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Model: Marco333/qwen2.5-0.5b-game-commands-stt
Source: Original Platform

train.py

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+"""
+Training script: Fine-tune Qwen2.5-0.5B-Instruct for game command recognition from noisy STT.
+
+Based on:
+- arxiv:2502.12923 approach (structured JSON output, synthetic command data)
+- TRL v1.3.0 SFTTrainer with LoRA (PEFT)
+- Target: llama.cpp GGUF compatible output (Qwen2 architecture)
+
+Dataset: Marco333/game-commands-noisy-stt (conversational messages format)
+Model: Qwen/Qwen2.5-0.5B-Instruct -> fine-tuned -> merge LoRA -> push to Hub
+
+Usage:
+    pip install trl peft transformers datasets accelerate torch
+    python train.py
+"""
+
+import torch
+from datasets import load_dataset
+from trl import SFTTrainer, SFTConfig
+from peft import LoraConfig
+
+# =============================================================================
+# Configuration - edit these for your use case
+# =============================================================================
+
+MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
+DATASET_NAME = "Marco333/game-commands-noisy-stt"
+OUTPUT_DIR = "./qwen2.5-0.5b-game-commands"
+HUB_MODEL_ID = "Marco333/qwen2.5-0.5b-game-commands-stt"
+
+# Training hyperparameters
+LEARNING_RATE = 2e-4       # Higher LR for LoRA (10x base)
+NUM_EPOCHS = 5
+BATCH_SIZE = 8
+GRAD_ACCUM_STEPS = 2
+MAX_LENGTH = 512           # Short sequences for command recognition
+WARMUP_RATIO = 0.05
+
+# LoRA configuration
+LORA_R = 32
+LORA_ALPHA = 16
+LORA_DROPOUT = 0.05
+
+
+def main():
+    print(f"Training {MODEL_NAME} on {DATASET_NAME}")
+    dataset = load_dataset(DATASET_NAME)
+
+    peft_config = LoraConfig(
+        r=LORA_R,
+        lora_alpha=LORA_ALPHA,
+        lora_dropout=LORA_DROPOUT,
+        bias="none",
+        task_type="CAUSAL_LM",
+        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    )
+
+    training_args = SFTConfig(
+        output_dir=OUTPUT_DIR,
+        num_train_epochs=NUM_EPOCHS,
+        per_device_train_batch_size=BATCH_SIZE,
+        per_device_eval_batch_size=BATCH_SIZE,
+        gradient_accumulation_steps=GRAD_ACCUM_STEPS,
+        learning_rate=LEARNING_RATE,
+        lr_scheduler_type="cosine",
+        warmup_ratio=WARMUP_RATIO,
+        bf16=True,
+        max_length=MAX_LENGTH,
+        packing=False,
+        logging_steps=10,
+        logging_first_step=True,
+        disable_tqdm=True,
+        logging_strategy="steps",
+        eval_strategy="epoch",
+        save_strategy="epoch",
+        save_total_limit=2,
+        load_best_model_at_end=True,
+        metric_for_best_model="eval_loss",
+        push_to_hub=True,
+        hub_model_id=HUB_MODEL_ID,
+        hub_strategy="end",
+        report_to="none",
+        assistant_only_loss=True,
+        seed=42,
+        dataloader_num_workers=2,
+    )
+
+    trainer = SFTTrainer(
+        model=MODEL_NAME,
+        args=training_args,
+        train_dataset=dataset["train"],
+        eval_dataset=dataset["test"],
+        peft_config=peft_config,
+    )
+
+    print("Starting training...")
+    train_result = trainer.train()
+    print(f"Done! Loss: {train_result.training_loss:.4f}")
+    trainer.save_model()
+
+    # Merge LoRA into base model for GGUF compatibility
+    print("Merging LoRA...")
+    from peft import AutoPeftModelForCausalLM
+    from transformers import AutoTokenizer
+
+    merged = AutoPeftModelForCausalLM.from_pretrained(OUTPUT_DIR, dtype=torch.bfloat16, device_map="auto")
+    merged = merged.merge_and_unload()
+    merged_dir = f"{OUTPUT_DIR}-merged"
+    merged.save_pretrained(merged_dir)
+    tok = AutoTokenizer.from_pretrained(MODEL_NAME)
+    tok.save_pretrained(merged_dir)
+
+    #print("Pushing to Hub...")
+    #merged.push_to_hub(HUB_MODEL_ID, commit_message="Merged model - LoRA into Qwen2.5-0.5B for game commands")
+    #tok.push_to_hub(HUB_MODEL_ID, commit_message="Add tokenizer")
+    #print(f"Model: https://huggingface.co/{HUB_MODEL_ID}")
+
+    # Quick inference test
+    print("\nInference test:")
+    from transformers import pipeline
+    pipe = pipeline("text-generation", model=merged_dir, dtype=torch.bfloat16, device_map="auto")
+    system = "You are a game command interpreter. The user's input is noisy speech-to-text from a microphone in a gaming environment. Your job is to identify the intended game command from the noisy input.\n\nValid commands: Attack, CastSpell, CloseDoor, Crouch, Defend, DropItem, Heal, Interact, Jump, OpenDoor, OpenInventory, OpenMap, PauseGame, PickUp, Reload, SaveGame, Sprint, Stop, SwitchWeapon, UseItem\n\nRespond with ONLY a JSON object: {\"command\": \"<CommandName>\"}\nIf the input doesn't match any command, respond: {\"command\": \"Unknown\"}"
+
+    for inp in ["opun da dur", "attak enami", "hel me", "pic it op", "hello how are you"]:
+        out = pipe([{"role": "system", "content": system}, {"role": "user", "content": inp}], max_new_tokens=50, do_sample=False)
+        print(f"  '{inp}' -> {out[0]['generated_text'][-1]['content']}")
+
+    print("\n\nGGUF conversion (for llama.cpp):")
+    print(f"  python llama.cpp/convert_hf_to_gguf.py {HUB_MODEL_ID} --outtype f16 --outfile model.gguf")
+    print(f"  ./llama-quantize model.gguf model_q4.gguf Q4_K_M")
+
+
+if __name__ == "__main__":
+    main()