add SFT script with LFM2.5-1.2B

README.md

@@ -21,6 +21,21 @@ UV scripts for fine-tuning LLMs and VLMs using [Unsloth](https://github.com/unsl
 
 ## Data Format
 
+### LLM Fine-tuning (SFT)
+
+Requires conversation data in ShareGPT or similar format:
+
+```python
+{
+    "messages": [
+        {"from": "human", "value": "What is the capital of France?"},
+        {"from": "gpt", "value": "The capital of France is Paris."}
+    ]
+}
+```
+
+The script auto-converts common formats (ShareGPT, Alpaca, etc.) via `standardize_data_formats`. See [mlabonne/FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) for a working dataset example.
+
 ### VLM Fine-tuning
 
 Requires `images` and `messages` columns:
@@ -63,7 +78,21 @@ Use `--text-column` if your column has a different name.
 View available options for any script:
 
 ```bash
-uv run https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-qwen3-vl.py --help
+uv run https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-lfm2.5.py --help
+```
+
+### LLM fine-tuning (recommended starting point)
+
+Fine-tune [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct), a compact and efficient text model from Liquid AI:
+
+```bash
+hf jobs uv run \
+  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-lfm2.5.py \
+  --flavor a10g-small --secrets HF_TOKEN --timeout 4h \
+  -- --dataset mlabonne/FineTome-100k \
+     --num-epochs 1 \
+     --eval-split 0.2 \
+     --output-repo your-username/lfm-finetuned
 ```
 
 ### VLM fine-tuning
@@ -94,17 +123,18 @@ hf jobs uv run \
 
 ```bash
 hf jobs uv run \
-  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-qwen3-vl.py \
-  --flavor a100-large --secrets HF_TOKEN \
-  -- --dataset your-username/dataset \
+  https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-lfm2.5.py \
+  --flavor a10g-small --secrets HF_TOKEN \
+  -- --dataset mlabonne/FineTome-100k \
      --trackio-space your-username/trackio \
-     --output-repo your-username/my-model
+     --output-repo your-username/lfm-finetuned
 ```
 
 ## Scripts
 
 | Script | Base Model | Task |
 |--------|------------|------|
+| [`sft-lfm2.5.py`](sft-lfm2.5.py) ⭐ | LFM2.5-1.2B-Instruct | LLM fine-tuning (recommended) |
 | [`sft-qwen3-vl.py`](sft-qwen3-vl.py) | Qwen3-VL-8B | VLM fine-tuning |
 | [`sft-gemma3-vlm.py`](sft-gemma3-vlm.py) | Gemma 3 4B | VLM fine-tuning (smaller) |
 | [`continued-pretraining.py`](continued-pretraining.py) | Qwen3-0.6B | Domain adaptation |

sft-lfm2.5.py

@@ -0,0 +1,512 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "unsloth",
+#     "datasets",
+#     "trl==0.22.2",
+#     "huggingface_hub[hf_transfer]",
+#     "trackio",
+#     "tensorboard",
+#     "transformers==4.57.3",
+# ]
+# ///
+"""
+Fine-tune LFM2.5-1.2B-Instruct (Liquid Foundation Model) using Unsloth optimizations.
+
+Uses Unsloth for ~60% less VRAM and 2x faster training.
+Supports epoch-based or step-based training with optional eval split.
+
+Epoch-based training (recommended for full datasets):
+    uv run sft-lfm2.5.py \
+        --dataset mlabonne/FineTome-100k \
+        --num-epochs 1 \
+        --eval-split 0.2 \
+        --output-repo your-username/lfm-finetuned
+
+Run on HF Jobs (1 epoch with eval):
+    hf jobs uv run sft-lfm2.5.py \
+        --flavor a10g-small --secrets HF_TOKEN --timeout 4h \
+        -- --dataset mlabonne/FineTome-100k \
+           --num-epochs 1 \
+           --eval-split 0.2 \
+           --output-repo your-username/lfm-finetuned
+
+Step-based training (for quick tests):
+    uv run sft-lfm2.5.py \
+        --dataset mlabonne/FineTome-100k \
+        --max-steps 500 \
+        --output-repo your-username/lfm-finetuned
+"""
+
+import argparse
+import logging
+import os
+import sys
+import time
+
+# Force unbuffered output for HF Jobs logs
+sys.stdout.reconfigure(line_buffering=True)
+sys.stderr.reconfigure(line_buffering=True)
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+def check_cuda():
+    """Check CUDA availability and exit if not available."""
+    import torch
+
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("Run on a machine with a CUDA-capable GPU or use HF Jobs:")
+        logger.error(
+            "  hf jobs uv run sft-lfm2.5.py --flavor a10g-small ..."
+        )
+        sys.exit(1)
+    logger.info(f"CUDA available: {torch.cuda.get_device_name(0)}")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Fine-tune LFM2.5-1.2B-Instruct with Unsloth",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Quick test run
+  uv run sft-lfm2.5.py \\
+      --dataset mlabonne/FineTome-100k \\
+      --max-steps 50 \\
+      --output-repo username/lfm-test
+
+  # Full training with eval
+  uv run sft-lfm2.5.py \\
+      --dataset mlabonne/FineTome-100k \\
+      --num-epochs 1 \\
+      --eval-split 0.2 \\
+      --output-repo username/lfm-finetuned
+
+  # With Trackio monitoring
+  uv run sft-lfm2.5.py \\
+      --dataset mlabonne/FineTome-100k \\
+      --num-epochs 1 \\
+      --output-repo username/lfm-finetuned \\
+      --trackio-space username/trackio
+        """,
+    )
+
+    # Model and data
+    parser.add_argument(
+        "--base-model",
+        default="LiquidAI/LFM2.5-1.2B-Instruct",
+        help="Base model (default: LiquidAI/LFM2.5-1.2B-Instruct)",
+    )
+    parser.add_argument(
+        "--dataset",
+        required=True,
+        help="Dataset in ShareGPT/conversation format (e.g., mlabonne/FineTome-100k)",
+    )
+    parser.add_argument(
+        "--output-repo",
+        required=True,
+        help="HF Hub repo to push model to (e.g., 'username/lfm-finetuned')",
+    )
+
+    # Training config
+    parser.add_argument(
+        "--num-epochs",
+        type=float,
+        default=None,
+        help="Number of epochs (default: None). Use instead of --max-steps.",
+    )
+    parser.add_argument(
+        "--max-steps",
+        type=int,
+        default=None,
+        help="Training steps (default: None). Use for quick tests or streaming.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=2,
+        help="Per-device batch size (default: 2)",
+    )
+    parser.add_argument(
+        "--gradient-accumulation",
+        type=int,
+        default=4,
+        help="Gradient accumulation steps (default: 4). Effective batch = batch-size * this",
+    )
+    parser.add_argument(
+        "--learning-rate",
+        type=float,
+        default=2e-4,
+        help="Learning rate (default: 2e-4)",
+    )
+    parser.add_argument(
+        "--max-seq-length",
+        type=int,
+        default=2048,
+        help="Maximum sequence length (default: 2048)",
+    )
+
+    # LoRA config
+    parser.add_argument(
+        "--lora-r",
+        type=int,
+        default=16,
+        help="LoRA rank (default: 16). Higher = more capacity but more VRAM",
+    )
+    parser.add_argument(
+        "--lora-alpha",
+        type=int,
+        default=16,
+        help="LoRA alpha (default: 16). Same as r per Unsloth recommendation",
+    )
+
+    # Logging
+    parser.add_argument(
+        "--trackio-space",
+        default=None,
+        help="HF Space for Trackio dashboard (e.g., 'username/trackio')",
+    )
+    parser.add_argument(
+        "--run-name",
+        default=None,
+        help="Custom run name for Trackio (default: auto-generated)",
+    )
+    parser.add_argument(
+        "--save-local",
+        default="lfm-output",
+        help="Local directory to save model (default: lfm-output)",
+    )
+
+    # Evaluation and data control
+    parser.add_argument(
+        "--eval-split",
+        type=float,
+        default=0.0,
+        help="Fraction of data for evaluation (0.0-0.5). Default: 0.0 (no eval)",
+    )
+    parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=None,
+        help="Limit samples (default: None = use all)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=3407,
+        help="Random seed for reproducibility (default: 3407)",
+    )
+    parser.add_argument(
+        "--merge-model",
+        action="store_true",
+        default=False,
+        help="Merge LoRA weights into base model before uploading (larger file, easier to use)",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    # Validate epochs/steps configuration
+    if not args.num_epochs and not args.max_steps:
+        args.num_epochs = 1
+        logger.info("Using default --num-epochs=1")
+
+    # Determine training duration display
+    if args.num_epochs:
+        duration_str = f"{args.num_epochs} epoch(s)"
+    else:
+        duration_str = f"{args.max_steps} steps"
+
+    print("=" * 70)
+    print("LFM2.5-1.2B Fine-tuning with Unsloth")
+    print("=" * 70)
+    print("\nConfiguration:")
+    print(f"  Base model:      {args.base_model}")
+    print(f"  Dataset:         {args.dataset}")
+    print(f"  Num samples:     {args.num_samples or 'all'}")
+    print(f"  Eval split:      {args.eval_split if args.eval_split > 0 else '(disabled)'}")
+    print(f"  Seed:            {args.seed}")
+    print(f"  Training:        {duration_str}")
+    print(f"  Batch size:      {args.batch_size} x {args.gradient_accumulation} = {args.batch_size * args.gradient_accumulation}")
+    print(f"  Learning rate:   {args.learning_rate}")
+    print(f"  LoRA rank:       {args.lora_r}")
+    print(f"  Max seq length:  {args.max_seq_length}")
+    print(f"  Output repo:     {args.output_repo}")
+    print(f"  Trackio space:   {args.trackio_space or '(not configured)'}")
+    print()
+
+    # Check CUDA before heavy imports
+    check_cuda()
+
+    # Enable fast transfers
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+    # Set Trackio space if provided
+    if args.trackio_space:
+        os.environ["TRACKIO_SPACE_ID"] = args.trackio_space
+        logger.info(f"Trackio dashboard: https://huggingface.co/spaces/{args.trackio_space}")
+
+    # Import heavy dependencies
+    from unsloth import FastLanguageModel
+    from unsloth.chat_templates import standardize_data_formats, train_on_responses_only
+    from datasets import load_dataset
+    from trl import SFTTrainer, SFTConfig
+    from huggingface_hub import login
+
+    # Login to Hub
+    token = os.environ.get("HF_TOKEN") or os.environ.get("hfjob")
+    if token:
+        login(token=token)
+        logger.info("Logged in to Hugging Face Hub")
+    else:
+        logger.warning("HF_TOKEN not set - model upload may fail")
+
+    # 1. Load model
+    print("\n[1/5] Loading model...")
+    start = time.time()
+
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=args.base_model,
+        max_seq_length=args.max_seq_length,
+        load_in_4bit=False,
+        load_in_8bit=False,
+        load_in_16bit=True,
+        full_finetuning=False,
+    )
+
+    # Add LoRA adapters with LFM-specific target modules
+    model = FastLanguageModel.get_peft_model(
+        model,
+        r=args.lora_r,
+        target_modules=["q_proj", "k_proj", "v_proj", "out_proj", "in_proj", "w1", "w2", "w3"],
+        lora_alpha=args.lora_alpha,
+        lora_dropout=0,
+        bias="none",
+        use_gradient_checkpointing="unsloth",
+        random_state=args.seed,
+        use_rslora=False,
+        loftq_config=None,
+    )
+    print(f"Model loaded in {time.time() - start:.1f}s")
+
+    # 2. Load and prepare dataset
+    print("\n[2/5] Loading dataset...")
+    start = time.time()
+
+    dataset = load_dataset(args.dataset, split="train")
+    print(f"  Dataset has {len(dataset)} total samples")
+
+    if args.num_samples:
+        dataset = dataset.select(range(min(args.num_samples, len(dataset))))
+        print(f"  Limited to {len(dataset)} samples")
+
+    # Auto-detect and normalize conversation column
+    for col in ["messages", "conversations", "conversation"]:
+        if col in dataset.column_names and isinstance(dataset[0][col], list):
+            if col != "conversations":
+                dataset = dataset.rename_column(col, "conversations")
+            break
+    dataset = standardize_data_formats(dataset)
+
+    # Apply chat template
+    def formatting_prompts_func(examples):
+        texts = tokenizer.apply_chat_template(
+            examples["conversations"],
+            tokenize=False,
+            add_generation_prompt=False,
+        )
+        # Remove BOS token to avoid duplicates
+        return {"text": [x.removeprefix(tokenizer.bos_token) for x in texts]}
+
+    dataset = dataset.map(formatting_prompts_func, batched=True)
+
+    # Split for evaluation if requested
+    if args.eval_split > 0:
+        split = dataset.train_test_split(test_size=args.eval_split, seed=args.seed)
+        train_data = split["train"]
+        eval_data = split["test"]
+        print(f"  Train: {len(train_data)} samples, Eval: {len(eval_data)} samples")
+    else:
+        train_data = dataset
+        eval_data = None
+
+    print(f"  Dataset ready in {time.time() - start:.1f}s")
+
+    # 3. Configure trainer
+    print("\n[3/5] Configuring trainer...")
+
+    # Calculate steps per epoch for logging/eval intervals
+    effective_batch = args.batch_size * args.gradient_accumulation
+    num_samples = len(train_data)
+    steps_per_epoch = num_samples // effective_batch
+
+    # Determine run name and logging steps
+    if args.run_name:
+        run_name = args.run_name
+    elif args.num_epochs:
+        run_name = f"lfm2.5-sft-{args.num_epochs}ep"
+    else:
+        run_name = f"lfm2.5-sft-{args.max_steps}steps"
+
+    if args.num_epochs:
+        logging_steps = max(1, steps_per_epoch // 10)
+        save_steps = max(1, steps_per_epoch // 4)
+    else:
+        logging_steps = max(1, args.max_steps // 20)
+        save_steps = max(1, args.max_steps // 4)
+
+    # Determine reporting backend
+    if args.trackio_space:
+        report_to = ["tensorboard", "trackio"]
+    else:
+        report_to = ["tensorboard"]
+
+    training_config = SFTConfig(
+        output_dir=args.save_local,
+        dataset_text_field="text",
+        per_device_train_batch_size=args.batch_size,
+        gradient_accumulation_steps=args.gradient_accumulation,
+        warmup_steps=5,
+        num_train_epochs=args.num_epochs if args.num_epochs else 1,
+        max_steps=args.max_steps if args.max_steps else -1,
+        learning_rate=args.learning_rate,
+        logging_steps=logging_steps,
+        optim="adamw_8bit",
+        weight_decay=0.01,
+        lr_scheduler_type="linear",
+        seed=args.seed,
+        max_length=args.max_seq_length,
+        report_to=report_to,
+        run_name=run_name,
+        push_to_hub=True,
+        hub_model_id=args.output_repo,
+        save_steps=save_steps,
+        save_total_limit=3,
+    )
+
+    # Add evaluation config if eval is enabled
+    if eval_data:
+        if args.num_epochs:
+            training_config.eval_strategy = "epoch"
+            print("  Evaluation enabled: every epoch")
+        else:
+            training_config.eval_strategy = "steps"
+            training_config.eval_steps = max(1, args.max_steps // 5)
+            print(f"  Evaluation enabled: every {training_config.eval_steps} steps")
+
+    trainer = SFTTrainer(
+        model=model,
+        tokenizer=tokenizer,
+        train_dataset=train_data,
+        eval_dataset=eval_data,
+        args=training_config,
+    )
+
+    # Train on responses only (mask user inputs)
+    trainer = train_on_responses_only(
+        trainer,
+        instruction_part="<|im_start|>user\n",
+        response_part="<|im_start|>assistant\n",
+    )
+
+    # 4. Train
+    print(f"\n[4/5] Training for {duration_str}...")
+    if args.num_epochs:
+        print(f"  (~{steps_per_epoch} steps/epoch, {int(steps_per_epoch * args.num_epochs)} total steps)")
+    start = time.time()
+
+    train_result = trainer.train()
+
+    train_time = time.time() - start
+    total_steps = train_result.metrics.get("train_steps", args.max_steps or steps_per_epoch * args.num_epochs)
+    print(f"\nTraining completed in {train_time / 60:.1f} minutes")
+    print(f"  Speed: {total_steps / train_time:.2f} steps/s")
+
+    # Print training metrics
+    train_loss = train_result.metrics.get("train_loss")
+    if train_loss:
+        print(f"  Final train loss: {train_loss:.4f}")
+
+    # Print eval results if eval was enabled
+    if eval_data:
+        print("\nRunning final evaluation...")
+        try:
+            eval_results = trainer.evaluate()
+            eval_loss = eval_results.get("eval_loss")
+            if eval_loss:
+                print(f"  Final eval loss: {eval_loss:.4f}")
+                if train_loss:
+                    ratio = eval_loss / train_loss
+                    if ratio > 1.5:
+                        print(f"  Warning: Eval loss is {ratio:.1f}x train loss - possible overfitting")
+                    else:
+                        print(f"  Eval/train ratio: {ratio:.2f} - model generalizes well")
+        except Exception as e:
+            print(f"  Warning: Final evaluation failed: {e}")
+            print("  Continuing to save model...")
+
+    # 5. Save and push
+    print("\n[5/5] Saving model...")
+
+    if args.merge_model:
+        print("Merging LoRA weights into base model...")
+        print(f"\nPushing merged model to {args.output_repo}...")
+        model.push_to_hub_merged(
+            args.output_repo,
+            tokenizer=tokenizer,
+            save_method="merged_16bit",
+        )
+        print(f"Merged model available at: https://huggingface.co/{args.output_repo}")
+    else:
+        model.save_pretrained(args.save_local)
+        tokenizer.save_pretrained(args.save_local)
+        print(f"Saved locally to {args.save_local}/")
+
+        print(f"\nPushing adapter to {args.output_repo}...")
+        model.push_to_hub(args.output_repo, tokenizer=tokenizer)
+        print(f"Adapter available at: https://huggingface.co/{args.output_repo}")
+
+    print("\n" + "=" * 70)
+    print("Done!")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 1:
+        print("=" * 70)
+        print("LFM2.5-1.2B Fine-tuning with Unsloth")
+        print("=" * 70)
+        print("\nFine-tune Liquid Foundation Model with optional train/eval split.")
+        print("\nFeatures:")
+        print("  - ~60% less VRAM with Unsloth optimizations")
+        print("  - 2x faster training vs standard methods")
+        print("  - Epoch-based or step-based training")
+        print("  - Optional evaluation to detect overfitting")
+        print("  - Trains only on assistant responses (masked user inputs)")
+        print("\nEpoch-based training:")
+        print("\n  uv run sft-lfm2.5.py \\")
+        print("      --dataset mlabonne/FineTome-100k \\")
+        print("      --num-epochs 1 \\")
+        print("      --eval-split 0.2 \\")
+        print("      --output-repo your-username/lfm-finetuned")
+        print("\nHF Jobs example:")
+        print("\n  hf jobs uv run sft-lfm2.5.py \\")
+        print("      --flavor a10g-small --secrets HF_TOKEN --timeout 4h \\")
+        print("      -- --dataset mlabonne/FineTome-100k \\")
+        print("         --num-epochs 1 \\")
+        print("         --eval-split 0.2 \\")
+        print("         --output-repo your-username/lfm-finetuned")
+        print("\nFor full help: uv run sft-lfm2.5.py --help")
+        print("=" * 70)
+        sys.exit(0)
+
+    main()