* studio: extract param count from model name as fallback
When HuggingFace API doesn't return totalParams for a model,
extract the param count from the model name (e.g. "Qwen3-0.6B"
-> "0.6B", "Llama-3.2-1B-Instruct" -> "1B"). Applied to both
the recommended list and HF search results.
* studio: read GGUF context_length via fast header parser, set max tokens
- Fast GGUF metadata reader (~30-55ms) parses only KV header, skips
tensor data and large arrays (tokenizer vocab etc)
- Extracts context_length and chat_template from GGUF metadata
- Returns context_length in LoadResponse for frontend to use
- Frontend sets maxTokens to actual context_length for GGUFs (e.g.
262144 for Qwen3.5-9B, 131072 for Qwen2.5-7B)
- Max Tokens slider shows "Max" and is locked for GGUFs
- Auto-load path also uses actual context_length from load response
- Toast auto-dismiss (5s) and close button for auto-load toast
* studio: GGUF TTS audio support (from PR #4318)
Add GGUF TTS audio generation via llama-server. When a GGUF model
loads, the backend probes its vocabulary to detect audio codecs
(SNAC/BiCodec/DAC/CSM/Whisper). If detected, the codec is pre-loaded
and the model is reported as audio to the frontend.
During chat, TTS models route to the audio generation path which sends
a per-codec prompt to llama-server's /completion endpoint, extracts
generated tokens/text, and decodes to WAV using AudioCodecManager.
Also strips base64 audio data from prior assistant messages to prevent
context overflow.
Co-authored-by: Manan Shah <mananshah511@gmail.com>
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* Remove package-lock.json from tracking
* studio: per-model inference defaults, GGUF max tokens fix, reasoning toggle
- Add inference_defaults.json with per-model-family sampling parameters
for ~50 families (Qwen3.5, Qwen3, Gemma-3, Llama-3, DeepSeek, etc.).
Values sourced from unslothai/docs and Ollama params blobs.
- Family-based lookup in inference_config.py: extracts model family from
identifier, matches against patterns (longest match first), merges with
priority: model-specific YAML > family JSON > default.yaml.
- Fix GGUF Max Tokens slider locked at "Max": store ggufContextLength
separately from maxTokens so the slider is adjustable (step=64).
- Fix Ministral YAML: top_p was literal string "default", now 0.95.
- Add reasoning toggle for thinking models (Qwen3.5, Qwen3, DeepSeek-R1,
DeepSeek-V3.1, etc.): detect enable_thinking support from GGUF chat
template metadata, pass --jinja to llama-server, send
chat_template_kwargs per-request. Frontend shows "Reasoning is ON/OFF"
pill button next to attachment button in composer.
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* studio: remove default system prompt injection
Backend was injecting "You are a helpful AI assistant." when no system
prompt was provided. Neither unslothai/docs nor Ollama specify a default
system prompt for most models. Now defaults to empty string, letting the
model's own chat template handle system behavior.
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* studio: use lightbulb icons and "Think" label for reasoning toggle
Lightbulb on when thinking enabled, lightbulb-off when disabled.
Label is just "Think" in both states; grayed out styling when off.
* studio: fix HTML file upload breaking chat
Replace SimpleTextAttachmentAdapter with custom TextAttachmentAdapter
(excludes text/html) and HtmlAttachmentAdapter that strips tags via
DOMParser, removing scripts/styles and extracting readable text content
instead of dumping raw HTML markup into the conversation.
* studio: show chat template in Configuration panel
Display the model's Jinja2 chat template in a new "Chat Template"
section under Settings (now open by default). For GGUFs, reads from
GGUF metadata; for safetensors, reads from tokenizer.chat_template.
Template is editable with a "Restore default chat template" button
that appears when modified. Section only shows when a model with a
chat template is loaded.
* studio: editable chat template with Apply & Reload
Chat template section now functional:
- Editing the template shows "Apply & Reload" (reloads model with
custom template) and "Revert changes" buttons
- For GGUFs: writes template to temp .jinja file, passes
--chat-template-file to llama-server on reload
- For non-GGUF: passes chat_template_override in load request
- Settings section now open by default
- selectModel supports forceReload to reload same model
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* studio: fix DeepSeek reasoning detection and auto-load metadata
- Set _model_identifier before _read_gguf_metadata so DeepSeek
"thinking" template detection works (was always None before)
- Populate ggufContextLength, supportsReasoning, reasoningEnabled,
defaultChatTemplate in autoLoadSmallestModel GGUF path
* studio: add spacing before BETA badge in navbar
Add gap-1.5 on the logo Link container to space the BETA label
from the wordmark.
Co-authored-by: Imagineer99 <Imagineer99@users.noreply.github.com>
* studio: vertically center BETA badge with logo
---------
Co-authored-by: Manan Shah <mananshah511@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Imagineer99 <Imagineer99@users.noreply.github.com>
* Strip <think> blocks from LLM assist model output
* Add debug logging for raw LLM assist output
* Quiet llama-server logs, use structlog in llm_assist
* Fix think-tag stripping when response is inside tags
* Remove debug logging of raw model output
* Clarify GGUF download logs: show cache hit vs actual download
* Clarify heuristic-detected mapping in UI text
* Default helper model to Qwen3-4B-Instruct-2507 UD-Q4_K_XL
* Remove package-lock.json from tracking, add to .gitignore
* Auto-open mapping dialog on Start Training for custom_heuristic format
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* Use last think block when extracting inner content (review feedback)
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- GGUF: use -c 0 for model's native context size (no 4096 cap)
- GGUF: hide Max Seq Length slider (irrelevant), set Max Tokens to Max
- Non-GGUF: default Max Tokens to 4096
- Max Tokens slider shows "Max" label when at ceiling for GGUFs
- Run non-GGUF load_model in asyncio.to_thread for progress polling
- Auto-load smallest downloaded model when chatting without selection
- Wait for in-progress model load before inference (modelLoading store flag)
- Recommended list: 4 GGUFs + 4 hub models after case-insensitive dedup
- Model selector waits for cached data before rendering
- Toast close button repositioned, Sampling section open by default
- Add logging to _get_repo_size_cached exception handler
- Use -c 0 for llama-server (model's native context size, no 4096 cap)
- Run non-GGUF backend.load_model in asyncio.to_thread for progress polling
- Auto-load smallest downloaded model when user chats without selecting one
- Wait for in-progress model load before inference (no "No model loaded" error)
- Add modelLoading flag to zustand store for cross-component coordination
- Dynamic top models: send 8 GGUFs + 8 hub models, frontend caps 4+4 after dedup
- Case-insensitive dedup: downloaded models correctly hide from recommended list
- Prevent duplicate toasts: guard against double selectModel calls
- Model selector waits for cached data before rendering (no empty flash)
- Toast close button positioned at top-right with proper spacing
- Sampling section expanded by default in chat settings
- Global toast close button styling fix
Change all repetition_penalty defaults from 1.1 (or 1.05/1.2 in
presets) to 1.0 across the entire backend and frontend. Most models
handle repetition well on their own and a non-1.0 penalty can degrade
output quality, especially for code, structured output, and creative
tasks.
Files changed:
- Backend: inference.py, llama_cpp.py, orchestrator.py, worker.py,
models/inference.py (Field defaults)
- Frontend: chat-settings-sheet.tsx (Creative/Precise presets),
runtime-provider.tsx (auto-title generation)
- maxTokens: 2048 -> 8192. The old 2048 limit caused generation to
stop mid-output for longer responses (e.g. reasoning/thinking models
that produce long chain-of-thought before the answer).
- repetitionPenalty: 1.1 -> 1.0 (disabled). Most models handle
repetition well on their own. A penalty of 1.1 can hurt quality
for creative tasks like code generation and ASCII art.
- Change welcome message from "Run LLMs or test your fine-tune" to
"Chat with your model".
Merge the toast UX refactor from PR #4304 (by @Shine1i):
- Toast duration 5s default with close button (X) for manual dismiss
- Inline progress bar component (ModelLoadInlineStatus) shown in the
header after toast is dismissed
- Model switch warning only for image compatibility (not generic)
- activeThreadId tracked in store via ActiveThreadSync
- Loading state cleanup via resetLoadingUi helper
- Toast uses Infinity duration during loading with onDismiss handler
Re-applied non-GGUF download progress additions on top:
- getDownloadProgress for all models (not just GGUF)
- hasShownProgress flag, loadingModelRef race condition checks
- First poll at 500ms, bytes-only fallback when expected size unknown
Don't show "Model changed for this chat" toast when the thread has
no messages. On a fresh page load with a stale thread from a previous
session, this warning is confusing. The warning is only useful
mid-conversation to alert about image compatibility with the new model.
When messages.length === 0, silently update the thread's modelId and
proceed with loading.
- Add sloth emoji prefix to "Downloaded" and "Recommended" section
labels in the Hub model picker so they are visually distinct.
- Replace browser network errors ("NetworkError when attempting to
fetch resource" / "Failed to fetch") with a clearer message:
"Studio isn't running -- please relaunch it."
1. Backend: When a model fails with "No config file found" or similar
unsupported-model errors, wrap the message with "This model is not
supported yet. Try a different model." instead of showing the raw
Unsloth exception.
2. Frontend: Compute estimated download size from the HF search API's
safetensors.parameters dtype breakdown (BF16=2B/param, I32=4B/param,
F32=4B/param, etc.) and show it in the model picker instead of just
the param count. For example, Kimi-K2.5 now shows "~554 GB" instead
of "171B" (which was misleading since 171B params != 171GB download).
Three fixes on top of the download progress feature:
1. Backend: Replace broken "no .incomplete = done" completion check
with a 95% byte threshold. HF downloads files sequentially, so
between files there are briefly no .incomplete files even though
the download is far from done (e.g. Kimi-K2.5 reported "done"
after downloading 22KB of config files out of 595GB).
2. Frontend: Track hasShownProgress flag. Only show "Download
complete. Loading into memory..." if we actually displayed
download progress before. For already-cached models where the
first poll returns progress=1.0, this avoids the misleading
"Download complete" message.
3. Frontend: Deduplicate recommended vs downloaded -- filter out
models already in the "Downloaded" section. Cache the fetched
lists at module level so re-mounting the popover does not flash
an empty "Downloaded" section.
Previously only GGUF models showed download progress in Chat. Non-GGUF
models (safetensors, bnb quantized, etc.) showed a static message with
no progress indication. This adds progress tracking for all model types
and fixes several related issues.
Backend:
- Add /api/models/download-progress endpoint that checks the HF cache
blobs directory for completed and .incomplete files. Uses model_info()
(cached per repo) to determine expected total size for percentage.
- Add /api/models/cached-models endpoint that lists non-GGUF model repos
from the HF cache via scan_cache_dir().
- Fix progress stuck at 0.99: when no .incomplete files remain, report
1.0 immediately (blob deduplication can make byte totals mismatch).
Frontend:
- Remove the ggufVariant gate so download progress polling works for all
non-cached models, not just GGUFs.
- Use GGUF-specific endpoint when variant + expectedBytes available,
otherwise use the general download-progress endpoint.
- Fix toast stuck after load: check loadingModelRef.current before and
after the async poll to prevent overwriting the success toast.
- First poll at 500ms instead of waiting for the 2s interval.
- Show downloaded non-GGUF models in the Hub model picker "Downloaded"
section alongside GGUFs.
* user can upload eval dataset, removed bugs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* resolving merge conflicts
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* resolving gpt comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
* feat(studio): switch to password-only login and simplify first-time setup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: align change-password button state with validation rules
---------
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Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
* chat only with gguf for mac devices
* resolving gpt comments
* add change-password for chat only
* hide lora adaptors dropdown
* solving gpt comments
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* addressing the comment
* fixing auth flow
---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Run GGUF load_model in asyncio.to_thread so the event loop stays free
for progress polling during download (was blocking all requests).
- Extract download phase out of the lock in LlamaCppBackend.load_model
so unload_model/cancel can take effect immediately during download.
- Fix "downloaded" badge for split GGUFs: check total cached bytes
across all shards vs expected size, not just first shard existence.
- Respect CUDA_VISIBLE_DEVICES in /api/system GPU reporting so the
frontend GGUF fit estimation uses actual available VRAM.
- Sort tight variants (need CPU offload) smallest-first instead of
largest-first -- closer to GPU budget = faster inference.
- Fix cancel: use refs instead of React state for abort controller and
toast ID so both cancel buttons (text + toast) work reliably. Make
cancel synchronous (fire-and-forget unload) for instant UI response.
Check abortCtrl.signal.aborted after loadModel returns to prevent
ghost model state. Skip rollback and suppress errors on cancel.
- Dynamic top 4 GGUF models fetched from HF API sorted by downloads,
prepended to the default recommended list.
- Remove turnAnchor="top" for auto-scroll to bottom during generation.
- Set default toast duration to 10s (was infinite for loading toasts).
- Deduplicate cached GGUF repos using scan_cache_dir API (fixes
Qwen/X-GGUF vs qwen/x-gguf duplicates from lowercased HF cache).
- Pre-compile repo_id validation regex to silence CodeQL ReDoS warning.
- Change welcome text and default suggestion text.
1. Progress endpoint now takes a variant parameter and only counts
.gguf files matching that variant (not all files in the repo cache,
which would include previously downloaded variants)
2. Tracks .incomplete files in HF blobs dir for in-progress single-shard
downloads, capping at 99% until the file is fully committed
3. Fixed loading text: "Loading model..." for cached, "Downloading
model..." for new downloads, with appropriate descriptions
4. Wording: "Downloading and loading model. Large models can take a
while." instead of "This may include downloading."
1. Loading text: shows "Loading model..." for cached models,
"Downloading model..." for new downloads. Toast description
adapts accordingly.
2. Download progress: polls /api/models/gguf-download-progress every
2s during downloads, updating the toast with percentage and GB
downloaded. Progress is estimated by checking the HF cache folder
size against the expected total bytes.
3. Passes isDownloaded and expectedBytes through the full chain from
variant click to selectModel for accurate UI state.
Downloaded variants now take priority over the recommended badge in
sort order. Within the same tier (downloaded+fits, etc.), recommended
still sorts first. Order: downloaded -> recommended -> fits -> tight -> OOM
- Backend: /gguf-variants now checks HF cache for each variant's file
and returns a downloaded flag per variant
- Frontend: downloaded variants sort before non-downloaded (after
recommended), and show a green "downloaded" badge
- Sort order: recommended -> downloaded+fits -> downloaded+tight ->
fits -> tight -> OOM
1. Interruptible downloads: load_model now checks a cancel event
between shard downloads. unload_model sets the event so cancel
stops the download at the next shard boundary.
2. /api/models/cached-gguf endpoint: scans the HF cache for
already-downloaded GGUF repos with their total size and cache path.
3. "Downloaded" section in Hub model picker: shows cached GGUF repos
at the top (before Recommended) so users can quickly re-load
previously downloaded models without re-downloading.
Replace toast.promise with a manual toast.loading that includes a
Cancel action button. Users can now cancel model downloads/loads from
the toast notification itself, not just from the header bar spinner.
Updated GGUF fit classification to match llama-server's --fit behavior:
- fits: model <= 70% of total GPU memory (all GPUs)
- tight: model > 70% GPU but <= 70% GPU + 70% available system RAM
(llama-server uses --fit to offload layers to CPU)
- OOM: model exceeds both GPU and system RAM budgets
useGpuInfo now also returns systemRamAvailableGb from /api/system so the
frontend can compute the combined GPU+RAM budget.
Two fixes for accurate GGUF OOM detection:
1. /api/system now uses nvidia-smi to enumerate all physical GPUs
instead of torch.cuda which only sees CUDA_VISIBLE_DEVICES. This
matches llama-server which can use all GPUs regardless of the env
var. Falls back to torch-based detection if nvidia-smi unavailable.
2. Frontend GGUF OOM check now uses 70% of total GPU memory as the
budget, matching the PR's _select_gpus logic (30% reserved for KV
cache and compute buffers). Previously used checkVramFit's 100%
threshold which was too generous.
Adds a Cancel button next to the "Downloading model..." spinner so
users can abort long downloads. Clicking it aborts the in-flight load,
calls unloadModel to kill any running llama-server process, and clears
the loading state.
OOM variants are more useful sorted ascending by size since smaller ones
are more likely to run with --fit. Non-OOM variants remain largest-first
(best quality).
Two fixes for GGUF variant dropdown:
1. useGpuInfo now sums memory across all GPU devices instead of only
reading devices[0]. This matches llama-server's multi-GPU allocation
where models can be split across GPUs.
2. When the backend-recommended variant (e.g. UD-Q4_K_XL) exceeds total
GPU VRAM, the frontend picks the largest variant that fits instead.
If all variants are OOM, it recommends the smallest one (most likely
to work with --fit).
The useMemo for sortedVariants was placed after the loading/error early
returns, which violated React's rules of hooks (hooks must be called in
the same order every render). Move it before the conditional returns.
Fixes: Minified React error #310
Move the sort logic from the backend to the frontend GgufVariantExpander
component where GPU VRAM info is available. The backend now does a simple
size-descending sort. The frontend pins the recommended variant at the
top, pushes OOM variants to the bottom, and sorts the rest by file size
descending (largest/best quality first).
* miscallenous studio
* chore: upload dataset misc
* chore: redudancy studio cleanup
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* fix: adress the pr comments
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* fix: adress comments about recipes
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* fix: quotation marks
* diceware passphrase generation
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