Fix YAML task_categories to official dataset taxonomy: text-generation, question-answering

README.md

@@ -33,7 +33,7 @@ configs:
 
 A state-of-the-art, balanced **7-domain** pre-training dataset engineered specifically for **Small Language Models (Tiny SLMs: 50M – 2B parameters)** such as SmolLM2, SmolLM3, MobileLLM, Llama 3.2 1B, and custom architectures.
 
-100% compatible with **Unsloth Studio**, **Unsloth AI**, **Hugging Face **, and **PyTorch DataLoaders**.
+100% compatible with **Unsloth Studio**, **Unsloth AI**, **Hugging Face `datasets`**, and **PyTorch DataLoaders**.
 
 ---
 
@@ -53,24 +53,66 @@ A state-of-the-art, balanced **7-domain** pre-training dataset engineered specif
 
 | Domain | Source Repository | Description & Quality Filter | Token Share |
 | :--- | :--- | :--- | :--- |
-| **Synthetic Textbooks** |  |  textbooks and topic deep-dives | **28.0%** |
-| **Educational Web** |  | Sample 10BT (score >= 2.8 filter) | **23.5%** |
-| **Step-by-Step Math** |  |  &  (score 4+) | **19.5%** |
-| **Clean Polyglot Code** |  | Python, Rust, C++, C, Go, JS, TS, Java, SQL, Shell | **12.5%** |
-| **General Knowledge** |  +  | English Wikipedia (dense factual) & DCLM web corpus | **7.8%** |
-| **Reasoning & Tool Traces** |  | OpenThoughts3, Nemotron R1, and agentic tool traces | **6.5%** |
-| **Stories & Narrative** |  | Synthetic coherent narrative, dialogue, & vocabulary | **2.1%** |
+| **Synthetic Textbooks** | `HuggingFaceTB/smollm-corpus` | `cosmopedia-v2` textbooks and topic deep-dives | **28.0%** |
+| **Educational Web** | `HuggingFaceFW/fineweb-edu` | Sample 10BT (score >= 2.8 filter) | **23.5%** |
+| **Step-by-Step Math** | `HuggingFaceTB/finemath` | `finemath-4plus` & `infiwebmath-4plus` (score 4+) | **19.5%** |
+| **Clean Polyglot Code** | `codeparrot/github-code-clean` | Python, Rust, C++, C, Go, JS, TS, Java, SQL, Shell | **12.5%** |
+| **General Knowledge** | `wikimedia/wikipedia` + `mlfoundations/dclm-baseline-1.0-parquet` | English Wikipedia (dense factual) & DCLM web corpus | **7.8%** |
+| **Reasoning & Tool Traces** | `HuggingFaceTB/smoltalk2` | OpenThoughts3, Nemotron R1, and agentic tool traces | **6.5%** |
+| **Stories & Narrative** | `roneneldan/TinyStories` | Synthetic coherent narrative, dialogue, & vocabulary | **2.1%** |
 
 ---
 
 ## 🦥 Quickstart: Unsloth Studio & Hugging Face
 
 ### 1. Load with Hugging Face Datasets
+```python
+from datasets import load_dataset
 
+# Load Train Split
+train_dataset = load_dataset("JustACluelessKidAtSchool/tiny-slm-pretraining-corpus", split="train")
 
-### 2. Pre-Train with Unsloth in 1 Command
+# Load Validation Split
+val_dataset = load_dataset("JustACluelessKidAtSchool/tiny-slm-pretraining-corpus", split="validation")
+```
 
+### 2. Pre-Train with Unsloth in 1 Command
+```python
+from unsloth import FastLanguageModel
+from datasets import load_dataset
+from transformers import TrainingArguments
+from trl import SFTTrainer
+
+# Load Tiny Model (e.g. SmolLM2-135M or Llama-3.2-1B)
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="HuggingFaceTB/SmolLM2-135M",
+    max_seq_length=2048,
+    load_in_4bit=False,
+)
+
+# Load Dataset from Hub
+dataset = load_dataset("JustACluelessKidAtSchool/tiny-slm-pretraining-corpus", split="train")
+
+# Train with Unsloth
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    max_seq_length=2048,
+    args=TrainingArguments(
+        output_dir="./tiny_slm_output",
+        per_device_train_batch_size=4,
+        gradient_accumulation_steps=8,
+        max_steps=5000,
+        learning_rate=5e-4,
+        fp16=True,
+        optim="adamw_8bit",
+    ),
+)
+trainer.train()
+```
 
 ### 3. In Unsloth Studio GUI
-1. Set Dataset to: 
-2. Text Column: 
+1. Set Dataset to: `JustACluelessKidAtSchool/tiny-slm-pretraining-corpus`
+2. Text Column: `text`