diff --git a/studio/backend/utils/datasets/dataset_utils.py b/studio/backend/utils/datasets/dataset_utils.py
index 72298dc245..3a4d54f93f 100644
--- a/studio/backend/utils/datasets/dataset_utils.py
+++ b/studio/backend/utils/datasets/dataset_utils.py
@@ -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,
diff --git a/studio/frontend/src/features/onboarding/components/steps/dataset-step.tsx b/studio/frontend/src/features/onboarding/components/steps/dataset-step.tsx
index 484b10ddb4..1ad4fad8f0 100644
--- a/studio/frontend/src/features/onboarding/components/steps/dataset-step.tsx
+++ b/studio/frontend/src/features/onboarding/components/steps/dataset-step.tsx
@@ -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}
           />
         </>
       ) : (
diff --git a/studio/frontend/src/features/studio/sections/dataset-section.tsx b/studio/frontend/src/features/studio/sections/dataset-section.tsx
index 1dfce7d21a..6f6a6a401e 100644
--- a/studio/frontend/src/features/studio/sections/dataset-section.tsx
+++ b/studio/frontend/src/features/studio/sections/dataset-section.tsx
@@ -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">
diff --git a/studio/frontend/src/features/studio/sections/params-section.tsx b/studio/frontend/src/features/studio/sections/params-section.tsx
index 144a1f34d7..b7ad7aec0b 100644
--- a/studio/frontend/src/features/studio/sections/params-section.tsx
+++ b/studio/frontend/src/features/studio/sections/params-section.tsx
@@ -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 */}
diff --git a/studio/frontend/src/features/studio/sections/training-section.tsx b/studio/frontend/src/features/studio/sections/training-section.tsx
index 3f342ad68c..af6a16fc44 100644
--- a/studio/frontend/src/features/studio/sections/training-section.tsx
+++ b/studio/frontend/src/features/studio/sections/training-section.tsx
@@ -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 */}
diff --git a/studio/frontend/src/features/training/api/mappers.ts b/studio/frontend/src/features/training/api/mappers.ts
index 5f98913158..1adfbd8b6d 100644
--- a/studio/frontend/src/features/training/api/mappers.ts
+++ b/studio/frontend/src/features/training/api/mappers.ts
@@ -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,
diff --git a/studio/frontend/src/features/training/components/hf-dataset-subset-split-selectors.tsx b/studio/frontend/src/features/training/components/hf-dataset-subset-split-selectors.tsx
index 00532ce860..d21fd55ff4 100644
--- a/studio/frontend/src/features/training/components/hf-dataset-subset-split-selectors.tsx
+++ b/studio/frontend/src/features/training/components/hf-dataset-subset-split-selectors.tsx
@@ -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>
+  );
+}
diff --git a/studio/frontend/src/features/training/stores/training-config-store.ts b/studio/frontend/src/features/training/stores/training-config-store.ts
index 12f0c2ab93..b2d1858716 100644
--- a/studio/frontend/src/features/training/stores/training-config-store.ts
+++ b/studio/frontend/src/features/training/stores/training-config-store.ts
@@ -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,
diff --git a/studio/frontend/src/features/training/types/api.ts b/studio/frontend/src/features/training/types/api.ts
index fce7e18b0b..22f02e4331 100644
--- a/studio/frontend/src/features/training/types/api.ts
+++ b/studio/frontend/src/features/training/types/api.ts
@@ -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;
diff --git a/studio/frontend/src/features/training/types/config.ts b/studio/frontend/src/features/training/types/config.ts
index f64c5d5aa8..6c2feec172 100644
--- a/studio/frontend/src/features/training/types/config.ts
+++ b/studio/frontend/src/features/training/types/config.ts
@@ -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;