* feat(studio): add DoRA support to studio
* fix: added use_dora fast encoder LoraConfig and gated use_dora on AdapterMethod
* fix(studio) serverside normalization for use_dora=true - add note documenting use_dora is silently dropped on diffusion
* fix: dora button disabled on mac, add preflight guard on GGUF lora export, mismatch now correctly falls through to existing error instead of silently no-opping
* Studio: add dora to the WizardState LoRA variant union for consistency
* Reject --use_dora on the MLX (Apple Silicon) CLI path
---------
Co-authored-by: danielhanchen <unslothai@gmail.com>
* Scan auto_map for GGUF-only repo ids in the consent gate
The trust_remote_code consent gate treated any repo classified GGUF-only
(ships .gguf, no transformers-loadable weight) as having no remote code,
so _config_has_auto_map returned False even when a config declared an
auto_map and the repo shipped the referenced .py. The evaluator then
skipped the scan/fingerprint for that target entirely.
GGUF-inertness is a property of the loader, not the repo. A GGUF
selection loads via llama.cpp, which never reads config.json/auto_map,
and that case is already short-circuited upstream by the caller's
is_gguf check (the inference route skips the remote-code preflight for a
GGUF load). Every path that reaches this helper (export, training,
non-GGUF inference) loads through transformers/Unsloth from_pretrained,
which DOES import auto_map even for a repo that only ships .gguf weights:
the custom module runs before from_pretrained fails on the missing
transformers weights. The export path has no is_gguf guard and passes the
source straight to FastLanguageModel.from_pretrained(trust_remote_code=True),
so the in-helper GGUF skip let a repo with config.json (auto_map) +
modeling_x.py + only a .gguf run unreviewed code during export.
Drop the redundant repo-level GGUF short-circuit (and the now-unused
_is_gguf_repo helper). A direct .gguf file reference stays inert via
_is_direct_gguf_file_ref because that genuinely is a single-file llama.cpp
load; repo ids are always scanned. A GGUF repo whose auto_map ships no .py
still allows via the existing empty-code path, so legitimate GGUF loads
are unaffected (and GGUF inference never reaches this helper at all). Only
a repo that actually contains a .gguf can change behavior here; non-GGUF
repos (safetensors, MLX) are byte-identical before and after.
Update the GGUF auto_map test to expect a scan, and add two regression
tests: a GGUF-only repo shipping auto_map Python is scanned and blocked,
and a transformers-style repo (safetensors / MLX .npz) with auto_map stays
scanned and blocked.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Remove trust_remote_code config defaults; consent dialog is the only enabler
trust_remote_code is a per-load decision that must go through the remote-code
consent dialog, which scans the auto_map code and pins the exact version. Two
pre-set paths could still enable it without the user reviewing any code, and the
GGUF consent bypass rode one of them into the export flow:
- 4 model_defaults YAMLs shipped trust_remote_code: true (GLM-4.7-Flash,
Nemotron-3-Nano-30B-A3B, PaddleOCR-VL, ERNIE-4.5-VL).
- The frontend consent hook silently enabled trust_remote_code on a clean scan
whenever the caller flagged the model as needing it.
Remove every trust_remote_code key from the model_defaults YAMLs (the loaders
already default to False when the key is absent) and delete the frontend silent
auto-enable, so trust_remote_code is only turned on after the user approves the
scanned code in the dialog.
The three models that genuinely run custom code ship auto_map, which the consent
gate detects on its own via _config_has_auto_map, so the dialog still fires for
them in inference, training, and export (Nemotron is also re-granted by the
trusted-org auto-enable in the workers). GLM-4.7-Flash has no auto_map:
glm4_moe_lite is native in transformers 5.0+ and it loads with
trust_remote_code=False, so its YAML flag was a no-op.
Adds test_yaml_trust_remote_code_removed.py.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Drop YAML sections emptied by trust_remote_code removal
Removing trust_remote_code from a model YAML whose section had no other key
left a bare `inference:` header, which PyYAML parses as None;
load_inference_config() then does `model_config.get("inference", {}).get(...)`
and crashes on the None. Drop those now-empty section headers (24 model
defaults, all the `inference:` section) so callers fall back to family/default
inference params, which is the same result those models had before (their only
inference override was trust_remote_code).
Strengthens test_yaml_trust_remote_code_removed.py to forbid any empty/None
top-level section and to load the affected models' inference config end to end.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add sweep asserting every model YAML loads via training + inference paths
Loads all model_defaults YAMLs through load_model_defaults (training) and
load_inference_config (inference) with the exact .get() access patterns the
routes use, so a malformed/None section that crashes either loader is caught.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Assert ex-TRC auto_map models still surface the consent dialog
Removing the trust_remote_code YAML default must not suppress the dialog for the
models that genuinely run custom code. The dialog is driven by the repo's auto_map
(via preflight_remote_code_consent_for_targets -> _config_has_auto_map), not the YAML
flag, so Nemotron/PaddleOCR-VL/ERNIE-4.5-VL still require consent; GLM-4.7-Flash (no
auto_map) takes no dialog and loads natively. Mocks only the Hub config + .py reader.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Tighten comments in consent-gate changes
* Trim comments to be more succinct
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* 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>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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>
