Delete training.py

training.py

@@ -1,98 +0,0 @@
-from datasets import load_dataset
-from transformers import (
-    AutoTokenizer,
-    AutoModelForCausalLM,
-    TrainingArguments,
-    Trainer,
-    DataCollatorForLanguageModeling
-)
-from peft import LoraConfig, get_peft_model, merge_lora_weights
-from huggingface_hub import login
-import os
-
-# ====== HF Login (optional if pushing to Hub) ======
-hf_token = os.getenv("HF_TOKEN")  # set this as env var or hardcode
-if hf_token:
-    login(token=hf_token)
-
-# ====== 1. Config ======
-model_name = "mistralai/Mistral-7B-v0.1"
-dataset_path = "tech_domains.jsonl"  # local file or HF dataset
-output_dir = "./TechChat"
-max_seq_length = 512
-
-# ====== 2. Load Dataset ======
-dataset = load_dataset("json", data_files=dataset_path)
-
-# ====== 3. Tokenizer ======
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer.pad_token = tokenizer.eos_token
-
-def tokenize(example):
-    text = example["instruction"] + "\n" + example["output"]
-    tokens = tokenizer(
-        text,
-        truncation=True,
-        padding="max_length",
-        max_length=max_seq_length
-    )
-    tokens["labels"] = tokens["input_ids"].copy()
-    return tokens
-
-dataset = dataset.map(tokenize, batched=True, remove_columns=dataset["train"].column_names)
-
-# ====== 4. Load Base Model ======
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# ====== 5. Apply LoRA ======
-lora_config = LoraConfig(
-    r=8,
-    lora_alpha=16,
-    target_modules=["q_proj", "v_proj"],
-    lora_dropout=0.1,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
-model = get_peft_model(model, lora_config)
-
-# ====== 6. Data Collator ======
-data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-
-# ====== 7. Training Args ======
-args = TrainingArguments(
-    output_dir="./lora_tmp",
-    per_device_train_batch_size=2,
-    gradient_accumulation_steps=4,
-    warmup_steps=50,
-    max_steps=1000,
-    learning_rate=2e-4,
-    fp16=True,
-    logging_steps=10,
-    save_strategy="no"  # We'll save after merging
-)
-
-# ====== 8. Trainer ======
-trainer = Trainer(
-    model=model,
-    args=args,
-    train_dataset=dataset["train"],
-    data_collator=data_collator
-)
-
-# ====== 9. Train ======
-trainer.train()
-
-# ====== 10. Merge LoRA into full model ======
-print("Merging LoRA weights into the base model...")
-model = merge_lora_weights(model)
-
-# ====== 11. Save Full Model ======
-model.save_pretrained(output_dir)
-tokenizer.save_pretrained(output_dir)
-
-print(f"✅ Full model saved at {output_dir}")
-
-# ====== 12. (Optional) Push to Hugging Face Hub ======
-# Uncomment to push
-# model.push_to_hub("hari7261/TechChat", use_temp_dir=False)
-# tokenizer.push_to_hub("hari7261/TechChat", use_temp_dir=False)