Changelog

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🎯 New Features & Improvements

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Vision Language Models (VLMs)

• Enhanced VLM Fine-tuning Support: Improved support for Qwen2-VL and Qwen2.5-VL models with better memory management and batch size optimization
• OpenAI Dataset Format Support: Now supports OpenAI-compatible image dataset formats for easier migration and data preparation
• S3 URL Support: Direct support for training with S3 URLs, including both public and private image URLs

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Fine-tuning Enhancements

• Cost & Time Estimates: Show estimated training price and duration before starting fine-tuning jobs
• Better Dataset Validation: Enhanced validation of dataset files before starting training jobs to catch issues early

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Infrastructure & Platform

• H200 GPU Support: Added support for H200 GPUs for improved inference performance

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User Experience

• Homepage UI Refresh: New streamlined homepage design focused on inference workflows
• New User Onboarding: Added TODO list and guided workflows for new users getting started

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🐛 Bug Fixes

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Training & Fine-tuning

• Sequence Length Computation: Fixed incorrect sequence length calculation for vision models
• Batch Size Optimization: Corrected batch size tuning that was overestimating optimal batch sizes

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Deployment & Serving

• Vision Model Deployment: Resolved deployment failures for vision model examples in documentation

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UI & API

• Metrics Collection: Fixed missing deployment metrics in UI for VPC tenants

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📚 Documentation Updates

• Vision Fine-tuning Docs: Updated documentation for vision model fine-tuning workflows
• Dataset Format Conversion: Added guides for converting OpenAI-compatible image datasets to Predibase format
• Supported Models: Updated supported models list to include Qwen2-VL and Qwen2.5-VL models

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New Features 🎉

• Hide utilization graph, only show spec decoding graph if speculator enabled
• Classification head training support
• Bring back speculative decoding chart in UI
• Add num_iterations support
• Add a warning when all scores are 0 for a set of completions
• Add reward server logging about reward function score

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Bug Fixes 🐛

• Permissions checking for readonly user API tokens
• Remove duplicate pass over the example dict
• Send json data instead of url encoded data when creating connections from UI
• Cast additional input data to Python literals before passing it to the reward function
• Use CPU node for rewardserver when recreating
• Recreate services when retrying after backoff for compute unavailability

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New Features 🎉

• Initial deployment metrics frontend
• Initial deployment metrics backend
• Refresh GRPO logs tab upon new step or new epoch
• Reward graph refinements
• Add and delete reward functions in the UI
• Show placeholders when there is no reward or completions data
• Reward graphs cleanup and improvements
• Add train_steps as parameter in SDK
• Add step counter for GRPO jobs
• Collapse timeline by default when GRPO job is training

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Bug Fixes 🐛

• Fix deployment metric queries
• Use debounce to prevent ddos from refetching logs
• Fix invite link URLs returned by API
• Enable adapter download in UI
• GRPO multipod bugs around lora loading and unloading
• Only show edit/add/delete reward function buttons during training
• Load medusa weights from checkpoint separately
• Set max height on reward graph legends when legend takes up >30% of tile height
• Use right query key for reward functions query
• Stop Completions View from DDOSing server for long running GRPO jobs
• Make GRPO completions viewer data collection robust to restarts
• More reward graph refinements
• Limit to 2 concurrent batch jobs per tenant
• Log metrics once per step in GRPO and Turbo fine-tuning jobs
• Use better colors for reward graphs
• Show reward graphs even if GRPO job was cancelled

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Documentation 📝

• Fix mistral chat template
• Add new turbo example to docs
• Add QwQ back to docs
• Clarify that turbo doesn’t use LoRA parameters
• Add RFT pricing info in docs

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New Features 🎉

• Expose EMA slider for reward graphs
• Add synchronization to reward graphs
• Remove y-axis label from reward graphs
• Hide tabs when GRPO job is queued
• SDK function for getting config via adapters API
• Show total_reward and total_reward_std charts
• Rewards Function View
• Reward function graphs tab
• Add timestamps to deployment/GRPO logs views
• Add dropdown to deployment modal for configuring speculator options
• GRPO pod logs UI
• Automatic frontend proxy

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Bug Fixes 🐛

• Treat reward function version history as ascending
• Remove extraneous comma from completions comparison viewer
• Properly reuse old reward function info if no code has changed
• Add virtualenv to rewardserver deps
• Remove extraneous parameter from metrics push
• Do not push duplicate completions metadata
• Show all versions of the same reward function on the same graph
• Print GRPO metrics properly in SDK
• Don’t require strong types for pushing payload to metrics stream
• Remove inaccurate cost for VPC inference costs
• Skip normalizing the split column name if it is named “split”
• Don’t set base model in config when in continued training flow

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Documentation 📝

• Wordsmith GRPO UI usage docs
• Change order of fine-tuning pages, rename GRPO page to RFT and add info msg about tiers
• GRPO feature docs and UI links
• Add example instructions for updating reward functions via SDK
• Add apply_chat_template to finetuning config docs
• Improvements to GRPO training docs
• Change int to float in reward function docs

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New Features 🎉

• Add GRPO to Predibase

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Bug Fixes 🐛

• Allow uploaded adapters (which have no types) to pass through speculator validation
• Ignore missing servingParams in speculator selection during updates for private models
• Fix return_to URL routing
• Return lowercase literals for auto/disabled speculator, perform case-insensitive checks for create/update
• When preloading adapters, skip adapter status validation if falling back to medusa adapter as speculator
• Get base model name from local cache when continuing function calling training
• Refine the list of supported models for function calling
• If using a medusa adapter as the speculator, skip type validation
• Speculator param bug bash
• Extend timeout for post-preprocessing multi-GPU barrier
• VPC UI fixes
• Show “disabled”, not empty string for an empty speculator field in API responses
• Active batch jobs count against an independent limit from normal deployments

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Documentation 📝

• Update GRPO docs with new reward functions payload
• Add docs for GRPO
• Document new model@revision syntax for specifying base models
• Add deepseek to models list and extra info msg

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New Features 🎉

• Add top-level speculator param to deployment config
• Raise batch inference model size restriction to 32B
• Add Preloaded Adapters and Guaranteed Capacity to Create Deployment UI
• Add delete deployment button to config tab
• Show tooltip for prompting keyboard shortcut in prompt UI
• Add similar time ranges to Grafana for deployment monitoring

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Bug Fixes 🐛

• Don’t pass —adapter-source twice when using speculator param
• If a speculator is in use, set the container arg —adapter-source to pbase
• Return error if —adapter-id custom arg and speculator param are both active
• Add checkpoint UUID for batch inference to avoid transaction ID collision
• Pass both system and user API tokens to sidecar for batch inference
• Batch inference dataset type check for file uploads
• Distinguish activity names for fetching internal API token between modules
• Make internal API tokens user-level and use activity to pass to llm deployer
• Allow admin scope when validating user token
• Make created_by_user field of auth tokens nullable
• Move api token generation outside of batch deploy activity
• Properly handle vault’s return values for internal API token
• Automatically inject user API token into batch inference lorax container
• Remove extra slash in batch inference checkpoint endpoint
• Ensure default engines are assigned when moving user to new tenant
• Set is_batch flag when creating batch inference deployments
• Filter batch deployments in v2 API
• Clean up user signup workflow
• Increase size of work-volume for batch inference worker
• Add redirect after deleting deployment
• Adjust max_steps computation to account for multiple GPUs when fine-tuning with streaming data
• Callback fixes for multi-GPU training
• Clearer file and zip archive names for downloaded request logs
• Manually set cooldown period for HPA when configuring KEDA scaling
• Download adapters in the same tab (so correct headers are sent) instead of in new tab
• Restrict post-training logic to rank 0
• Leading zeros in S3 prefix formatting when searching for log files
• Update Auth0 Org and User connection validation
• Handle null reservations response in UI
• Tighten auth boundaries at the root of the app
• Separate Okta and Email Signin Buttons
• Use mutually exclusive conditionals around useNavigate hook

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Documentation 📝

• Update config docs with new parameters
• Fix update command usage on private_deployments page
• Various docs fixes around getting rid of custom args
• Batch inference user guide
• Add timeouts and retries section to inference overview
• Add quick motivating example to quickstart
• Fix tool example
• Add redirect from old end-to-end URL to new
• Some improvements to end-to-end example documentation
• Formatting improvements for function calling docs page

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New Features 🎉

• Make L4s available and fixes missing L40s
• Reservations Tab V0, small reorganization of settings module
• Default to lora adapter type when continuing training

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Bug Fixes 🐛

• Training timeline fixes
• Replace word “instances” with “replicas” in reservations UI
• Show L4 only for VPC
• Navigate calls should be inside useEffects
• Wait until environment is defined before deciding whether to redirect to VPC setup view
• Fixes Zapier Workflow issue
• Avoid swallowing errors when enqueuing metrics to posthog client
• Replace all react router Navigate component usage with useNavigate hook
• Ensure LLM auth policies are re-setup when tenant upgrades
• Clear invite token when user proceeds to existing tenant
• Metronome transaction ID conflicts for LLM deployments
• Allow readonly users to use the users/move endpoint
• Fetch accelerator properties from config when returning deployment metadata
• Hide new adapter version (normal training flow) option for turbo adapters
• Auto-select file uploads connection in new adapter view
• Fix and refine connection selector in Create Adapter View
• New adapter version from latest should use latest non-imported adapter
• Cast steps_per_epoch to int
• Delete parent model distributed logs when continuing training on the same dataset

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Documentation 📝

• Add docs for function calling
• Add l40s to accelerator
• Update colab positioning in docs
• Clarify vision docs

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New Features 🎉

• Track and return adapter output type for finetuning jobs
• Display adapter type on individual and list adapter page
• Turbo and Turbo LoRA batch size tuning support
• Add adapter filter to SDK function for request logs download
• Show rank, target modules, and base model for imported adapters
• Automatically infer base model when uploading adapters
• Track and return base version for continued finetuning jobs
• API to opt into/out of inference request logging

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Bug Fixes 🐛

• Default value for REQUEST_LOGGING_ENABLED env var
• Turbo UI - hide parameters form in continue mode until ready
• Bump unsloth version to enable loading local adapter weights
• Fix V2 account deletion endpoint
• Fix continued training parameter logic
• For continued adapters, only show learning rate if the parent and child datasets are different
• Pass invite token to anonymous auth boundary
• Don’t set request_logger_url on lorax container unless logging is enabled
• Account for repos where adapters are not yet written
• Manually set missing values in adapter type column up migration
• Multiple Turbo UI fixes
• Disable timeline polling for imported adapters
• Fix missing hoisted properties
• Set batch size to 1 when continuing training on a new dataset
• Display of imported adapter properties on version page
• Parsing of adapter config file for displaying imported adapter properties
• Time bounds passing for request log download
• Log key parsing for request log download
• Fetching base model name and conversion to shortname
• Load pretokenized batches on each worker instead of dispatching from rank 0
• Missing logic to pipe requestLoggingEnabled parameter through LLM deployment path

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Documentation 📝

• Update turbo lora adapter id load docs
• Add MLlama user guide for VLM training
• Open up all base model support for turbo lora and turbo
• Fix header for download_request_logs
• For request logs opt-in and download
• Remove specifying base model when uploading an adapter

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New Features 🎉

• Organization/Okta/SSO support in Auth0
• Allow users to click on archived adapters
• Enable continued training from imported adapters and with modified adapter type
• Expose usesGuaranteedCapacity in deployment config and on deployment object
• Add readonly user role and disable access to appropriate operations
• Move prompt UI’s tooltips to hover over labels/titles instead of over question mark icons
• Add support for Turbo adapters
• Enable DDP in Predibase

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Bug Fixes 🐛

• Fix operation blockers for readonly users and preload for tenant information in user identity DB call
• Fix Org ID nil pointers
• Fix V2 invite link bugs and Auth0 casing inconsistencies
• Refresh page when merging accounts to get new JWT
• Update Current User to handle empty roles
• Store invite token so users can validate email
• Use correct parameter name for adapter type
• Disable delete user UI and fix wording on Sign In page
• Exclusively update config_obj during fine-tuning setup
• Move llm policy to create new tenant flow
• Dangling readonly role UI fixes
• Monospaced font
• Disable turbo / turbo-lora adapter type validation in the gateway for HF models

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Documentation 📝

• Update pydantic model_json_schema()
• Add Llama-3.2 and Qwen-2.5 models to fine-tuning models page
• Consolidate two deployment models tables into one, add new models, and more

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New Features 🎉

• Implement chat format in Predibase
• Enable showing tensorboard with finetuning jobs
• Save lorax image tag as a new column in the deployments DB
• Disable update deployment button when deployment is not updatable
• Add TRAIN/EVALUATION split check
• Add support for fine-tuning VLMs in Predibase

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Bug Fixes 🐛

• Add adaptive tag to h100 engine
• Remove possibly spurious error raise param in copy_within_bucket
• Default value for checkpoint_step param in update_trainer_settings activity
• Disable update deployment button popup when updatable
• Replace usage of overloaded deployment model name param with new model path param
• Avoid resetting engine during failure cleanup if it was never started
• Another incorrect reference to target instead of base version
• Avoid using protocol prefix when doing itemized model file copy
• Additional migration logic fixes for continued training
• Render turbo lora message in adapter builder correctly
• Correctly read checkpoint from base version
• Runtime issues in finetuning flow related to new checkpoint code
• Fixed reading image as bytes from parquet file
• Fix dayjs plugin errors with vite
• Fix vite production build
• Improve model matching logic (replace periods with dashes in mod…)

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Documentation 📝

• Small R17 docs updates
• Update docs
• Messages docs
• Add docs and expose param for showing tensorboard
• Add docs for apply chat template
• Update docs for continuing to train from arbitrary checkpoint
• Update Comet integration page and add force_bare_client=True
• Moved headers into REST API section

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New Features 🎉

• Add solar pro instruct model
• Add convenience method for getting openai-compatible endpoint URL for deployments
• Add deployment update and get_recommended_config functions, expose quantization on deployment object

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Bug Fixes 🐛

• Mark quantization optional on deployment object

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Documentation 📝

• Improve SDK docs
• Don’t use snake case for lorax args

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New Features 🎉

• Add guardrails for continued training with new dataset case
• Load adapter weights from checkpoint without additional state
• Add custom_args, Quantization and Update Deployment to UI
• Ingest arrow datasets
• Add support for dataset streaming for large, pretokenized datasets
• Added frontend for Comet API key integration
• Add support for Comet experiment tracking integration
• Add Phi-3.5 model to Predibase
• Invoke resume state setup activities in FinetuningWorkflow
• Add tooltip on guaranteed deployment chip
• Guaranteed deployment chip in UI
• Adding Mistral Nemo models
• Add latest gpt-4o model for augmentation
• Reveal GPU utilization metrics to all, nowhere to hide
• Enable turbo lora for solar-mini

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Bug Fixes 🐛

• Fix issue during synthesis with MoA
• Set adapter weights path on config rather than config_obj
• Update catch-all error message to be more general
• Fix nil pointer and description usage in update deployment handler
• Various fixes for continued training
• Set base_model correctly in metadata when continuing training, also additional guardrails
• Use fully resolved model name when creating a deployment
• Handle 404s in the Prompt UI
• Minor fixes for checkpoint resume
• Parameter passing to new continued training migration activities
• Remove unnecessary SDK-side default value for enable_early_stopping parameter
• Fix several bugs found in Engines and Modal Versions pages
• Pydantic model for Adapter versions to have correct tag and checkpoint types
• Guard import of HF transformers from the critical SDK path
• Don’t swallow error when loading specific checkpoint
• Model Version page loads again
• Fix API call to set default engines in UI
• Restart fine-tuning engine pods when the nvidia driver is not loaded

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New Features 🎉

• Add new simple augmentation strategy
• Validate WandB key in the client and in fine-tuning job
• Allow users to provide a name for generated datasets

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Bug Fixes 🐛

• Deployment health page labels: seconds not milliseconds
• Surface WandB token validation error message
• Point WandB token validation to validate_wandb_token
• Fix training engines table crash (due to improper null handling in engine modals)
• Update the UI with the correct key when creating an LLM
• Multiple deployment health fixes

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Documentation 📝

• Add async create finetuning job
• Enable turbo lora support for Zephyr
• Add name parameter to augmentation docs

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New Features 🎉

• Add new create deployment params to the client
• Added delete adapter modal to the repo UI
• Deployment health tab, reorg of deployments module
• Update Auth Service to allow V2 Invite Token Validation
• Add User v2 API endpoints
• Terminal logs use monospace font
• Connect file and connect pandas dataframe calling parameters documentation update
• Update CORS to allow authorization header
• Clear React Query cache on signout

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Bug Fixes 🐛

• Set num_samples_to_generate in the augment data payload
• Multiple deployment health page fixes
• Small fixes for auth operations
• Fix Auth0 frontend configuration
• Fix login spam, change Auth0 signup link to go to signup form directly
• Pass OpenAI API key to MoA through the SDK
• Register augment_dataset
• Augment activity tests & fixes
• Augment SDK and endpoint fixes
• Fix audience claim in JWT and mock out validate token function
• Include dataset ID in upload workflow ID
• Disable pricing details in deployment view for all enterprise customers
• Timestamp fields in identity record
• Missing return in Authorize middleware
• API call failures due to undefined apiServer (caused by incorrect axios typings)
• Hide incorrect Min/Max Replica counts
• Infinite loop during pagination of GetCreditBalances
• Fix debouncing and error handling on valid filename check in file connector
• Fix thumbnails API call and dataset previewer rendering for object values
• Add Description to Credit Card authorization
• Fix datasets for connection API call (malformed string)

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Documentation 📝

• Synthetic data, SDK augment dataset and download dataset
• Add delete adapters page
• Update e2e guide and replicas defaults info and code snippets
• Fix reference
• Add llama-3.1 8B versions to docs
• Fix comma in e2e example and colab link styling for evaluations
• Fix qwen2 coming soon

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New Features 🎉

• Implement connecting a Pandas DataFrame as the Predibase Dataset
• Accept shared and private as deployment list filter types
• Add new Turbo LoRA fine-tuning support to the Predibase App and SDK
• Add dataset upload troubleshooting section
• Update Auth Service on Staging
• Add version list method to repo in SDK
• Allow download of solar models even for non-enterprise tenants
• Add wandb logging to fine-tuning engine
• Add support for qwen2
• Plumb WandB token into engine as env var
• Use batch tensor ops to speed up medusa computation
• Add Private Solar to Predibase
• Enable archiving and deleting adapter versions
• Train Medusa LoRA adapters in Predibase

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Bug Fixes 🐛

• Remove install package
• Improve prompt UI matching when “-dequantized”/“-quantized” or “-instruct”/“-it” could be in the model’s name
• Python 3.8 compatibility with Annotated
• Avoid linting error during SDK installation caused by pydantic field
• Clear learning curves store when websocket reconnects
• Disable train button in create adapter view for expired tenants
• Temporarily comment out L40S in accelerators.yaml
• Update prices for accelerators
• Syntax error in predibase-agent.yaml
• Typo in min_replicas for SDK DeploymentConfig
• Set default cooldown to 12 hours in SDK
• Resolve inconsistent case sensitivity for LLM config lookup in deployment path
• Make error nil on empty wandb vault value
• Fix missing learning rate in medusa loss logs
• Fix URL routing logic for old models
• Show prompt instructions in kebab menu whenever adapter path is available
• Docs link broken in main menu

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Documentation 📝

• Add warning about capacity
• Add archiving and unarchiving adapter docs
• Add notebooks
• Remove vpc trial language from docs
• Update docs
• Added an example tutorial and modified the Models page
• Add wandb