Upload train_qwen35_27b.py with huggingface_hub

train_qwen35_27b.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""
+OPUS-CANDID V2 — QWEN3.5-27B (DENSE)
+Uses FastLanguageModel (dense model, per Unsloth docs).
+Same safety fixes as MoE script: num_proc=1, bf16, dropout=0.
+Needs ~56GB VRAM — runs on H200 or A100 80GB.
+"""
+import os, json, torch, random
+
+print("=" * 60)
+print("OPUS-CANDID V2 — QWEN3.5-27B (DENSE)")
+print("=" * 60)
+
+if torch.cuda.is_available():
+    gpu = torch.cuda.get_device_name(0)
+    vram = torch.cuda.get_device_properties(0).total_memory / 1024**3
+    print(f"GPU: {gpu} | VRAM: {vram:.1f} GB")
+    if vram < 55:
+        print("WARNING: This model needs ~56GB VRAM. You may OOM.")
+
+# === Config ===
+MODEL       = "unsloth/Qwen3.5-27B"
+MAX_SEQ     = 8192
+LORA_R      = 16
+LORA_ALPHA  = 16
+LR          = 1e-5
+EPOCHS      = 2
+BATCH       = 1
+GRAD_ACCUM  = 16
+OUTPUT      = "/workspace/opus_candid_qwen35_27b_lora"
+DATASET     = "/workspace/opus_candid_v2_dataset.json"
+
+# === Dataset ===
+print(f"\nLoading {DATASET}...")
+with open(DATASET) as f:
+    data = json.load(f)
+random.seed(42)
+random.shuffle(data)
+split = max(1, int(len(data) * 0.02))
+eval_data, train_data = data[:split], data[split:]
+print(f"Total: {len(data)} | Train: {len(train_data)} | Eval: {len(eval_data)}")
+
+# === Model — FastLanguageModel for dense ===
+print(f"\nLoading {MODEL}...")
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=MODEL,
+    max_seq_length=MAX_SEQ,
+    load_in_4bit=False,
+    load_in_16bit=True,
+    full_finetuning=False,
+)
+
+# === LoRA ===
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=LORA_R,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                     "gate_proj", "up_proj", "down_proj"],
+    lora_alpha=LORA_ALPHA,
+    lora_dropout=0,
+    bias="none",
+    use_gradient_checkpointing="unsloth",
+    random_state=3407,
+    max_seq_length=MAX_SEQ,
+)
+
+# === Format dataset ===
+from datasets import Dataset
+
+def fmt(examples):
+    texts = []
+    for convos in examples["conversations"]:
+        msgs = []
+        for m in convos:
+            role = "user" if m.get("from") == "human" else "assistant"
+            content = m.get("value") or m.get("content") or ""
+            if content:
+                msgs.append({"role": role, "content": content})
+        if msgs:
+            texts.append(tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False))
+        else:
+            texts.append("")
+    return {"text": texts}
+
+train_ds = Dataset.from_list(train_data).map(fmt, batched=True, remove_columns=["conversations"])
+eval_ds  = Dataset.from_list(eval_data).map(fmt, batched=True, remove_columns=["conversations"])
+print(f"Formatted: train {len(train_ds)} | eval {len(eval_ds)}")
+
+# === Train ===
+from trl import SFTTrainer, SFTConfig
+
+steps = (len(train_ds) * EPOCHS) // (BATCH * GRAD_ACCUM)
+warmup = max(1, int(steps * 0.05))
+
+print(f"\n{'='*60}")
+print(f"TRAINING: {EPOCHS}ep | bs {BATCH}x{GRAD_ACCUM}={BATCH*GRAD_ACCUM} | lr {LR}")
+print(f"Steps: ~{steps} | Warmup: {warmup}")
+print(f"{'='*60}")
+
+trainer = SFTTrainer(
+    model=model, tokenizer=tokenizer,
+    train_dataset=train_ds, eval_dataset=eval_ds,
+    args=SFTConfig(
+        max_seq_length=MAX_SEQ,
+        per_device_train_batch_size=BATCH,
+        gradient_accumulation_steps=GRAD_ACCUM,
+        warmup_steps=warmup,
+        num_train_epochs=EPOCHS,
+        learning_rate=LR,
+        lr_scheduler_type="cosine",
+        logging_steps=5,
+        eval_strategy="steps",
+        eval_steps=50,
+        save_strategy="steps",
+        save_steps=50,
+        output_dir=OUTPUT,
+        optim="adamw_8bit",
+        bf16=True,
+        seed=3407,
+        dataset_num_proc=1,
+    ),
+)
+
+trainer.train()
+
+# === Save ===
+loss = trainer.state.log_history[-1].get("train_loss", "N/A")
+print(f"\nDONE — Final loss: {loss}")
+
+model.save_pretrained(OUTPUT)
+tokenizer.save_pretrained(OUTPUT)
+
+with open(os.path.join(OUTPUT, "training_stats.json"), "w") as f:
+    json.dump({"model": MODEL, "dataset_size": len(train_data), "epochs": EPOCHS,
+               "lora_r": LORA_R, "lora_alpha": LORA_ALPHA, "learning_rate": LR,
+               "batch_size": BATCH * GRAD_ACCUM, "max_seq_length": MAX_SEQ,
+               "final_loss": loss, "log_history": trainer.state.log_history}, f, indent=2)
+
+print(f"Adapters: {OUTPUT}")