This weblog submit focuses on new options and enhancements. For a complete checklist, together with bug fixes, please see the launch notes.
A brand new Python-based methodology for mannequin importing and inference
We now have fully revamped the best way fashions are uploaded and used for inference with a brand new Python-based methodology that prioritizes simplicity, pace, and developer expertise.
Constructed with a Python-first, user-centric design, this versatile strategy simplifies the method of working with fashions. It permits customers to focus extra on constructing and iterating, and fewer on navigating API mechanics. The brand new methodology streamlines inference, accelerates improvement, and considerably improves total usability.
Mannequin Add
The Clarifai Python SDK now makes it even simpler to add customized fashions. Whether or not you are utilizing a pre-trained mannequin from Hugging Face or OpenAI, or one you have developed from scratch, integration is seamless. As soon as uploaded, your mannequin can instantly benefit from Clarifai’s strong platform options.
After import, your mannequin is mechanically deployed and prepared to be used. You’ll be able to consider it, join it with different fashions and agent operators in a workflow, or serve inference requests immediately.
As a part of this launch, we’ve considerably simplified the way you outline the mannequin.py file for customized mannequin uploads. The brand new ModelClass sample permits you to implement predict, generate, and streaming strategies with out the necessity for further abstraction or boilerplate. You may get began in just some strains of code.
Right here’s a fast instance: a easy methodology that appends “Good day World” to any enter textual content, with built-in assist for several types of streaming responses. Take a look at the total documentation right here.
Inference
The brand new inference strategy gives an environment friendly, scalable, and simplified strategy to run predictions together with your fashions.
Designed with a Python-first, developer-friendly focus, it reduces complexity so you possibly can spend extra time constructing and iterating, and fewer time coping with low-level API particulars.
Under is an instance of tips on how to make a client-side predict name that corresponds to the predict methodology outlined within the earlier part. Checkout the docs right here.
New Revealed Fashions
- Revealed Llama-4-Scout-17B-16E-Instruct, a strong mannequin within the Llama 4 collection that includes 17 billion parameters and 16 specialists for superior instruction tuning. It helps a local 10 million-token context window (at the moment 8k supported on Clarifai), making it splendid for multi-document evaluation, advanced codebase understanding, and personalised, clever workflows.
- Revealed Qwen3-30B-A3B-GGUF, the newest addition to the Qwen collection. This new launch options each dense and mixture-of-experts (MoE) fashions, with important enhancements in reasoning, instruction-following, agent-based duties, and multilingual capabilities. The Qwen3-30B-A3B outperforms bigger fashions like QwQ-32B, leveraging fewer energetic parameters whereas sustaining sturdy efficiency throughout coding and reasoning benchmarks.
- Revealed OpenAI’s newest o3 mannequin, a strong and well-rounded LLM that units a brand new customary for efficiency throughout math, science, coding, and visible reasoning duties. It’s constructed for advanced, multi-step considering and excels at technical problem-solving, deciphering visible knowledge akin to charts and diagrams, high-stakes decision-making, and artistic ideation.
- Revealed o4-mini, a smaller mannequin optimized for quick, cost-efficient reasoning. Regardless of its compact dimension, o4-mini delivers spectacular accuracy on math and coding benchmarks like AIME 2025. It’s splendid to be used instances that require sturdy reasoning capabilities whereas conserving latency and value low. Each the fashions are additionally obtainable on the Playground, Attempt them out right here.
Enhanced the Playground expertise
- Added automated mode detection based mostly on the chosen mannequin — now intelligently switches between Chat and Imaginative and prescient modes for predictions.
- Improved mannequin search and identification for a quicker, extra correct choice expertise.
- Launched a Private Entry Token (PAT) dropdown, enabling customers to simply insert their PAT keys into code snippets.
- Carried out dynamic pricing show that updates based mostly on the chosen deployment.
- The chosen deployment ID is now mechanically injected into the inference code.
Enhanced the Management Heart
Improved the Neighborhood platform
- Revamped the Discover web page with refreshed visible designs, a featured fashions showcase, and categorized use instances akin to LLMs and VLMs.
- Up to date the person mannequin viewer web page with an improved UI, direct entry to the Playground, deployment listings, and extra enhancements.
Extra Modifications
- The House web page is now accessible to all customers, with sections requiring login mechanically hidden for non-logged-in customers. A brand new “Latest Exercise” part reveals customers their most up-to-date actions and operations. We additionally made enhancements to usability, efficiency, and total person expertise.
- New group accounts now begin on the Neighborhood plan by default, as an alternative of inheriting the person’s private plan. This transformation applies to customers on the Neighborhood, Important, and Skilled plans. Enterprise customers aren’t affected. The “Member Since” column now reveals when a member joined the group, and Settings pages are hidden from customers with out the required permissions.
- The billing part has been redesigned for a extra intuitive bank card administration expertise. We have added validation to stop duplicate card entries and assist for setting or altering the default bank card.
- The Python SDK now helps Pythonic fashions for a extra native expertise. We fastened failing checks to enhance stability. The CLI is now ~20x quicker for many operations, consists of config contexts, improved error messages, and corrected return arguments within the mannequin builder. Be taught extra right here.
Prepared to begin constructing?
With this Python-first launch, importing and working inference in your customized fashions is now quicker, less complicated, and extra intuitive than ever. Whether or not you are integrating a pre-trained mannequin or deploying one you have constructed from scratch, the Clarifai Python SDK offers you the instruments to maneuver from prototype to manufacturing with minimal overhead.
Discover the documentation and begin constructing at present.
