We’ve introduced Group Relative Policy Optimization (GRPO) Fine-Tuning in SeekrFlow, a powerful reinforcement learning technique that unlocks advanced reasoning capabilities in Large Language Models (LLMs). GRPO fine-tuning transforms models from passive information retrievers into active problem-solvers, capable of handling complex, verifiable tasks with greater precision and reliability.
This method is effective in domains where answers can be definitively validated, such as mathematics, coding, and other structured problem-solving scenarios. GRPO training follows a similar process to standard fine-tuning, with a few targeted modifications, and is now fully supported in SeekrFlow.
You can now define structured response formats for SeekrFlow™ agents, available through the API and SDK. This capability allows developers to enforce specific schemas for agent outputs, ensuring integration-ready responses from the first generation. By eliminating the need for post-processing or custom parsers, structured outputs simplify development and enable more reliable downstream workflows
We’ve launched one of the biggest SeekrFlow updates to date: a redesigned UI that makes agent creation and experimentation more accessible, and intuitive.
Agents for Everyone: You can now create, configure, and chat with agents directly in the UI.
Guided forms simplify model, tool, and policy selection.
Built-in conversation scaffolding means no manual thread setup.
Test agents instantly in the new Agent Chat sandbox.
New SeekrFlow Dashboard::
Quick-Start tiles let you jump directly into creation: AI-Ready Data, Fine-Tune, Create an Agent, and Playground.
Tabbed Recent Work view for Fine-Tuning and Agents, complete with status badges.
Deployment snapshot panel showing current deployment counts and live token usage meters.
UI Enhancements:
A complete visual reskin for a modern, polished look.
Reorganized left navigation aligned to real workflows:
Data Engine: New Storage section for managing files and vector stores
Development: Fine-Tuning, Agents, Deployments, and Model Library
Playground
Refreshed layout and graphics to improve clarity and usability.
Inline “Learn More” links for quick, contextual guidance.
This update makes SeekrFlow more accessible to both technical and non-technical users, enabling fast agent prototyping and evaluation straight from the browser. The new dashboard supports faster onboarding, agent-first workflows, and one-screen deployment health checks. It also lays the foundation for future platform capabilities.
Minor Bug Fixes & Stability Improvements
This release includes a number of behind-the-scenes updates, bug fixes, performance tuning, and workflow stability improvements to ensure a smoother, more reliable experience across SeekrFlow.
Today, we’re launching SeekrFlow™ Agents, a full-stack solution for building and deploying intelligent agents that operate autonomously, securely, and at scale. With SeekrFlow Agents, enterprises can deploy agents that are secure, explainable, and optimized for their specific tasks and industry. With SeekrFlow, you get the flexibility to run agents across cloud and on-premises—delivering the infrastructure and control needed to embed agentic AI into real-world workflows.
Seekr has signed a multi-year agreement with Oracle Cloud Infrastructure (OCI) to accelerate AI deployments and support the development of next-gen models and agents. This collaboration brings together Seekr’s secure AI platform, OCI’s high-performance infrastructure, and AMD Instinct™ MI300X GPUs to enable faster model training, scalable deployment, and support for mission-critical environments.
We’re excited to announce that SeekrFlow™, is now available on AWS Marketplace. This listing gives AWS customers a streamlined way to deploy SeekrFlow directly within their environments, accelerating AI adoption while maintaining full control over security, compliance, and procurement.
We’ve made several updates to improve platform reliability and user experience. This includes enhanced model cards that now display model-specific information, and updated links to our Privacy Policy and Terms & Conditions to ensure greater transparency. These refinements, along with ongoing stability and performance enhancements, help ensure a smoother and more trustworthy workflow across SeekrFlow.
Seekr now supports MFA for all user sign-ins. After entering their credentials, users are guided to select and complete a second-factor verification via:
TOTP (Time-based one-time password) through authenticator apps
SMS one-time codes
Email one-time codes
This adaptive login flow automatically directs users to their configured method (or lets them choose if multiple factors are set up), ensuring a seamless yet secure sign-in experience.
In-App Help & Support Access
We have made it easier for users to access help right when they need it. A new “Help” label now appears next to the question icon in the top navigation bar.
Editable Project Titles and Descriptions
We have introduced inline editing for project titles and descriptions on the Project Details page, making it faster and easier to manage key project information.
Titles can now be updated inline and are limited to 100 characters.
Descriptions support multiline editing and are capped at 1,000 characters.
These updates provide a more flexible and user-friendly way to keep project details accurate and up to date.
Today, we’re introducing multi-file ingestion, a new feature within SeekrFlow™. This capability simplifies and accelerates the process of automatic generation of a fine-tuning dataset. With multi-file ingestion, you can now upload a set of files across multiple formats, all in one step. SeekrFlow automatically converts, merges, and structures the content into markdown (.md) that is AI-ready, eliminating the need for manual formatting or stitching files together.
We have implemented several small bug fixes and performance enhancements to improve the data creation workflow, including updated instructional copy, clearer conversion progress indicators, refined exit warnings, and a more streamlined experience from upload to completion.
We have launched the Seekr AI-Ready Data Engine, a powerful new system within the SeekrFlow™ platform that transforms diverse enterprise data into a structured, AI-ready format—faster, more accurately, and at a lower cost. This intelligent, end-to-end pipeline eliminates manual bottlenecks, ensuring seamless integration and deployment of high-quality, trustworthy AI applications.
Users can generate their own API keys directly from their profile. This enhancement streamlines access management and improves user autonomy and security, ensuring a seamless developer experience.
How to create your API Key
Users can generate an API key by clicking the “+ Generate Key” button in their profile.
The system will securely generate and return a unique API key.
Once generated, users will be prompted to save their key before exiting.
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Improvements & Bug Fixes
Updated User Profile Page with Navigation
The SeekrFlow user profile page has been redesigned with a new sub-navigation component, improving accessibility and ease of use. The page is now labeled "My Account" and includes two sections:
Profile: which serves as the default landing page
API Key: where users can generate and manage their API access.
This update provides a more intuitive way to navigate account settings.
Bug Fixes and Stability Improvements
We have implemented several small bug fixes and performance enhancements to further optimize SeekrFlow. These updates improve the platform’s overall stability and ensure a smoother, more reliable experience for users.
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UI/UX Enhancements
Improved User Profile Experience
Enhanced the User Profile experience with a new User Details section, including profile editing, password updates, sign-out functionality, and cookie settings management.
We have added a dynamic training loss chart to the Run Details page, providing users with critical insights into model performance during fine-tuning:
Y-Axis: Displays loss values to indicate model performance trends.
Upper X-Axis: Shows the number of epochs selected during run creation.
Lower X-Axis: Represents "steps," calculated as:
Total Steps = (Total Number of Samples ÷ number of instances * Batch Size) × Number of Epochs
This new feature enables users to track loss convergence and make informed adjustments, reducing the number of repeated fine-tuning cycles needed to optimize results. By minimizing fine-tuning runs, it saves time, compute resources, and associated costs.
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Improvements & Bug Fixes
Enhanced Data Ingestion
Users can now upload Markdown, PDF, and Word files as valid file types for data ingestion during data creation. This update, available on API and SDK, makes working with diverse data formats easier, streamlining the ingestion process and reducing the need for file conversions.
Expanded Base Model Library
The base model library now includes the Mistral-7B-Instruct-v0.2 model from Mistral AI. This model is available for fine-tuning, inference, and testing in the Sandbox, offering more options to tailor your AI solutions.
Bug Fixes and Stability Improvements
Small bug fixes and performance updates to make SeekrFlow™ smoother and more reliable for users.
We’ve improved the user experience in the Model Library. Icon buttons in base model cards now display a hover tooltip with descriptive text, ensuring users clearly understand the available actions.
Faster Response Rendering in Sandbox
The simulated typing animation for sandbox responses now matches the speed of token count updates. This enhancement delivers quicker rendering, offering a more seamless and accurate representation of response generation.
Run Description Added to Run Summary
The run summary now displays the description entered during the run creation wizard, enhancing clarity and alignment with the design specifications.
Minor Bug Fixes and Stability Improvements
This release includes several minor bug fixes and performance enhancements across SeekrFlow™ to ensure a smoother and more reliable user experience.
November’s release introduces new features designed to give users greater control and efficiency over their AI workflows—from optimizing model outputs to managing large-scale deployments. For more details, read our full release blog.
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New Features
Sandbox Input Parameters
We have added new input parameters to the Sandbox environment: Temperature, Top P, and Max Tokens. These options give more control over inference outputs, allowing users tailor responses for specific tasks and use cases.
Enhanced Inference Engine for Faster Inference
With the integration of vLLM, inference speeds have dramatically improved, making your AI workflows faster and more efficient. This integration ensures that even complex models deliver faster results, helping users achieve more in less time.
We conducted performance testing to compare TGI and vLLM on Intel Gaudi2 accelerators.
TGI: tgi-gaudi v2.0.4
vLLM: vllm-fork v0.5.3.post1-Gaudi-1.17.0
On average, the enhanced inference engine performed 32% faster when compared to TGI for the RAG experiments (full interaction traces, 10 concurrent requests) using Meta-Llama-3.1-8B-Instruct
Significant latency improvements in load testing with 100 concurrent users:
Meta-Llama-3-8B-Instruct: ~45% faster
Meta-Llama-3.1-8B-Instruct: ~39% faster
Enhanced OpenAI compatibility features
Our inference engine now seamlessly integrates with OpenAI’s ecosystem, expanding workflow capabilities and enhancing usability.
Log Probabilities: New support for log_probs and top_logprobs, providing insights into model decision-making, aiding debugging, and improving output accuracy.
Dynamic Tool Calling: Custom functions can now be automatically invoked by the model based on context, streamlining business logic integration.
Try it yourself!
The example code below shows you how to leverage the OpenAI client and SeekrFlow's inference engine to create a custom unit conversion tool that can be configured dynamically.
Create the client and make an API request
import os
import openai
# Set the API key
os.environ["OPENAI_API_KEY"] = "Paste your API key here"
# Create the OpenAI client and retrieve the API key.
client = openai.OpenAI(
base_url="https://flow.seekr.com/v1/inference",
api_key=os.environ.get("OPENAI_API_KEY"
)
# Send a request to the OpenAI API to leverage the specified Llama model as a unit conversion tool.
response = client.chat.completions.create(
model="meta-llama/Llama-3.1-8B-Instruct",
stream=False,
messages=[{
"role": "user",
"content": "Convert from 5 kilometers to miles"
}],
max_tokens=100,
tools=[{
"type": "function",
"function": {
"name": "convert_units",
"description": "Convert between different units of measurement",
"parameters": {
"type": "object",
"properties": {
"value": {"type": "number"},
"from_unit": {"type": "string"},
"to_unit": {"type": "string"}
},
"required": ["value", "from_unit", "to_unit"]
}
}
}]
)
Register a function from JSON
Next, define and register a Python function from JSON data.
This function executes the tool call, given an LLM response object.
# Execute our tool
def execute_tool_call(resp):
tool_call = resp.choices[0].message.tool_calls[0]
func_name = tool_call.function.name
args = tool_call.function.arguments
func = globals().get(func_name)
if not func:
raise ValueError(f"Function {func_name} not found")
if isinstance(args, str):
import json
args = json.loads(args)
return func(**args)
execute_tool_call(response)
Sample output
This is the output expected in response to the request made earlier to convert 5 kilometers to miles.
3.106855
Federated Login with Intel
Intel® Tiber™ AI Cloud users can now access SeekrFlow™ with a new federated login feature
First-time users: Start by using your Intel® Tiber™ AI Cloud credentials, which will auto-populate the sign-up form for quick and easy access to SeekrFlow.
Returning users: Simply log in with your Intel® Tiber™ AI Cloud credentials for direct access to SeekrFlow.
This integration simplifies user management and access for those connected to Intel® Tiber™ AI Cloud.
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Improvements & Bug Fixes
Streaming in Sandbox
We have enabled streaming for chat responses in the Sandbox, delivering results incrementally so users can utilize results without a delay.
Clear and Restart Button Fix
We have resolved an issue where the “Clear and Restart” button in Sandbox didn’t function. A dialog box now appears, confirming that chat history will be cleared, allowing you to start fresh.
Dataset Directory Update
Uploaded file improvements in the Create Run Modal
Successfully uploaded files immediately appear in the dataset directory.
Switching to the directory view auto-selects the newly uploaded file.
Radio buttons now remain active, ensuring smooth file selection.
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UI/UX Enhancements
We have made several updates to improve the user experience and provide clearer guidance across the platform
Sandbox: Updated language to better support the new model parameter settings for improved clarity.
Deployment Dashboard: Enhanced explanations of cost transparency features for better understanding of resource usage.
Projects: Cancellation dialogs now show detailed cost information related to token usage, offering users more visibility into their resource consumption.
These updates aim to make SeekrFlow’s interface more intuitive and user-friendly, enhancing navigation and overall clarity.
