Upload train_alizee_coder.py with huggingface_hub

train_alizee_coder.py

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+# /// script
+# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "datasets", "transformers>=4.45.0", "accelerate", "bitsandbytes", "torch"]
+# ///
+
+import os
+import torch
+from datasets import load_dataset
+from peft import LoraConfig, TaskType
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import trackio
+
+print("="*50)
+print("Starting Alizee Coder Devstral Training")
+print("="*50)
+
+# Configuration
+MODEL_NAME = "mistralai/Devstral-Small-2505"
+OUTPUT_REPO = "stmasson/alizee-coder-devstral-1-small"
+DATASET_SIZE = 10000
+
+# Verify HF_TOKEN
+if not os.environ.get("HF_TOKEN"):
+    raise ValueError("HF_TOKEN not set!")
+print("HF_TOKEN verified")
+
+print(f"Loading dataset nvidia/OpenCodeReasoning...")
+try:
+    dataset = load_dataset("nvidia/OpenCodeReasoning", split="split_0")
+    dataset = dataset.shuffle(seed=42).select(range(min(DATASET_SIZE, len(dataset))))
+    print(f"Dataset loaded: {len(dataset)} examples")
+except Exception as e:
+    print(f"Error loading dataset: {e}")
+    raise
+
+# Split train/eval
+dataset_split = dataset.train_test_split(test_size=0.05, seed=42)
+train_dataset = dataset_split["train"]
+eval_dataset = dataset_split["test"]
+print(f"Train: {len(train_dataset)}, Eval: {len(eval_dataset)}")
+
+# Format for code reasoning
+def format_example(example):
+    solution = example.get('solution', '') or ''
+    output = example.get('output', '') or ''
+    text = f"<s>[INST] Solve this programming problem with detailed reasoning:\n\n{example['input']}\n[/INST]\n\n**Reasoning:**\n{output}\n\n**Solution:**\n```python\n{solution}\n```</s>"
+    return {"text": text}
+
+print("Formatting dataset...")
+train_dataset = train_dataset.map(format_example, remove_columns=train_dataset.column_names)
+eval_dataset = eval_dataset.map(format_example, remove_columns=eval_dataset.column_names)
+print("Dataset formatted")
+
+# Load tokenizer
+print(f"Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+print("Tokenizer loaded")
+
+# 4-bit quantization
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+print(f"Loading model {MODEL_NAME}...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+)
+print("Model loaded")
+
+# LoRA configuration
+lora_config = LoraConfig(
+    r=32,
+    lora_alpha=64,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type=TaskType.CAUSAL_LM,
+)
+
+# Training config
+training_config = SFTConfig(
+    output_dir="./alizee-coder-devstral-1-small",
+    num_train_epochs=1,
+    per_device_train_batch_size=1,
+    per_device_eval_batch_size=1,
+    gradient_accumulation_steps=16,
+    gradient_checkpointing=True,
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.1,
+    max_length=4096,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=200,
+    eval_strategy="steps",
+    eval_steps=200,
+    bf16=True,
+    push_to_hub=True,
+    hub_model_id=OUTPUT_REPO,
+    hub_strategy="every_save",
+    report_to="trackio",
+    run_name="alizee-coder-devstral-1-small",
+)
+
+print("Initializing trainer...")
+trainer = SFTTrainer(
+    model=model,
+    args=training_config,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=lora_config,
+    processing_class=tokenizer,
+)
+
+print("="*50)
+print("STARTING TRAINING")
+print("="*50)
+trainer.train()
+
+print("Pushing to Hub...")
+trainer.push_to_hub()
+print(f"Done! Model: https://huggingface.co/{OUTPUT_REPO}")