Create App.py

App.py

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+# =============================================================================
+# Full-Parameter SFT: Qwen/Qwen3.5-4B-Base on Bc-AI/SFT-Ultra
+# On-the-fly streaming | Bad row filtering | HF Hub upload
+# =============================================================================
+# Requirements:
+
+#   pip install torch transformers datasets trl accelerate huggingface_hub
+
+import os
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import SFTTrainer, SFTConfig
+from huggingface_hub import HfApi, login
+
+# =============================================================================
+# 0. CONFIG — Edit these as needed
+# =============================================================================
+MODEL_ID        = "Qwen/Qwen3.5-4B"
+DATASET_ID      = "Bc-AI/SFT-Ultra"
+OUTPUT_DIR      = "./qwen3.5-4b-full-sft"
+
+# HF Hub — paste your token when prompted at runtime
+HF_REPO_ID      = "Bc-AI/qwen3.5-4b-sft"   # ← change this
+HF_TOKEN        = ""    # Leave None — you will be prompted to paste it below
+
+# Sequence
+MAX_SEQ_LENGTH  = 2048
+
+# Training hyperparameters
+NUM_TRAIN_EPOCHS              = 1
+PER_DEVICE_TRAIN_BATCH_SIZE   = 2
+GRADIENT_ACCUMULATION_STEPS   = 8
+LEARNING_RATE                 = 1e-5
+WEIGHT_DECAY                  = 0.01
+WARMUP_RATIO                  = 0.05
+LR_SCHEDULER                  = "cosine"
+MAX_GRAD_NORM                 = 1.0
+
+# Logging & saving
+LOGGING_STEPS    = 10
+SAVE_STEPS       = 500
+SAVE_TOTAL_LIMIT = 3
+
+# Precision — bf16 on Ampere+ (A100, 3090, 4090)
+#             set bf16=False fp16=True on older GPUs (V100, T4)
+USE_BF16 = True
+USE_FP16 = False
+
+# Streaming = on-the-fly download, no full disk pre-cache
+STREAM_DATASET = True
+
+SEED = 42
+
+# =============================================================================
+# 1. HF HUB LOGIN  — paste token here at runtime
+# =============================================================================
+print("=" * 60)
+print("  Hugging Face Hub Login")
+print("=" * 60)
+
+if HF_TOKEN is None:
+    HF_TOKEN = input("  Paste your HF token (hf_…): ").strip()
+
+login(token=HF_TOKEN, add_to_git_credential=False)
+print("  ✅ Logged in successfully.\n")
+
+# =============================================================================
+# 2. TOKENIZER
+# =============================================================================
+print(f"[1/4] Loading tokenizer: {MODEL_ID}")
+
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+tokenizer.padding_side = "right"
+
+# =============================================================================
+# 3. MODEL — full bf16, no quantisation, no adapter
+# =============================================================================
+print(f"[2/4] Loading full model: {MODEL_ID}")
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16 if USE_BF16 else torch.float32,
+    device_map="auto",
+)
+
+model.config.use_cache = False
+
+for param in model.parameters():
+    param.requires_grad = True
+
+total_params = sum(p.numel() for p in model.parameters())
+print(f"   Trainable parameters: {total_params:,}  ({total_params / 1e9:.2f}B)")
+
+# =============================================================================
+# 4. DATASET — streaming + bad row filtering + on-the-fly tokenisation
+# =============================================================================
+print(f"[3/4] Loading dataset: {DATASET_ID}  (streaming={STREAM_DATASET})")
+
+raw_dataset = load_dataset(
+    DATASET_ID,
+    split="train",
+    streaming=STREAM_DATASET,
+    trust_remote_code=True,
+)
+
+# ── Bad row validator ────────────────────────────────────────────────────────
+# Catches every known failure mode so no single row can crash training
+
+REQUIRED_ROLES = {"user", "assistant"}   # Minimum roles a valid convo must have
+
+def is_valid_row(example):
+    """
+    Returns True only if the row is safe to train on.
+    Filters out:
+      - Missing / non-list messages field
+      - Empty message list
+      - Messages with missing role or content keys
+      - Messages where role or content is not a string
+      - Messages where content is an empty / whitespace-only string
+      - Conversations missing at least one user AND one assistant turn
+      - Rows where the entire rendered text would be empty
+    """
+    try:
+        messages = example.get("messages", None)
+
+        # Must exist and be a non-empty list
+        if not isinstance(messages, list) or len(messages) == 0:
+            return False
+
+        seen_roles = set()
+        for msg in messages:
+            # Each message must be a dict
+            if not isinstance(msg, dict):
+                return False
+
+            role    = msg.get("role",    None)
+            content = msg.get("content", None)
+
+            # role and content must be non-empty strings
+            if not isinstance(role, str) or not role.strip():
+                return False
+            if not isinstance(content, str) or not content.strip():
+                return False
+
+            seen_roles.add(role.strip().lower())
+
+        # Must have at least one user turn and one assistant turn
+        if not REQUIRED_ROLES.issubset(seen_roles):
+            return False
+
+        return True
+
+    except Exception:
+        # Catch-all: any unexpected structure is silently dropped
+        return False
+
+
+# ── Safe formatter ───────────────────────────────────────────────────────────
+
+def format_messages(example):
+    """
+    Applies the Qwen3.5 chat template.
+    Wrapped in try/except so any template rendering failure is handled
+    gracefully — the row is marked with an empty text field and later dropped.
+    """
+    try:
+        text = tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+            add_generation_prompt=False,
+        )
+        # Final safety: rendered text must be non-trivial
+        if not text or not text.strip():
+            return {"text": ""}
+        return {"text": text}
+    except Exception:
+        return {"text": ""}
+
+
+def is_non_empty_text(example):
+    """Drop any row where formatting produced an empty string."""
+    text = example.get("text", "")
+    return isinstance(text, str) and len(text.strip()) > 0
+
+
+# ── Apply pipeline ───────────────────────────────────────────────────────────
+print("   Step 1 — Filtering malformed rows …")
+clean_dataset = raw_dataset.filter(is_valid_row)
+
+print("   Step 2 — Applying chat template on the fly …")
+formatted_dataset = clean_dataset.map(format_messages)
+
+print("   Step 3 — Dropping any rows with empty rendered text …")
+formatted_dataset = formatted_dataset.filter(is_non_empty_text)
+
+print("   ✅ Dataset pipeline ready.\n")
+
+# =============================================================================
+# 5. TRAINER
+# =============================================================================
+print("[4/4] Configuring SFTTrainer …")
+
+sft_config = SFTConfig(
+    # ── Output ───────────────────────────────────────────────────────────────
+    output_dir=OUTPUT_DIR,
+
+    # ── Sequence ─────────────────────────────────────────────────────────────
+    max_seq_length=MAX_SEQ_LENGTH,
+
+    # ── Training schedule ────────────────────────────────────────────────────
+    num_train_epochs=NUM_TRAIN_EPOCHS,
+    per_device_train_batch_size=PER_DEVICE_TRAIN_BATCH_SIZE,
+    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
+    learning_rate=LEARNING_RATE,
+    weight_decay=WEIGHT_DECAY,
+    warmup_ratio=WARMUP_RATIO,
+    lr_scheduler_type=LR_SCHEDULER,
+    max_grad_norm=MAX_GRAD_NORM,
+
+    # ── Optimizer ────────────────────────────────────────────────────────────
+    optim="adamw_torch_fused",
+
+    # ── Precision ────────────────────────────────────────────────────────────
+    bf16=USE_BF16,
+    fp16=USE_FP16,
+
+    # ── Gradient checkpointing ───────────────────────────────────────────────
+    gradient_checkpointing=True,
+    gradient_checkpointing_kwargs={"use_reentrant": False},
+
+    # ── Loss ─────────────────────────────────────────────────────────────────
+    completion_only_loss=True,
+
+    # ── Dataset ──────────────────────────────────────────────────────────────
+    dataset_text_field="text",
+    dataset_num_proc=1,             # Must be 1 for IterableDataset (streaming)
+
+    # ── Logging & checkpointing ──────────────────────────────────────────────
+    logging_steps=LOGGING_STEPS,
+    save_steps=SAVE_STEPS,
+    save_total_limit=SAVE_TOTAL_LIMIT,
+    report_to="none",               # Swap to "wandb" or "tensorboard" if needed
+
+    # ── Misc ─────────────────────────────────────────────────────────────────
+    seed=SEED,
+    remove_unused_columns=True,
+)
+
+trainer = SFTTrainer(
+    model=model,
+    args=sft_config,
+    train_dataset=formatted_dataset,
+    tokenizer=tokenizer,
+)
+
+# =============================================================================
+# 6. TRAIN
+# =============================================================================
+print("\n🚀  Starting full fine-tuning …\n")
+trainer.train()
+
+# =============================================================================
+# 7. SAVE LOCALLY
+# =============================================================================
+print(f"\n💾  Saving full model + tokenizer to: {OUTPUT_DIR}")
+trainer.save_model(OUTPUT_DIR)
+tokenizer.save_pretrained(OUTPUT_DIR)
+print("   ✅ Local save complete.\n")
+
+# =============================================================================
+# 8. PUSH TO HF HUB
+# =============================================================================
+print(f"☁️   Uploading to Hugging Face Hub: {HF_REPO_ID}")
+print("    (This may take a while depending on your upload speed …)\n")
+
+try:
+    # Push model
+    model.push_to_hub(
+        HF_REPO_ID,
+        token=HF_TOKEN,
+        commit_message="Full SFT — Qwen3.5-4B-Base on Bc-AI/SFT-Ultra",
+        private=True,              # Set False if you want a public repo
+    )
+
+    # Push tokenizer
+    tokenizer.push_to_hub(
+        HF_REPO_ID,
+        token=HF_TOKEN,
+        commit_message="Add tokenizer",
+    )
+
+    # Push a minimal model card so the repo is well-documented
+    api = HfApi()
+    model_card = f"""---
+language:
+- en
+license: apache-2.0
+base_model: {MODEL_ID}
+datasets:
+- {DATASET_ID}
+tags:
+- full-fine-tune
+- sft
+- qwen3.5
+---
+
+# Qwen3.5-4B — Full SFT
+
+- **Base model:** `{MODEL_ID}`
+- **Dataset:** `{DATASET_ID}`
+- **Training type:** Full parameter supervised fine-tuning (no LoRA)
+- **Max sequence length:** {MAX_SEQ_LENGTH}
+- **Epochs:** {NUM_TRAIN_EPOCHS}
+- **Learning rate:** {LEARNING_RATE}
+- **Precision:** {"bf16" if USE_BF16 else "fp16"}
+"""
+    api.upload_file(
+        path_or_fileobj=model_card.encode("utf-8"),
+        path_in_repo="README.md",
+        repo_id=HF_REPO_ID,
+        token=HF_TOKEN,
+        commit_message="Add model card",
+    )
+
+    print(f"\n✅  Model successfully uploaded to: https://huggingface.co/{HF_REPO_ID}")
+
+except Exception as e:
+    print(f"\n❌  Upload failed: {e}")
+    print(f"    Your model is still saved locally at: {OUTPUT_DIR}")
+    print("    You can retry the upload manually with:")
+    print(f"      model.push_to_hub('{HF_REPO_ID}')")
+    print(f"      tokenizer.push_to_hub('{HF_REPO_ID}')")
+
+print("\n✅  All done!")