","Databricks customers building AI-powered analytics assistants"],"limitations":["Instruction-tuning methodology not documented; unclear what instruction-following techniques were used (SFT, RLHF, DPO, etc.)","No documented system prompt support or prompt engineering guidelines","Context window of 32K tokens limits very long multi-turn conversations; older messages may need to be summarized","No built-in memory or persistent state management — conversation history must be managed externally","Hallucination rates and failure modes on adversarial or out-of-distribution instructions not documented"],"requires":["DBRX Instruct model weights from Hugging Face or Databricks","Inference framework supporting 32K context and streaming output (vLLM, TGI, or Databricks Model Serving)","GPU with sufficient VRAM (estimated 264GB float16)","Conversation management layer (e.g., LangChain, LlamaIndex, or custom implementation)","Optional: API wrapper or chat interface (e.g., FastAPI, Gradio)"],"input_types":["user messages (text)","multi-turn conversation history","explicit instructions or system prompts","structured input (JSON for task specification)"],"output_types":["conversational responses (text)","streaming text output","structured output (JSON, markdown)","reasoning traces or step-by-step explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_5","uri":"capability://text.generation.language.general.purpose.language.understanding.and.reasoning","name":"general-purpose language understanding and reasoning","description":"DBRX Base and Instruct models achieve state-of-the-art performance on general language understanding benchmarks (MMLU) and reasoning tasks (GSM8K) through pretraining on 12 trillion carefully curated tokens. The model demonstrates competitive capability with Gemini 1.0 Pro and surpasses GPT-3.5 on general tasks. Fine-grained MoE architecture enables efficient parameter utilization while maintaining quality. 32K context window supports complex reasoning tasks requiring extended context.","intents":["Use as a general-purpose LLM for text understanding, summarization, and analysis tasks","Solve reasoning problems and mathematical questions (GSM8K-style tasks)","Perform information extraction, classification, and semantic understanding","Implement domain-agnostic AI features in applications (search, recommendations, content moderation)"],"best_for":["Organizations deploying open-source LLMs as general-purpose reasoning engines","Teams seeking GPT-3.5-level capability without API costs or data privacy concerns","Researchers evaluating general-purpose LLM capabilities and benchmarks","Developers building LLM-powered applications with diverse use cases"],"limitations":["Benchmark scores (MMLU, GSM8K) not numerically specified — only relative comparisons provided","Training data cutoff date not documented; knowledge may be outdated for recent events","Data contamination analysis not provided; unclear if benchmark datasets were in training data","No documented multilingual capability; likely English-primary based on training data description","Known biases and failure modes not documented; no bias mitigation methodology disclosed"],"requires":["DBRX Base or Instruct model from Hugging Face or Databricks","Inference framework (vLLM, TGI, Ollama, or Databricks Model Serving)","GPU with sufficient VRAM (estimated 264GB float16 for full model)","Optional: Quantization tools for reduced memory footprint (if supported)"],"input_types":["text prompts (up to 32K tokens)","multi-turn conversation context","structured input (JSON, CSV for analysis)","code or technical content for understanding"],"output_types":["generated text responses","reasoning traces or explanations","structured output (JSON, markdown)","classifications or labels"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_6","uri":"capability://automation.workflow.efficient.inference.serving.with.150.tokens.second.throughput","name":"efficient inference serving with 150 tokens/second throughput","description":"DBRX achieves up to 150 tokens/second per user throughput on Databricks Model Serving through optimized inference implementation leveraging the fine-grained MoE architecture. The model is 2x faster than LLaMA2-70B despite comparable capability, enabling real-time applications and high-concurrency serving. Inference optimization exploits the 36B active parameters per token (vs. 70B for LLaMA2), reducing memory bandwidth and compute requirements. Streaming output support enables progressive token generation for responsive user interfaces.","intents":["Deploy LLM inference at scale with high throughput and low latency for production applications","Serve multiple concurrent users with real-time response requirements (chatbots, code completion, search)","Reduce infrastructure costs by achieving GPT-3.5-level capability with 2x faster inference than LLaMA2-70B","Enable streaming text generation for responsive user experiences"],"best_for":["Teams deploying LLM inference at scale with cost and latency constraints","Organizations seeking to reduce API costs by self-hosting competitive open models","Developers building real-time LLM applications (chatbots, code completion, search)","Databricks customers leveraging native Model Serving integration"],"limitations":["Throughput benchmark (150 tokens/second) is measured per user on Databricks Model Serving; actual throughput depends on hardware, batch size, and context length","Hardware requirements not explicitly specified; 132B model likely requires multi-GPU setup (estimated 8x A100 80GB or equivalent)","Quantization support not documented; full precision inference may be required, limiting deployment options","Inference optimization is Databricks-specific; self-hosted inference may not achieve documented throughput without equivalent optimization","Latency per token not specified; throughput metric alone doesn't indicate time-to-first-token or p99 latency"],"requires":["GPU infrastructure with sufficient VRAM (estimated 264GB float16 for full model, or multi-GPU setup)","Inference framework optimized for MoE (vLLM with MoE support, TGI, or Databricks Model Serving)","Network bandwidth for model loading and inference requests","Optional: Load balancing and request queuing for multi-user serving"],"input_types":["text prompts (up to 32K tokens)","batch inference requests","streaming input for real-time applications"],"output_types":["streaming text tokens (real-time)","complete generated text (batch)","token probabilities or logits (if supported)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_7","uri":"capability://automation.workflow.pretraining.and.continued.training.from.checkpoints","name":"pretraining and continued training from checkpoints","description":"Databricks customers can pretrain DBRX-class models from scratch or continue training from DBRX checkpoints using Databricks training infrastructure. The training stack and methodology are available to enterprise customers, enabling custom model development with DBRX-scale efficiency (4x compute reduction vs. previous-generation MPT models). Continued training allows adaptation to domain-specific data or fine-tuning for specialized tasks. Training efficiency is achieved through careful data curation (12 trillion tokens) and optimized MoE architecture.","intents":["Pretrain custom LLMs from scratch using Databricks infrastructure with DBRX-class efficiency","Continue training from DBRX checkpoints on proprietary or domain-specific data","Fine-tune DBRX for specialized tasks (domain adaptation, instruction-tuning, alignment)","Develop custom MoE models with access to Databricks training stack and expertise"],"best_for":["Databricks enterprise customers with large proprietary datasets and training infrastructure","Organizations seeking to build custom LLMs with DBRX-scale efficiency and capability","Research teams studying mixture-of-experts training and optimization","Companies requiring domain-specific model variants with data privacy guarantees"],"limitations":["Training stack and methodology access is restricted to Databricks enterprise customers; specific access model and pricing not documented","Pretraining from scratch requires massive compute resources (estimated 1000s of GPU-hours); cost and timeline not specified","Fine-tuning methodology not documented; unclear what techniques are recommended (SFT, RLHF, DPO, etc.)","Data curation methodology not disclosed; unclear how to replicate 12 trillion token quality for custom pretraining","Training stability and convergence characteristics for continued training not documented"],"requires":["Databricks workspace with enterprise access to training infrastructure","Large-scale GPU cluster (estimated 100s of GPUs for efficient training)","Proprietary or curated training data (12+ trillion tokens for pretraining from scratch)","Expertise in distributed training, MoE optimization, and LLM training practices","Access to Databricks training stack and documentation (not publicly available)"],"input_types":["raw text data (for pretraining)","instruction-response pairs (for fine-tuning)","DBRX checkpoints (for continued training)","training configuration and hyperparameters"],"output_types":["trained model checkpoints","training logs and metrics","fine-tuned model variants","evaluation results on custom benchmarks"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_8","uri":"capability://tool.use.integration.open.source.model.distribution.via.hugging.face.with.commercial.license","name":"open-source model distribution via hugging face with commercial license","description":"DBRX Base and Instruct models are distributed via Hugging Face Hub under the Databricks Open Model License, enabling free download and self-hosting with commercial use permitted (subject to license restrictions not fully detailed). Model weights are available in standard formats (likely safetensors/PyTorch based on Hugging Face conventions). Interactive demo (Hugging Face Space) provides zero-setup evaluation. License is more permissive than some alternatives but includes restrictions not explicitly documented in public materials.","intents":["Download and self-host DBRX models without API costs or vendor lock-in","Evaluate model capability through interactive Hugging Face Space demo before deployment","Integrate DBRX into open-source projects and commercial applications (subject to license terms)","Fine-tune or adapt DBRX for custom use cases with full model access"],"best_for":["Developers and researchers seeking open-source LLM alternatives to proprietary models","Organizations with data privacy requirements necessitating self-hosted inference","Teams building open-source projects and commercial applications with permissive licensing","Enterprises evaluating DBRX before committing to Databricks infrastructure"],"limitations":["Databricks Open Model License restrictions not fully detailed in public materials; specific commercial use restrictions unknown","Model format specifications not documented; assumed safetensors/PyTorch but not explicitly confirmed","No quantized variants (GGUF, int8, int4) documented; full precision inference may be required","Hugging Face Space demo may have rate limiting or availability constraints","License may require attribution or other compliance measures not clearly specified"],"requires":["Hugging Face account (free) to download model weights","Storage for 132B model (estimated 264GB float16, 132GB float32)","GPU with sufficient VRAM for inference (estimated 264GB float16)","Inference framework (vLLM, TGI, Ollama, or custom implementation)","Internet connection for initial model download"],"input_types":["model weight files (safetensors or PyTorch format)","tokenizer configuration","model architecture definition"],"output_types":["downloaded model files ready for inference","model card with documentation","example code for inference"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__cap_9","uri":"capability://memory.knowledge.32k.token.context.window.for.extended.document.and.conversation.processing","name":"32k token context window for extended document and conversation processing","description":"DBRX supports a fixed 32K token context window, enabling processing of extended documents, multi-file code, and long conversation histories without external retrieval or summarization. The context window is implemented through standard transformer mechanisms (rotary position encodings) and is not dynamically extensible. 32K tokens accommodate approximately 24,000 words or 8-10 typical documents, enabling single-pass processing for many real-world scenarios. Context length is sufficient for RAG, code understanding, and multi-turn dialogue without requiring external memory systems.","intents":["Process long documents (research papers, books, legal contracts) in a single inference pass","Understand multi-file code context for refactoring or analysis tasks","Maintain extended multi-turn conversations without summarization or context pruning","Implement RAG systems without iterative retrieval for most document sets"],"best_for":["Applications requiring document understanding without external retrieval","Code analysis and refactoring tools working with multiple files","Conversational AI systems with extended dialogue requirements","RAG implementations where single-pass processing is preferred"],"limitations":["32K token context is fixed and not extensible; very large documents or codebases require chunking or external retrieval","Context window is smaller than some competing models (Claude 3 supports 200K, GPT-4 supports 128K)","Inference cost and latency scale with context length; full 32K context may be slower than shorter contexts","Position encoding (RoPE) may have degraded performance at maximum context length; exact degradation not documented","No documented support for context compression or summarization to extend effective context"],"requires":["DBRX model (Base or Instruct)","Inference framework supporting 32K context (vLLM, TGI, or Databricks Model Serving)","GPU with sufficient VRAM for full context (estimated 264GB float16)","Document chunking or retrieval system for larger documents (optional)"],"input_types":["text documents (up to 32K tokens)","code files (up to 32K tokens total)","conversation history (up to 32K tokens total)","structured context (JSON, markdown)"],"output_types":["analysis or understanding of full context","generated text grounded in context","citations or references to context"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dbrx__headline","uri":"capability://text.generation.language.state.of.the.art.mixture.of.experts.language.model","name":"state-of-the-art mixture-of-experts language model","description":"DBRX is a cutting-edge 132B parameter mixture-of-experts language model designed for general-purpose language and code generation, outperforming competitors on key benchmarks.","intents":["best language model for code generation","mixture-of-experts model for AI tasks","top LLM for programming challenges","high-performance language model for commercial use","best model for large context processing"],"best_for":["AI-driven programming tasks","high-context language generation"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["GPU with sufficient VRAM to load 132B parameters (estimated 264GB in float16, likely requires 8x A100 80GB or equivalent)","PyTorch 2.0+ or compatible inference framework (vLLM, TGI, or Databricks Model Serving)","Hugging Face transformers library or compatible inference engine","For fine-tuning: Databricks workspace or self-hosted training infrastructure with distributed training support","DBRX Instruct model weights from Hugging Face or Databricks","Inference framework supporting 32K context (vLLM, TGI, or Databricks Model Serving)","GPU with sufficient VRAM (estimated 264GB float16 for full model)","Optional: IDE plugin or API wrapper for integration with development tools","Databricks workspace (paid subscription)","Access to Databricks GenAI products (may require specific tier or early access)"],"failure_modes":["Only 36B of 132B parameters active per token — full model must be loaded into VRAM even though only 27% is used per inference step","Fine-grained MoE architecture adds routing overhead and complexity compared to dense models; 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