Fix all ruff lint errors and failing test

- Fix 23 ruff errors: line too long, unused imports, ambiguous vars
- Fix validator: empty string is valid data, only count None as empty
- Remove unused imports in display.py and validator.py
- Rename ambiguous `l` vars to `part`, `entry`, `length`
- Break long lines in callback.py, display.py, sft.py, constants.py

All 20 tests passing, ruff clean.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

soup_cli/commands/data.py

@@ -57,7 +57,10 @@ def inspect(
 @app.command()
 def validate(
     path: str = typer.Argument(..., help="Path to dataset file"),
-    format: str = typer.Option("alpaca", "--format", "-f", help="Expected format: alpaca, sharegpt, chatml"),
+    format: str = typer.Option(
+        "alpaca", "--format", "-f",
+        help="Expected format: alpaca, sharegpt, chatml",
+    ),
 ):
     """Validate dataset format and report issues."""
     file_path = Path(path)
@@ -75,4 +78,6 @@ def validate(
     else:
         console.print("[bold green]Dataset is valid![/]")
 
-    console.print(f"\n[green]{stats['valid_rows']}/{stats['total']} rows valid for {format} format[/]")
+    valid = stats["valid_rows"]
+    total = stats["total"]
+    console.print(f"\n[green]{valid}/{total} rows valid for {format} format[/]")

soup_cli/commands/init.py

@@ -64,7 +64,9 @@ def _interactive_wizard() -> str:
     )
     task = Prompt.ask("Task", choices=["sft", "dpo"], default="sft")
     data_path = Prompt.ask("Training data path", default="./data/train.jsonl")
-    data_format = Prompt.ask("Data format", choices=["alpaca", "sharegpt", "chatml"], default="alpaca")
+    data_format = Prompt.ask(
+        "Data format", choices=["alpaca", "sharegpt", "chatml"], default="alpaca",
+    )
     epochs = Prompt.ask("Epochs", default="3")
     use_qlora = Prompt.ask("Use QLoRA (4-bit)?", choices=["yes", "no"], default="yes")
 

soup_cli/config/loader.py

@@ -24,7 +24,7 @@ def load_config(path: Path) -> SoupConfig:
     except ValidationError as e:
         console.print("[red bold]Config validation error:[/]\n")
         for err in e.errors():
-            loc = " → ".join(str(l) for l in err["loc"])
+            loc = " → ".join(str(part) for part in err["loc"])
             console.print(f"  [red]{loc}:[/] {err['msg']}")
         raise SystemExit(1)
 

soup_cli/data/validator.py

@@ -1,7 +1,5 @@
 """Dataset validation and statistics."""
 
-from collections import Counter
-
 from soup_cli.data.formats import FORMAT_SIGNATURES
 
 
@@ -29,7 +27,7 @@ def validate_and_stats(data: list[dict], expected_format: str | None = None) ->
         text = " ".join(str(v) for v in row.values() if v)
         lengths.append(len(text))
         for v in row.values():
-            if v is None or (isinstance(v, str) and not v.strip()):
+            if v is None:
                 empty_count += 1
 
     # Detect duplicates by stringifying rows
@@ -57,7 +55,7 @@ def validate_and_stats(data: list[dict], expected_format: str | None = None) ->
         issues.append(f"{empty_count} empty fields found")
 
     # Check for very short samples
-    short = sum(1 for l in lengths if l < 10)
+    short = sum(1 for length in lengths if length < 10)
     if short > 0:
         issues.append(f"{short} samples are very short (<10 chars)")
 

soup_cli/monitoring/callback.py

@@ -1,6 +1,11 @@
 """HuggingFace Trainer callback that feeds metrics to our display."""
 
-from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
 
 from soup_cli.monitoring.display import TrainingDisplay
 
@@ -11,10 +16,16 @@ class SoupTrainerCallback(TrainerCallback):
     def __init__(self, display: TrainingDisplay):
         self.display = display
 
-    def on_train_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+    def on_train_begin(
+        self, args: TrainingArguments, state: TrainerState,
+        control: TrainerControl, **kwargs,
+    ):
         self.display.start(total_steps=state.max_steps)
 
-    def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, logs=None, **kwargs):
+    def on_log(
+        self, args: TrainingArguments, state: TrainerState,
+        control: TrainerControl, logs=None, **kwargs,
+    ):
         if logs is None:
             return
 
@@ -40,5 +51,8 @@ class SoupTrainerCallback(TrainerCallback):
             gpu_mem=gpu_mem,
         )
 
-    def on_train_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+    def on_train_end(
+        self, args: TrainingArguments, state: TrainerState,
+        control: TrainerControl, **kwargs,
+    ):
         self.display.stop()

soup_cli/monitoring/display.py

@@ -3,9 +3,6 @@
 from rich.console import Console
 from rich.live import Live
 from rich.panel import Panel
-from rich.progress import BarColumn, Progress, TextColumn, TimeRemainingColumn
-from rich.table import Table
-from rich.text import Text
 
 from soup_cli.config.schema import SoupConfig
 
@@ -54,13 +51,18 @@ class TrainingDisplay:
 
     def _render(self) -> Panel:
         """Render the dashboard panel."""
-        progress_pct = (self.current_step / self.total_steps * 100) if self.total_steps > 0 else 0
+        if self.total_steps > 0:
+            progress_pct = self.current_step / self.total_steps * 100
+        else:
+            progress_pct = 0
         bar_width = 30
         filled = int(bar_width * progress_pct / 100)
-        bar = "█" * filled + "░" * (bar_width - filled)
+        bar = "\u2588" * filled + "\u2591" * (bar_width - filled)
 
+        epochs = self.config.training.epochs
+        epoch_str = f"Epoch {self.current_epoch:.1f}/{epochs}"
         lines = []
-        lines.append(f"Epoch {self.current_epoch:.1f}/{self.config.training.epochs}  [{bar}] {progress_pct:.0f}%")
+        lines.append(f"{epoch_str}  [{bar}] {progress_pct:.0f}%")
         lines.append(f"Step:  {self.current_step}/{self.total_steps}")
         lines.append(f"Loss:  {self.loss:.4f}    LR: {self.lr:.2e}")
 
@@ -72,9 +74,10 @@ class TrainingDisplay:
             lines.append(f"Grad:  {self.grad_norm:.4f}")
 
         content = "\n".join(lines)
+        name = self.config.experiment_name or self.config.base
         return Panel(
             content,
-            title=f"[bold green]Soup Training: {self.config.experiment_name or self.config.base}[/]",
+            title=f"[bold green]Soup Training: {name}[/]",
             subtitle=f"[dim]{self.device_name}[/]",
             border_style="green",
         )

soup_cli/trainer/sft.py

@@ -84,7 +84,10 @@ class SFTTrainerWrapper:
         self.model = get_peft_model(self.model, lora_config)
         trainable, total = self.model.get_nb_trainable_parameters()
         pct = 100 * trainable / total
-        console.print(f"[green]LoRA applied:[/] {trainable:,} trainable / {total:,} total ({pct:.2f}%)")
+        console.print(
+            f"[green]LoRA applied:[/] {trainable:,} trainable"
+            f" / {total:,} total ({pct:.2f}%)"
+        )
 
         # --- Batch size ---
         batch_size = tcfg.batch_size
@@ -161,7 +164,7 @@ class SFTTrainerWrapper:
 
             self.trainer.add_callback(SoupTrainerCallback(display))
 
-        result = self.trainer.train()
+        self.trainer.train()
         duration = time.time() - start
 
         # Save final model (LoRA adapter)
@@ -170,7 +173,7 @@ class SFTTrainerWrapper:
 
         # Extract metrics
         logs = self.trainer.state.log_history
-        train_losses = [l["loss"] for l in logs if "loss" in l]
+        train_losses = [entry["loss"] for entry in logs if "loss" in entry]
 
         hours = int(duration // 3600)
         minutes = int((duration % 3600) // 60)

soup_cli/utils/constants.py

@@ -5,4 +5,13 @@ CONFIG_FILE = "soup.yaml"
 SOUP_DIR = ".soup"
 EXPERIMENTS_DB = "experiments.db"
 
-DEFAULT_CHAT_TEMPLATE = """{% for message in messages %}{% if message['role'] == 'system' %}{{ message['content'] + '\\n' }}{% elif message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + '\\n' }}{% endif %}{% endfor %}"""
+DEFAULT_CHAT_TEMPLATE = (
+    "{% for message in messages %}"
+    "{% if message['role'] == 'system' %}"
+    "{{ message['content'] + '\\n' }}"
+    "{% elif message['role'] == 'user' %}"
+    "{{ 'User: ' + message['content'] + '\\n' }}"
+    "{% elif message['role'] == 'assistant' %}"
+    "{{ 'Assistant: ' + message['content'] + '\\n' }}"
+    "{% endif %}{% endfor %}"
+)

tests/conftest.py

@@ -19,9 +19,21 @@ def sample_alpaca_data(tmp_data_dir: Path) -> Path:
     """Create a sample alpaca-format JSONL file."""
     path = tmp_data_dir / "train.jsonl"
     samples = [
-        {"instruction": "What is Python?", "input": "", "output": "Python is a programming language."},
-        {"instruction": "Explain gravity", "input": "", "output": "Gravity is a fundamental force."},
-        {"instruction": "Translate hello to Spanish", "input": "hello", "output": "hola"},
+        {
+            "instruction": "What is Python?",
+            "input": "",
+            "output": "Python is a programming language.",
+        },
+        {
+            "instruction": "Explain gravity",
+            "input": "",
+            "output": "Gravity is a fundamental force.",
+        },
+        {
+            "instruction": "Translate hello to Spanish",
+            "input": "hello",
+            "output": "hola",
+        },
     ]
     with open(path, "w", encoding="utf-8") as f:
         for s in samples: