# Model Catalogue ## Model Catalog The Model Catalogue is your launchpad for working with large AI models on AI Studio.\ In a single view, you can **discover models, inspect costs and licenses, run live tests in the Playground, and copy ready-to-use API snippets**—all before writing a line of production code. *** ### What You Can Do in the Catalogue * **Search & filter** models by model name, modality (text-generation, multimodal, speech-to-text, and more) or provider. * **Inspect key metadata** at a glance: model type, pricing tag, model provider * **Compare alternatives quickly**—sort by date of addition and number of parameter. * **Open a Playground** session directly from the card to test prompts or upload inputs. * **Copy starter cURL/Python code** pre-filled with the correct `model` string. * **Move to the next step** (fine-tune, evaluate, deploy) without leaving the Models section. *** ### Supported Modalities | Modality | Typical Use Cases | | ------------------ | ---------------------------------------------- | | Text-Generation | Chatbots, summarisation, code completion | | Text-to-Embedding | Semantic search, recommendation engines | | Image-Text-to-Text | Image captioning, visual Q\&A | | Speech-to-Text | Transcription, voice-assistant pipelines | | Text-to-Speech | Voice synthesis, IVR systems | | Multimodal | Combined vision & language reasoning | | Tokenizer | Stand-alone tokenisation for offline pipelines | *** ### Model Card Overview A Model Card contains three tabs: | Tab | Purpose | | ---------------- | ------------------------------------------------------------------------------------------------------------------------- | | **Playground** | Run interactive tests. Text models expose `temperature`, `top_p`, `max_tokens`, etc.; speech/image models accept uploads. | | **Starter Code** | Copy cURL and Python snippets that call the production endpoint with minimal setup. | | **Overview** | Architecture notes, training data summary, benchmarks, responsible-AI statements, and license details. | Common header tags—**pricing**, **license**, **GitHub**, **Base Model / Fine-tuned**—let you evaluate suitability at a glance. *** ### Quickstart · Trying a Model 1. Locate a model card and open **Playground**. 2. Enter a prompt or upload an input sample. 3. Adjust parameters if needed and click **Run**. 4. Review the output. 5. Switch to **Starter Code**, copy the snippet, and replace `""` with your own token. *** ### Choosing the Right Model | Consideration | Practical Guidance | | ---------------- | ------------------------------------------------------------------------------------- | | Task Fit | Match the modality to your problem (e.g., speech-to-text for call recordings). | | Latency vs Cost | Smaller models are cheaper and faster; larger models often deliver higher quality. | | Domain Alignment | Fine-tune when a base model’s answers are too generic for specialised content. | | Licensing | Confirm the license permits commercial or derivative use if your product requires it. | *** ### Requesting Additional Models If you need a model that is not listed, Please fill the form below with following details: {% embed url="" %} * Model name & version * Source link or paper reference * Intended use case and traffic details Requests are reviewed weekly and prioritised by feasibility and demand. *** ### Current Catalogue Snapshot (July 2025) | Model | Provider | Type | | ---------------------------------- | ------------ | ------------------ | | chitrapathak | Krutrim | Image-text-to-text | | Llama-3.2-11B-Vision-Instruct | Meta | Image-text-to-text | | gemma-3-27b-it | Google | Multimodal | | Llama-4-Maverick-17B-128E-Instruct | Meta | Multimodal | | Krutrim-Dhwani | Krutrim | Speech-to-Text | | DeepSeek-R1-Distill-Llama-70B | deepseek\_ai | Text-Generation | | DeepSeek-R1-Distill-Llama-8B | deepseek\_ai | Text-Generation | | DeepSeek-R1 | deepseek\_ai | Text-Generation | | Krutrim-1 | Krutrim | Text-Generation | | Krutrim-2 | Krutrim | Text-Generation | | Llama-3.3-70B-Instruct | Meta | Text-Generation | | Phi-4-reasoning-plus | Microsoft | Text-Generation | | Mistral-7B-v0.2 | MistralAI | Text-Generation | | Qwen3-30B-A3B | Qwen | Text-Generation | | Qwen3-32B | Qwen | Text-Generation | | Bhasantarit | Krutrim | Text-to-Embedding | | Vyakyarth | Krutrim | Text-to-Embedding | | Krutrim-TTS | Krutrim | Text-to-Speech | | Krutrim-tokenizer | Krutrim | Tokenizer | For current pricing and rate limits, see the **Billing** section. *** ### Next Steps * Continue to **AI Job → Inferencing** to run production jobs. * Review **Billing** for token accounting and GPU pricing. * Consult the **API Reference** for full endpoint specifications. *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.cloud.olakrutrim.com/basics/ai-studio/model-catalogue.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.