Upload train_qwen3_multidata.py with huggingface_hub

train_qwen3_multidata.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "datasets", "transformers", "accelerate", "bitsandbytes"]
+# ///
+
+from datasets import load_dataset, concatenate_datasets
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+import trackio
+
+print("Loading datasets...")
+
+# Dataset 1: Codeforces competitive programming (messages format)
+ds1 = load_dataset(
+    "open-r1/codeforces-cots", 
+    "solutions_w_editorials_py_decontaminated",
+    split="train"
+)
+ds1 = ds1.select_columns(["messages"])
+print(f"Codeforces: {len(ds1)} examples")
+
+# Dataset 2: Golang coder (messages format)
+ds2 = load_dataset("smcleod/golang-coder", split="train")
+ds2 = ds2.select_columns(["messages"])
+print(f"Golang: {len(ds2)} examples")
+
+# Dataset 3: Vue/Nuxt/Tailwind (text format - needs conversion)
+ds3_raw = load_dataset("kevind13/vuejs-nuxt-tailwind-codellama", split="train")
+# Convert text format to messages format for consistency
+def text_to_messages(example):
+    return {"messages": [{"role": "user", "content": "Continue the code."}, {"role": "assistant", "content": example["text"]}]}
+ds3 = ds3_raw.map(text_to_messages, remove_columns=ds3_raw.column_names)
+print(f"Vue/Nuxt/Tailwind: {len(ds3)} examples")
+
+# Dataset 4: React code instructions (messages format)
+ds4 = load_dataset("cfahlgren1/react-code-instructions", split="train")
+ds4 = ds4.select_columns(["messages"])
+print(f"React: {len(ds4)} examples")
+
+# Concatenate all datasets
+combined = concatenate_datasets([ds1, ds2, ds3, ds4])
+combined = combined.shuffle(seed=42)
+print(f"Combined dataset: {len(combined)} examples")
+
+# Create train/eval split
+dataset_split = combined.train_test_split(test_size=0.05, seed=42)
+print(f"Train: {len(dataset_split['train'])}, Eval: {len(dataset_split['test'])}")
+
+print("Starting training...")
+
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=dataset_split["train"],
+    eval_dataset=dataset_split["test"],
+    peft_config=LoraConfig(
+        r=16,
+        lora_alpha=32,
+        lora_dropout=0.05,
+        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+        bias="none",
+        task_type="CAUSAL_LM",
+    ),
+    args=SFTConfig(
+        output_dir="qwen3-0.6b-multicode",
+        push_to_hub=True,
+        hub_model_id="chaddy81/qwen3-0.6b-multicode-sft",
+        hub_private_repo=False,
+        num_train_epochs=1,
+        per_device_train_batch_size=2,
+        per_device_eval_batch_size=2,
+        gradient_accumulation_steps=8,
+        gradient_checkpointing=True,
+        learning_rate=2e-4,
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.05,
+        logging_steps=10,
+        save_strategy="steps",
+        save_steps=500,
+        eval_strategy="steps",
+        eval_steps=500,
+        hub_strategy="every_save",
+        bf16=True,
+        report_to="trackio",
+        project="qwen3-multicode",
+        run_name="qwen3-0.6b-sft-multicode",
+    )
+)
+
+trainer.train()
+trainer.push_to_hub()
+print("Training complete! Model pushed to Hub.")