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Merge pull request #259 from unslothai/feat/dataset-subsets-split

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Feat/dataset subsets split
This commit is contained in:
Roland Tannous 2026-02-25 15:27:12 +04:00 • committed by GitHub
commit cb3e4f2c26
10 changed files with 442 additions and 393 deletions
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129
studio/backend/utils/datasets/dataset_utils.py
View file

@ -357,48 +357,25 @@ def format_dataset(
conversations = []
num_examples = len(examples[list(examples.keys())[0]])
# NEW: Check if this is user-provided or auto-detected
is_user_provided = custom_format_mapping is not None # Passed explicitly
# Preserve non-mapped columns ONLY if auto-detected
preserved_columns = {}
if not is_user_provided: # Only preserve for auto-detection
all_columns = set(examples.keys())
mapped_columns = set(custom_mapping.keys())
non_mapped_columns = all_columns - mapped_columns
for col in non_mapped_columns:
preserved_columns[col] = examples[col]
# Preserve non-mapped columns
all_columns = set(examples.keys())
mapped_columns = set(custom_mapping.keys())
preserved_columns = {
col: examples[col]
for col in all_columns - mapped_columns
}
for i in range(num_examples):
convo = []
# Enforce standard role order
role_order = ['system', 'user', 'assistant']
for target_role in role_order:
for target_role in ['system', 'user', 'assistant']:
for col_name, role in custom_mapping.items():
if role == target_role and col_name in examples:
content = examples[col_name][i]
# NEW: Different behavior based on mapping source
if is_user_provided:
# User explicitly mapped this - always include even if empty
convo.append({"role": role, "content": str(content) if content else ""})
else:
# Auto-detected - skip empty (original behavior)
if content and str(content).strip():
convo.append({"role": role, "content": str(content)})
if content and str(content).strip():
convo.append({"role": role, "content": str(content)})
conversations.append(convo)
result = {"conversations": conversations}
# Only add preserved columns if auto-detected
if not is_user_provided:
result.update(preserved_columns)
return result
return {"conversations": conversations, **preserved_columns}
try:
@ -507,7 +484,7 @@ def format_dataset(
}
# CHATML MODE: Convert to ChatML
elif format_type in ["chatml", "conversational"]:
elif format_type in ["chatml", "conversational", "sharegpt"]:
if detected["format"] == "alpaca":
converted = convert_alpaca_to_chatml(dataset, batch_size, num_proc)
@ -556,36 +533,38 @@ def format_dataset(
else:
warnings.append(f"Unknown format, attempting standardization")
try:
standardized = standardize_chat_format(
dataset, tokenizer, aliases_for_system,
aliases_for_user, aliases_for_assistant,
batch_size, num_proc
)
return {
"dataset": standardized,
"detected_format": "unknown",
"final_format": f"chatml_{detected['chat_column']}",
"chat_column": detected["chat_column"],
"is_standardized": True,
"requires_manual_mapping": False,
"is_multimodal": multimodal_info["is_multimodal"],
"multimodal_info": multimodal_info,
"warnings": warnings
}
except Exception as e:
warnings.append(f"Standardization failed: {e}")
return {
"dataset": dataset,
"detected_format": "unknown",
"final_format": "unknown",
"chat_column": detected["chat_column"],
"is_standardized": False,
"requires_manual_mapping": True,
"is_multimodal": multimodal_info["is_multimodal"],
"multimodal_info": multimodal_info,
"warnings": warnings
}
if detected["chat_column"]:
try:
standardized = standardize_chat_format(
dataset, tokenizer, aliases_for_system,
aliases_for_user, aliases_for_assistant,
batch_size, num_proc
)
return {
"dataset": standardized,
"detected_format": "unknown",
"final_format": f"chatml_{detected['chat_column']}",
"chat_column": detected["chat_column"],
"is_standardized": True,
"requires_manual_mapping": False,
"is_multimodal": multimodal_info["is_multimodal"],
"multimodal_info": multimodal_info,
"warnings": warnings
}
except Exception as e:
warnings.append(f"Standardization failed: {e}")
return {
"dataset": dataset,
"detected_format": "unknown",
"final_format": "unknown",
"chat_column": detected["chat_column"],
"is_standardized": False,
"requires_manual_mapping": True,
"is_multimodal": multimodal_info["is_multimodal"],
"multimodal_info": multimodal_info,
"warnings": warnings
}
else:
raise ValueError(f"Unknown format_type: {format_type}")
@ -816,8 +795,10 @@ def format_and_template_dataset(
)
# Step 2: Apply chat template
if "gemma" in model_name.lower() and not dataset_info["is_multimodal"] and (format_type != "alpaca" or (format_type == "auto" and dataset_info["detected_format"] != "alpaca")):
print("remove_bos_prefix is true")
# Gemma emits a leading <bos> that must be stripped for text-only chatml/sharegpt.
is_alpaca = format_type == "alpaca" or (format_type == "auto" and dataset_info["detected_format"] == "alpaca")
is_gemma = "gemma" in model_name.lower()
if is_gemma and not dataset_info["is_multimodal"] and not is_alpaca:
remove_bos_prefix = True
template_result = apply_chat_template_to_dataset(
dataset_info=dataset_info,
@ -839,14 +820,24 @@ def format_and_template_dataset(
all_warnings = dataset_info.get("warnings", []) + template_result.get("warnings", [])
all_errors = template_result.get("errors", [])
# If format_dataset returned "unknown" but apply_chat_template rescued
# it via heuristic detection, update final_format to reflect reality.
final_format = dataset_info["final_format"]
requires_manual = dataset_info.get("requires_manual_mapping", False)
if final_format == "unknown" and template_result["success"]:
out_ds = template_result["dataset"]
if hasattr(out_ds, "column_names") and "text" in out_ds.column_names:
final_format = "chatml_conversations"
requires_manual = False
return {
"dataset": template_result["dataset"],
"detected_format": dataset_info["detected_format"],
"final_format": dataset_info["final_format"],
"final_format": final_format,
"chat_column": dataset_info.get("chat_column"),
"is_vlm": False, # This is LLM flow
"success": template_result["success"],
"requires_manual_mapping": dataset_info.get("requires_manual_mapping", False),
"requires_manual_mapping": requires_manual,
"warnings": all_warnings,
"errors": all_errors,
"summary": summary,

6
studio/frontend/src/features/onboarding/components/steps/dataset-step.tsx
View file

@ -75,6 +75,8 @@ export function DatasetStep() {
setDatasetSubset,
datasetSplit,
setDatasetSplit,
datasetEvalSplit,
setDatasetEvalSplit,
uploadedFile,
setUploadedFile,
} = useTrainingConfigStore(
@ -91,6 +93,8 @@ export function DatasetStep() {
setDatasetSubset: s.setDatasetSubset,
datasetSplit: s.datasetSplit,
setDatasetSplit: s.setDatasetSplit,
datasetEvalSplit: s.datasetEvalSplit,
setDatasetEvalSplit: s.setDatasetEvalSplit,
uploadedFile: s.uploadedFile,
setUploadedFile: s.setUploadedFile,
})),
@ -304,6 +308,8 @@ export function DatasetStep() {
setDatasetSubset={setDatasetSubset}
datasetSplit={datasetSplit}
setDatasetSplit={setDatasetSplit}
datasetEvalSplit={datasetEvalSplit}
setDatasetEvalSplit={setDatasetEvalSplit}
/>
</>
) : (

353
studio/frontend/src/features/studio/sections/dataset-section.tsx
View file

@ -1,5 +1,10 @@
import { SectionCard } from "@/components/section-card";
import { Button } from "@/components/ui/button";
import {
Collapsible,
CollapsibleContent,
CollapsibleTrigger,
} from "@/components/ui/collapsible";
import {
Combobox,
ComboboxContent,
@ -35,6 +40,7 @@ import {
useTrainingConfigStore,
} from "@/features/training";
import {
ArrowDown01Icon,
CloudUploadIcon,
Database02Icon,
FileAttachmentIcon,
@ -66,6 +72,8 @@ export function DatasetSection() {
setDatasetSubset,
datasetSplit,
setDatasetSplit,
datasetEvalSplit,
setDatasetEvalSplit,
hfToken,
} = useTrainingConfigStore(
useShallow((s) => ({
@ -77,11 +85,14 @@ export function DatasetSection() {
setDatasetSubset: s.setDatasetSubset,
datasetSplit: s.datasetSplit,
setDatasetSplit: s.setDatasetSplit,
datasetEvalSplit: s.datasetEvalSplit,
setDatasetEvalSplit: s.setDatasetEvalSplit,
hfToken: s.hfToken,
})),
);
const [inputValue, setInputValue] = useState("");
const [advancedOpen, setAdvancedOpen] = useState(false);
const openPreview = useDatasetPreviewDialogStore((s) => s.openPreview);
const selectingRef = useRef(false);
const debouncedQuery = useDebouncedValue(inputValue);
@ -132,79 +143,82 @@ export function DatasetSection() {
title="Dataset"
description="Select or upload training data"
accent="indigo"
className="md:min-h-[450px] dark:shadow-border"
className="md:min-h-[470px] dark:shadow-border"
>
<div className="flex flex-col gap-4">
<div className="flex flex-col gap-2">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
Load from Hub
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
Search Hugging Face datasets or enter a path like
'username/dataset-name'.{" "}
<a
href="https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/datasets-guide"
target="_blank"
rel="noopener noreferrer"
className="text-primary underline"
>
Read more
</a>
</TooltipContent>
</Tooltip>
</span>
<div
ref={comboboxAnchorRef}
onKeyDown={(event) => {
if (event.key !== "Enter") return;
if (!(event.target instanceof HTMLInputElement)) return;
event.preventDefault();
if (hfResults.length > 0) {
handleDatasetSelect(hfResults[0].id);
} else {
const text = event.target.value.trim();
if (text) handleDatasetSelect(text);
}
}}
>
<Combobox
items={resultIds}
filteredItems={resultIds}
filter={null}
value={dataset}
onValueChange={handleDatasetSelect}
onInputValueChange={handleInputChange}
itemToStringValue={(id) => id}
autoHighlight={true}
<div className="flex flex-col gap-2">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
Load from Hub
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
Search Hugging Face datasets or enter a path like
'username/dataset-name'.{" "}
<a
href="https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/datasets-guide"
target="_blank"
rel="noopener noreferrer"
className="text-primary underline"
>
Read more
</a>
</TooltipContent>
</Tooltip>
</span>
<div
ref={comboboxAnchorRef}
onKeyDown={(event) => {
if (event.key !== "Enter") return;
if (!(event.target instanceof HTMLInputElement)) return;
event.preventDefault();
if (hfResults.length > 0) {
handleDatasetSelect(hfResults[0].id);
} else {
const text = event.target.value.trim();
if (text) handleDatasetSelect(text);
}
}}
>
<ComboboxInput placeholder="Search datasets..." className="w-full">
<InputGroupAddon>
<HugeiconsIcon icon={Search01Icon} className="size-4" />
</InputGroupAddon>
</ComboboxInput>
<ComboboxContent anchor={comboboxAnchorRef}>
{isLoading ? (
<div className="flex items-center justify-center py-4 gap-2 text-xs text-muted-foreground">
<Spinner className="size-4" /> Searching...
</div>
) : (
<ComboboxEmpty>No datasets found</ComboboxEmpty>
)}
<div
ref={scrollRef}
className="max-h-64 overflow-y-auto overscroll-contain [scrollbar-width:thin]"
<Combobox
items={resultIds}
filteredItems={resultIds}
filter={null}
value={dataset}
onValueChange={handleDatasetSelect}
onInputValueChange={handleInputChange}
itemToStringValue={(id) => id}
autoHighlight={true}
>
<ComboboxInput
placeholder="Search datasets..."
className="w-full"
>
<InputGroupAddon>
<HugeiconsIcon icon={Search01Icon} className="size-4" />
</InputGroupAddon>
</ComboboxInput>
<ComboboxContent anchor={comboboxAnchorRef}>
{isLoading ? (
<div className="flex items-center justify-center py-4 gap-2 text-xs text-muted-foreground">
<Spinner className="size-4" /> Searching...
</div>
) : (
<ComboboxEmpty>No datasets found</ComboboxEmpty>
)}
<div
ref={scrollRef}
className="max-h-64 overflow-y-auto overscroll-contain [scrollbar-width:thin]"
>
<ComboboxList className="p-1 !max-h-none !overflow-visible">
{(id: string) => {
const r = hfResults.find((ds) => ds.id === id);
@ -220,58 +234,58 @@ export function DatasetSection() {
<ComboboxItem
key={id}
value={id}
className="justify-between"
>
<Tooltip>
<TooltipTrigger asChild={true}>
<span className="min-w-0 flex-1 truncate">
className="justify-between"
>
<Tooltip>
<TooltipTrigger asChild={true}>
<span className="min-w-0 flex-1 truncate">
{id}
</span>
</TooltipTrigger>
<TooltipContent
side="left"
className="max-w-xs break-all"
>
{id}
</TooltipContent>
</Tooltip>
{detail && (
<span className="text-[10px] text-muted-foreground shrink-0">
{detail}
</span>
</TooltipTrigger>
<TooltipContent
side="left"
className="max-w-xs break-all"
>
{id}
</TooltipContent>
</Tooltip>
{detail && (
<span className="text-[10px] text-muted-foreground shrink-0">
{detail}
</span>
)}
</ComboboxItem>
);
}}
</ComboboxList>
<div ref={sentinelRef} className="h-px" />
{isLoadingMore && (
<div className="flex items-center justify-center py-2">
<Spinner className="size-3.5 text-muted-foreground" />
</div>
)}
</div>
</ComboboxContent>
</Combobox>
)}
</ComboboxItem>
);
}}
</ComboboxList>
<div ref={sentinelRef} className="h-px" />
{isLoadingMore && (
<div className="flex items-center justify-center py-2">
<Spinner className="size-3.5 text-muted-foreground" />
</div>
)}
</div>
</ComboboxContent>
</Combobox>
</div>
{(tokenValidationError ?? hfSearchError) && (
<p className="text-xs text-destructive">
{tokenValidationError ?? hfSearchError}
{" — "}
<a
href="https://huggingface.co/settings/tokens"
target="_blank"
rel="noopener noreferrer"
className="underline"
>
Get or update token
</a>
</p>
)}
{isCheckingToken && (
<p className="text-xs text-muted-foreground">Checking token…</p>
)}
</div>
{(tokenValidationError ?? hfSearchError) && (
<p className="text-xs text-destructive">
{tokenValidationError ?? hfSearchError}
{" — "}
<a
href="https://huggingface.co/settings/tokens"
target="_blank"
rel="noopener noreferrer"
className="underline"
>
Get or update token
</a>
</p>
)}
{isCheckingToken && (
<p className="text-xs text-muted-foreground">Checking token…</p>
)}
</div>
<HfDatasetSubsetSplitSelectors
variant="studio"
@ -282,52 +296,67 @@ export function DatasetSection() {
setDatasetSubset={setDatasetSubset}
datasetSplit={datasetSplit}
setDatasetSplit={setDatasetSplit}
datasetEvalSplit={datasetEvalSplit}
setDatasetEvalSplit={setDatasetEvalSplit}
/>
<div className="flex flex-col gap-2">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
Target Format
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
Format of your training data. Auto-detect works for most
datasets.{" "}
<a
href="https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/datasets-guide"
target="_blank"
rel="noopener noreferrer"
className="text-primary underline"
>
Read more
</a>
</TooltipContent>
</Tooltip>
</span>
<Select
value={datasetFormat}
onValueChange={(v) => setDatasetFormat(v as typeof datasetFormat)}
>
<SelectTrigger className="w-full">
<SelectValue />
</SelectTrigger>
<SelectContent>
<SelectItem value="auto">Auto</SelectItem>
<SelectItem value="alpaca">Alpaca</SelectItem>
<SelectItem value="chatml">ChatML</SelectItem>
<SelectItem value="sharegpt">ShareGPT</SelectItem>
</SelectContent>
</Select>
</div>
<Collapsible open={advancedOpen} onOpenChange={setAdvancedOpen}>
<CollapsibleTrigger className="flex w-full cursor-pointer items-center gap-1.5 text-xs text-muted-foreground">
<HugeiconsIcon
icon={ArrowDown01Icon}
className={`size-3.5 transition-transform ${advancedOpen ? "rotate-180" : ""}`}
/>
Advanced
</CollapsibleTrigger>
<CollapsibleContent className="mt-3">
<div className="flex flex-col gap-2">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
Target Format
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
Format of your training data. Auto-detect works for most
datasets.{" "}
<a
href="https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/datasets-guide"
target="_blank"
rel="noopener noreferrer"
className="text-primary underline"
>
Read more
</a>
</TooltipContent>
</Tooltip>
</span>
<Select
value={datasetFormat}
onValueChange={(v) =>
setDatasetFormat(v as typeof datasetFormat)
}
>
<SelectTrigger className="w-full">
<SelectValue />
</SelectTrigger>
<SelectContent>
<SelectItem value="auto">Auto</SelectItem>
<SelectItem value="alpaca">Alpaca</SelectItem>
<SelectItem value="chatml">ChatML</SelectItem>
<SelectItem value="sharegpt">ShareGPT</SelectItem>
</SelectContent>
</Select>
</div>
</CollapsibleContent>
</Collapsible>
{dataset ? (
<div className="flex items-center gap-3 rounded-lg border bg-muted/40 px-3.5 py-3">

2
studio/frontend/src/features/studio/sections/params-section.tsx
View file

@ -127,7 +127,7 @@ export function ParamsSection(): ReactElement {
title="Parameters"
description="Configure training hyperparameters"
accent="orange"
className="md:min-h-[450px]"
className="md:min-h-[470px]"
>
<div className="flex flex-col gap-4">
{/* Max Steps */}

2
studio/frontend/src/features/studio/sections/training-section.tsx
View file

@ -98,7 +98,7 @@ export function TrainingSection() {
title="Training"
description="Monitor and control training"
accent="blue"
className="md:min-h-[450px]"
className="md:min-h-[470px]"
>
<div className="flex flex-col gap-4">
{/* Loss chart */}

5
studio/frontend/src/features/training/api/mappers.ts
View file

@ -24,8 +24,9 @@ export function buildTrainingStartPayload(
load_in_4bit: adapterMethod ? isQlorMethod : false,
max_seq_length: config.contextLength,
hf_dataset: hfDataset,
hf_dataset_config: hfDataset ? config.datasetSubset : null,
hf_dataset_split: hfDataset ? config.datasetSplit : null,
subset: hfDataset ? config.datasetSubset : null,
train_split: hfDataset ? config.datasetSplit : null,
eval_split: hfDataset ? config.datasetEvalSplit : null,
local_datasets: [],
format_type: config.datasetFormat,
custom_format_mapping: customFormatMapping,

319
studio/frontend/src/features/training/components/hf-dataset-subset-split-selectors.tsx
View file

@ -29,6 +29,8 @@ type Props = {
setDatasetSubset: (v: string | null) => void;
datasetSplit: string | null;
setDatasetSplit: (v: string | null) => void;
datasetEvalSplit: string | null;
setDatasetEvalSplit: (v: string | null) => void;
};
export function HfDatasetSubsetSplitSelectors({
@ -40,12 +42,13 @@ export function HfDatasetSubsetSplitSelectors({
setDatasetSubset,
datasetSplit,
setDatasetSplit,
datasetEvalSplit,
setDatasetEvalSplit,
}: Props) {
const {
subsets: hfSubsets,
splits: hfSplits,
hasMultipleSubsets,
hasMultipleSplits,
isLoading,
error,
} = useHfDatasetSplits(enabled ? datasetName : null, datasetSubset, {
@ -78,6 +81,8 @@ export function HfDatasetSubsetSplitSelectors({
if (!enabled || !datasetName) return null;
const showDropdowns = !isLoading && !error && hfSubsets.length > 0;
return (
<>
{isLoading && (
@ -105,167 +110,169 @@ export function HfDatasetSubsetSplitSelectors({
</div>
)}
{!isLoading && !error && hasMultipleSubsets && (
{showDropdowns && (
<>
{variant === "wizard" ? (
<Field>
<FieldLabel className="flex items-center gap-1.5">
Subset
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-muted-foreground/50 hover:text-muted-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3.5"
/>
</button>
</TooltipTrigger>
<TooltipContent className="max-w-xs">
This dataset has multiple subsets. Select which one to use
for training.
</TooltipContent>
</Tooltip>
</FieldLabel>
<Select
value={datasetSubset ?? ""}
onValueChange={(v) => setDatasetSubset(v || null)}
>
<SelectTrigger className="w-full">
<SelectValue placeholder="Select a subset..." />
</SelectTrigger>
<SelectContent>
{hfSubsets.map((subset) => (
<SelectItem key={subset} value={subset}>
{subset}
</SelectItem>
))}
</SelectContent>
</Select>
</Field>
) : (
<div className="flex flex-col gap-1.5">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
Subset
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
This dataset has multiple subsets. Select which one to use
for training.
</TooltipContent>
</Tooltip>
</span>
<Select
value={datasetSubset ?? ""}
onValueChange={(v) => setDatasetSubset(v || null)}
>
<SelectTrigger className="w-full">
<SelectValue placeholder="Select a subset..." />
</SelectTrigger>
<SelectContent>
{hfSubsets.map((subset) => (
<SelectItem key={subset} value={subset}>
{subset}
</SelectItem>
))}
</SelectContent>
</Select>
{variant === "studio" ? (
<div className="grid gap-3 sm:grid-cols-2">
<SelectorDropdown
variant={variant}
label="Subset"
tooltip="Select which subset (config) of the dataset to use."
value={datasetSubset}
onChange={setDatasetSubset}
options={hfSubsets}
placeholder="Select a subset..."
/>
<SelectorDropdown
variant={variant}
label="Train Split"
tooltip="Select which split to use for training."
value={datasetSplit}
onChange={setDatasetSplit}
options={hfSplits}
placeholder="Select a split..."
/>
</div>
)}
</>
)}
{!isLoading && !error && hasMultipleSplits && (
<>
{variant === "wizard" ? (
<Field>
<FieldLabel className="flex items-center gap-1.5">
Split
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-muted-foreground/50 hover:text-muted-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3.5"
/>
</button>
</TooltipTrigger>
<TooltipContent className="max-w-xs">
Select which split of the dataset to use for training.
</TooltipContent>
</Tooltip>
</FieldLabel>
<Select
value={datasetSplit ?? ""}
onValueChange={(v) => setDatasetSplit(v || null)}
>
<SelectTrigger className="w-full">
<SelectValue placeholder="Select a split..." />
</SelectTrigger>
<SelectContent>
{hfSplits.map((split) => (
<SelectItem key={split} value={split}>
{split}
</SelectItem>
))}
</SelectContent>
</Select>
</Field>
) : (
<div className="flex flex-col gap-1.5">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
Split
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
Select which split of the dataset to use for training.
</TooltipContent>
</Tooltip>
</span>
<Select
value={datasetSplit ?? ""}
onValueChange={(v) => setDatasetSplit(v || null)}
>
<SelectTrigger className="w-full">
<SelectValue placeholder="Select a split..." />
</SelectTrigger>
<SelectContent>
{hfSplits.map((split) => (
<SelectItem key={split} value={split}>
{split}
</SelectItem>
))}
</SelectContent>
</Select>
</div>
<>
<SelectorDropdown
variant={variant}
label="Subset"
tooltip="Select which subset (config) of the dataset to use."
value={datasetSubset}
onChange={setDatasetSubset}
options={hfSubsets}
placeholder="Select a subset..."
/>
<SelectorDropdown
variant={variant}
label="Train Split"
tooltip="Select which split to use for training."
value={datasetSplit}
onChange={setDatasetSplit}
options={hfSplits}
placeholder="Select a split..."
/>
</>
)}
<SelectorDropdown
variant={variant}
label="Eval Split"
tooltip="Select which split to use for evaluation. None means no evaluation during training."
value={datasetEvalSplit}
onChange={setDatasetEvalSplit}
options={hfSplits}
placeholder="None"
allowNone
/>
</>
)}
</>
);
}
function SelectorDropdown({
variant,
label,
tooltip,
value,
onChange,
options,
placeholder,
allowNone = false,
}: {
variant: "wizard" | "studio";
label: string;
tooltip: string;
value: string | null;
onChange: (v: string | null) => void;
options: string[];
placeholder: string;
allowNone?: boolean;
}) {
if (variant === "wizard") {
return (
<Field>
<FieldLabel className="flex items-center gap-1.5">
{label}
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-muted-foreground/50 hover:text-muted-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3.5"
/>
</button>
</TooltipTrigger>
<TooltipContent className="max-w-xs">
{tooltip}
</TooltipContent>
</Tooltip>
</FieldLabel>
<Select
value={value ?? "_none"}
onValueChange={(v) => onChange(v === "_none" ? null : v)}
>
<SelectTrigger className="w-full">
<SelectValue placeholder={placeholder} />
</SelectTrigger>
<SelectContent>
{allowNone && (
<SelectItem value="_none">None</SelectItem>
)}
{options.map((opt) => (
<SelectItem key={opt} value={opt}>
{opt}
</SelectItem>
))}
</SelectContent>
</Select>
</Field>
);
}
return (
<div className="flex flex-col gap-1.5">
<span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
{label}
<Tooltip>
<TooltipTrigger asChild={true}>
<button
type="button"
className="text-foreground/70 hover:text-foreground"
>
<HugeiconsIcon
icon={InformationCircleIcon}
className="size-3"
/>
</button>
</TooltipTrigger>
<TooltipContent>
{tooltip}
</TooltipContent>
</Tooltip>
</span>
<Select
value={value ?? "_none"}
onValueChange={(v) => onChange(v === "_none" ? null : v)}
>
<SelectTrigger className="w-full">
<SelectValue placeholder={placeholder} />
</SelectTrigger>
<SelectContent>
{allowNone && (
<SelectItem value="_none">None</SelectItem>
)}
{options.map((opt) => (
<SelectItem key={opt} value={opt}>
{opt}
</SelectItem>
))}
</SelectContent>
</Select>
</div>
);
}

14
studio/frontend/src/features/training/stores/training-config-store.ts
View file

@ -26,6 +26,7 @@ const initialState: TrainingConfigState = {
dataset: null,
datasetSubset: null,
datasetSplit: null,
datasetEvalSplit: null,
datasetManualMapping: emptyManualMapping(),
uploadedFile: null,
isCheckingVision: false,
@ -252,6 +253,7 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
dataset,
datasetSubset: null,
datasetSplit: null,
datasetEvalSplit: null,
datasetManualMapping: emptyManualMapping(),
isDatasetMultimodal: null,
isCheckingDataset: false,
@ -264,6 +266,7 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
set({
datasetSubset,
datasetSplit: null,
datasetEvalSplit: null,
datasetManualMapping: emptyManualMapping(),
isDatasetMultimodal: null,
isCheckingDataset: false,
@ -300,6 +303,12 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
const split = state.datasetSplit || "train";
runDatasetCheck(datasetName, split);
},
setDatasetEvalSplit: (datasetEvalSplit) => {
set({
datasetEvalSplit,
evalSteps: datasetEvalSplit ? 0.1 : 0,
});
},
setDatasetManualMapping: (datasetManualMapping) =>
set({ datasetManualMapping }),
setUploadedFile: (uploadedFile) => set({ uploadedFile }),
@ -359,7 +368,7 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
},
{
name: "unsloth_training_config_v1",
version: 5,
version: 6,
migrate: (persisted, version) => {
const s = persisted as Record<string, unknown>;
if (version < 2 && s.datasetSubset == null && s.datasetConfig != null) {
@ -375,6 +384,9 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
if (version < 5 && s.lrSchedulerType == null) {
s.lrSchedulerType = DEFAULT_HYPERPARAMS.lrSchedulerType;
}
if (version < 6 && s.datasetEvalSplit == null) {
s.datasetEvalSplit = null;
}
return s as unknown as TrainingConfigStore;
},
partialize: partializePersistedState,

5
studio/frontend/src/features/training/types/api.ts
View file

@ -5,8 +5,9 @@ export interface TrainingStartRequest {
load_in_4bit: boolean;
max_seq_length: number;
hf_dataset: string | null;
hf_dataset_config: string | null;
hf_dataset_split: string | null;
subset: string | null;
train_split: string | null;
eval_split: string | null;
local_datasets: string[];
format_type: string;
custom_format_mapping?: Record<string, string> | null;

2
studio/frontend/src/features/training/types/config.ts
View file

@ -24,6 +24,7 @@ export interface TrainingConfigState {
dataset: string | null;
datasetSubset: string | null;
datasetSplit: string | null;
datasetEvalSplit: string | null;
datasetManualMapping: DatasetManualMapping;
uploadedFile: string | null;
epochs: number;
@ -81,6 +82,7 @@ export interface TrainingConfigActions {
setDataset: (dataset: string | null) => void;
setDatasetSubset: (subset: string | null) => void;
setDatasetSplit: (split: string | null) => void;
setDatasetEvalSplit: (split: string | null) => void;
setDatasetManualMapping: (mapping: DatasetManualMapping) => void;
setUploadedFile: (file: string | null) => void;
setEpochs: (epochs: number) => void;