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finetune_soc.py

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+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "datasets>=2.0.0",
+#     "trackio",
+# ]
+# ///
+
+"""
+Fine-tune swiss-ai/Apertus-8B-2509 on marcodsn/SOC-2508 (Synthetic Online Conversations).
+
+Preserves the full multi-participant chat structure: each conversation is formatted
+as ChatML with custom roles (persona usernames) rather than collapsing to user/assistant.
+Loss is computed on ALL tokens so the model learns every participant's voice.
+"""
+
+import trackio
+from datasets import load_dataset
+from peft import LoraConfig
+from transformers import AutoTokenizer
+from trl import SFTConfig, SFTTrainer
+
+MODEL_ID = "swiss-ai/Apertus-8B-2509"
+DATASET_ID = "marcodsn/SOC-2508"
+OUTPUT_REPO = "Colby/apertus-8b-soc"
+
+print("📦 Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+print("📦 Loading dataset...")
+dataset = load_dataset(DATASET_ID, split="train")
+print(f"✅ Loaded {len(dataset)} conversations")
+
+
+def format_conversation(example):
+    """
+    Convert a SOC conversation to a ChatML text string for training.
+
+    Structure:
+      - system turn: full persona bios, relationship, and situation context
+      - one turn per chat_parts entry, role = sender's username, content = all messages joined
+
+    Using apply_chat_template + dataset_text_field trains on all tokens (all participants),
+    which is correct for multi-participant chat — there is no single "assistant" role.
+    """
+    exp = example["experience"]
+    p1, p2 = exp["persona1"], exp["persona2"]
+    id_to_username = {
+        p1["id"]: p1["username"],
+        p2["id"]: p2["username"],
+    }
+
+    system_content = (
+        f"Participants:\n"
+        f"- {p1['name']} (@{p1['username']}, age {p1['age']}): {p1['background']} "
+        f"Chatting style: {p1['chatting_style']}\n"
+        f"- {p2['name']} (@{p2['username']}, age {p2['age']}): {p2['background']} "
+        f"Chatting style: {p2['chatting_style']}\n"
+        f"Relationship: {exp['relationship']}\n"
+        f"Situation: {exp['situation']}"
+    )
+
+    messages = [{"role": "system", "content": system_content}]
+    for turn in example["chat_parts"]:
+        username = id_to_username.get(turn["sender"], turn["sender"])
+        content = "\n".join(turn["messages"])
+        messages.append({"role": username, "content": content})
+
+    text = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=False
+    )
+    return {"text": text}
+
+
+print("🔀 Formatting conversations to ChatML...")
+dataset = dataset.map(format_conversation, remove_columns=dataset.column_names)
+split = dataset.train_test_split(test_size=0.05, seed=42)
+train_dataset = split["train"]
+eval_dataset = split["test"]
+print(f"   Train: {len(train_dataset)}  Eval: {len(eval_dataset)}")
+
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules="all-linear",
+)
+
+config = SFTConfig(
+    # Hub push — ephemeral environment, must push or results are lost
+    output_dir="apertus-8b-soc",
+    push_to_hub=True,
+    hub_model_id=OUTPUT_REPO,
+    hub_strategy="every_save",
+
+    # Train on ALL tokens (all participant voices, not just "assistant")
+    dataset_text_field="text",
+    max_length=2048,
+
+    # Hyperparameters
+    num_train_epochs=2,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=8,  # effective batch = 16
+    learning_rate=2e-4,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.05,
+    bf16=True,
+    gradient_checkpointing=True,
+
+    # Checkpointing
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=2,
+    eval_strategy="steps",
+    eval_steps=100,
+
+    # Monitoring
+    report_to="trackio",
+    project="apertus-soc-finetune",
+    run_name="apertus-8b-soc-v1",
+)
+
+print("🎯 Initializing trainer...")
+trainer = SFTTrainer(
+    model=MODEL_ID,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=peft_config,
+    args=config,
+)
+
+print("🚀 Starting training...")
+trainer.train()
+
+print("💾 Pushing to Hub...")
+trainer.push_to_hub()
+trackio.finish()
+
+print(f"✅ Done! Model at: https://huggingface.co/{OUTPUT_REPO}")
+print(f"📊 Metrics at: https://huggingface.co/spaces/Colby/trackio")