progress

* refactor: improve output_dir handling in generate_config_files

* fix typo

* cli: harden sweep output_dir handling with base fallback

- Ensure sweep permutations always resolve a valid output_dir
- Default to ./model-out if neither permutation nor base config sets output_dir
- Append sweepXXXX suffix consistently for each permutation
- Prevent Path(None) TypeError and improve robustness of sweep config generation

* fix typo

* chore: lint

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

.coderabbit.yaml

@@ -12,5 +12,6 @@ reviews:
   auto_review:
     enabled: true
     drafts: false
+    auto_incremental_review: true
 chat:
   auto_reply: true

examples/gpt-oss/README.md

@@ -41,6 +41,12 @@ model, and final model output, you may need at least 3TB of free disk space to k
 axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
 ```
 
+To simplify fine-tuning across 2 nodes × 8x H100 (80GB) GPUs, we've partnered with [Baseten](https://baseten.co) to showcase multi-node
+training of the 120B model using Baseten Truss. You can read more about this recipe on
+[Baseten's blog](https://www.baseten.co/blog/how-to-fine-tune-gpt-oss-120b-with-baseten-and-axolotl/). The recipe can
+be found on their
+[GitHub](https://github.com/basetenlabs/ml-cookbook/tree/main/examples/oss-gpt-120b-axolotl/training).
+
 ERRATA: Transformers saves the model Architecture prefixed with `FSDP` which needs to be manually renamed in `config.json`.
 See https://github.com/huggingface/transformers/pull/40207 for the status of this issue.
 
@@ -61,9 +67,23 @@ mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
 
 ### Inferencing your fine-tuned model
 
+#### vLLM
+
 GPT-OSS support in vLLM does not exist in a stable release yet. See https://x.com/MaziyarPanahi/status/1955741905515323425
 for more information about using a special vllm-openai docker image for inferencing with vLLM.
 
+Optionally, vLLM can be installed from nightly:
+
+```bash
+pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
+```
+and the vLLM server can be started with the following command (modify `--tensor-parallel-size 8` to match your environment):
+```bash
+vllm serve ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-20b --host 0.0.0.0 --port 8888  --tensor-parallel-size 8
+```
+
+#### SGLang
+
 SGLang has 0-day support in main, see https://github.com/sgl-project/sglang/issues/8833 for infomation on installing
 SGLang from source. Once you've installed SGLang, run the following command to launch a SGLang server:
 

examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml

@@ -44,7 +44,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-deepspeed-zero3.yaml

@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -41,7 +41,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -53,7 +53,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

requirements.txt

@@ -72,3 +72,8 @@ axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.5
 
 mistral-common==1.8.3
+
+# TUI dependencies
+textual==1.0.0
+rich==14.1.0
+tree_sitter_ruby==0.23.1

setup.py

@@ -118,9 +118,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.8.2"],
+    "flash-attn": ["flash-attn==2.8.3"],
     "ring-flash-attn": [
-        "flash-attn==2.8.2",
+        "flash-attn==2.8.3",
         "ring-flash-attn>=0.1.7",
         "yunchang==0.6.0",
     ],

src/axolotl/cli/cloud/modal_.py

@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu124-2.6.0"
+        docker_tag = "main-py3.11-cu126-2.7.1"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"
@@ -200,7 +200,7 @@ class ModalCloud(Cloud):
         if family in ["a10", "a10g"]:
             return modal.gpu.A10G(count=count)
         if family == "h100":
-            return modal.gpu.H100(count=count)
+            return f"H100:{count}"
         if family == "t4":
             return modal.gpu.T4(count=count)
         if family == "l4":

src/axolotl/cli/inference.py

@@ -64,7 +64,7 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
     elif cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer

src/axolotl/cli/main.py

@@ -344,6 +344,26 @@ def delinearize_llama4(model: str, output: str):
 cli.add_command(lm_eval)
 
 
+@cli.command()
+def tui():
+    """
+    Launch the Axolotl Terminal User Interface (TUI).
+
+    Provides an interactive interface for configuration management,
+    training monitoring, dataset handling, and model operations.
+    """
+    try:
+        from axolotl.tui.app import run
+
+        run()
+    except ImportError:
+        click.echo(
+            "TUI dependencies not installed. Install with: pip install textual rich"
+        )
+    except Exception as e:
+        click.echo(f"Error launching TUI: {e}")
+
+
 def main():
     cli()
 

src/axolotl/cli/preprocess.py

@@ -97,7 +97,8 @@ def do_cli(
     """
     # pylint: disable=duplicate-code
     os.environ["AXOLOTL_IS_PREPROCESS"] = "1"
-    parsed_cfg = load_cfg(config, **kwargs)
+    is_preprocess = kwargs.pop("is_preprocess", True)
+    parsed_cfg = load_cfg(config, is_preprocess=is_preprocess, **kwargs)
     parsed_cfg.is_preprocess = True
     parser = transformers.HfArgumentParser(PreprocessCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/utils/sweeps.py

@@ -3,11 +3,12 @@
 import random
 from copy import deepcopy
 from itertools import product
+from typing import Any
 
 
 def generate_sweep_configs(
     base_config: dict[str, list], sweeps_config: dict[str, list]
-) -> list[dict[str, list]]:
+) -> list[dict[str, Any]]:
     """
     Recursively generates all possible configurations by applying sweeps to the base config.
 

src/axolotl/cli/utils/train.py

@@ -4,6 +4,7 @@ import os
 import subprocess  # nosec
 import sys
 import tempfile
+from pathlib import Path
 from typing import Any, Iterator, Literal
 
 import yaml
@@ -88,7 +89,12 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str,
     # Generate all possible configurations
     permutations = generate_sweep_configs(base_config, sweep_config)
     is_group = len(permutations) > 1
-    for permutation in permutations:
+    base_output_dir = base_config.get("output_dir", "./model-out")
+    for idx, permutation in enumerate(permutations, start=1):
+        permutation_dir = Path(permutation.get("output_dir", base_output_dir))
+        permutation_id = f"sweep{idx:04d}"
+        permutation["output_dir"] = str(permutation_dir / permutation_id)
+
         # pylint: disable=consider-using-with
         temp_file = tempfile.NamedTemporaryFile(
             mode="w",

src/axolotl/core/builders/causal.py

@@ -40,7 +40,6 @@ from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     MambaDataCollator,
-    StreamingDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
@@ -423,17 +422,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         is_eval=False,
         **kwargs,
     ):
-        from datasets import IterableDataset
-
-        if isinstance(self.train_dataset, IterableDataset) and not is_eval:
-            LOG.info("Using StreamingDataCollator")
-            return StreamingDataCollator(
-                tokenizer=self.tokenizer,
-                cfg=self.cfg,
-                prompter=None,
-                **kwargs,
-            )
-
         if training_args.pretraining:
             if (
                 self.cfg.pretraining_sample_concatenation is False

src/axolotl/datasets.py

@@ -43,11 +43,7 @@ class TokenizedPromptDataset(Dataset):
         )
 
     def process(self, dataset):
-        # For IterableDataset, we can't access features upfront
-        # We'll need to infer from the first batch
-        features = None
-        if hasattr(dataset, "features") and dataset.features:
-            features = dataset.features.keys()
+        features = dataset.features.keys()
 
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
@@ -58,29 +54,18 @@ class TokenizedPromptDataset(Dataset):
             hasattr(self.prompt_tokenizer, "filter_rows")
             and self.prompt_tokenizer.filter_rows
         ):
-            filter_kwargs = {"desc": "Strategy Filtering Rows"}
-            # Only add num_proc for regular datasets
-            if features is not None:
-                filter_kwargs["num_proc"] = self.process_count
-
             dataset = dataset.filter(
                 self.prompt_tokenizer.filter_rows,
-                **filter_kwargs,
+                num_proc=self.process_count,
+                desc="Strategy Filtering Rows",
             )
 
-        map_kwargs = {
-            **map_kwargs,
-            "desc": "Tokenizing Prompts",
-        }
-
-        # Only add remove_columns for regular datasets
-        if features is not None:
-            map_kwargs["remove_columns"] = features
-            map_kwargs["num_proc"] = self.process_count
-            map_kwargs["keep_in_memory"] = self.keep_in_memory
-
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
+            num_proc=self.process_count,
+            remove_columns=features,
+            keep_in_memory=self.keep_in_memory,
+            desc="Tokenizing Prompts",
             **map_kwargs,
         )
 

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -187,7 +187,7 @@ def _process_lora_module_for_fsdp(module, fsdp2_kwargs):
 
     # Linear4Bit will keep it's bias term in fp32. If the weight dtype is in bf16 we are not able to
     # wrap this. Therefore we must ensure the bias has the same dtype as the weight
-    if module.base_layer.bias is not None:
+    if hasattr(module.base_layer, "bias") and module.base_layer.bias is not None:
         if module.base_layer.weight.dtype != module.base_layer.bias.dtype:
             log_bias_dtype_mismatch = True
             module.base_layer.bias.data = module.base_layer.bias.data.to(

src/axolotl/prompt_strategies/messages/chat.py

@@ -72,9 +72,10 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         builder_kwargs["message_field_training"] = message_field_training
 
     chat_template = ds_cfg.get("chat_template", cfg.get("chat_template", "chatml"))
-    format_message = (
-        lambda x: x  # noqa E731  # pylint: disable=unnecessary-lambda-assignment
-    )
+
+    def format_message(x):
+        return x
+
     if chat_template == "chatml":
         from axolotl.core.chat.format.chatml import format_message  # noqa F811
     if chat_template.startswith("llama3"):

src/axolotl/train.py

@@ -253,7 +253,9 @@ def save_trained_model(
             # final model weights have already been saved by `ReLoRACallback.on_train_end`
             return
 
-    if trainer.is_fsdp_enabled or cfg.fsdp_config:
+    if (  # pylint: disable=too-many-nested-blocks
+        trainer.is_fsdp_enabled or cfg.fsdp_config
+    ):
         if cfg.fsdp_config or cfg.fsdp:
             if cfg.fsdp_config.final_state_dict_type:
                 state_dict_type = cfg.fsdp_config.final_state_dict_type
@@ -285,6 +287,8 @@ def save_trained_model(
                 if trainer.accelerator.is_main_process:
                     # move all files in merged_path to cfg.output_dir
                     for merged_file in Path(merged_path).iterdir():
+                        if (Path(cfg.output_dir) / merged_file.name).exists():
+                            (Path(cfg.output_dir) / merged_file.name).unlink()
                         shutil.move(str(merged_file), cfg.output_dir)
                     shutil.rmtree(merged_path)  # remove what should be an empty dir
         # TODO(wing):see https://github.com/huggingface/transformers/pull/40207

src/axolotl/tui/README.md

@@ -0,0 +1,216 @@
+# Axolotl TUI (Terminal User Interface)
+
+A comprehensive Terminal User Interface for Axolotl, providing an interactive way to manage configurations, training jobs, datasets, models, and system monitoring.
+
+## Features
+
+### 🏠 Main Dashboard
+- **Welcome Screen**: Central hub with quick access to all features
+- **Keyboard Navigation**: Efficient navigation with keyboard shortcuts
+- **Screen Management**: Easy switching between different functional areas
+
+### 📝 Configuration Management
+- **YAML Editor**: Syntax-highlighted editor for Axolotl configurations
+- **Real-time Validation**: Instant config validation with detailed error reporting
+- **File Browser**: Navigate and select configuration files
+- **Template Loading**: Load example configurations
+- **Remote Config Support**: Load configurations from URLs
+
+**Key Shortcuts:**
+- `Ctrl+N`: New configuration
+- `Ctrl+S`: Save configuration
+- `Ctrl+V`: Validate configuration
+- `Ctrl+E`: Toggle edit mode
+
+### 🚀 Training Management
+- **Job Launcher**: Start training with different launchers (accelerate, torchrun)
+- **Real-time Monitoring**: Live training progress and metrics
+- **Loss Visualization**: Sparkline charts for loss curves
+- **Job Control**: Start, stop, resume, and manage multiple training jobs
+- **Log Streaming**: Real-time log viewing and filtering
+
+**Key Shortcuts:**
+- `Ctrl+T`: New training job
+- `Ctrl+R`: Resume training
+- `Ctrl+X`: Stop training
+- `R`: Refresh status
+
+### 📊 Dataset Management
+- **Dataset Browser**: Explore local and remote datasets
+- **Preview & Statistics**: View dataset samples and metadata
+- **Preprocessing**: Run dataset preprocessing with progress tracking
+- **HuggingFace Integration**: Download and manage HF datasets
+- **Format Detection**: Automatic dataset format recognition
+
+**Key Shortcuts:**
+- `Ctrl+P`: Preprocess dataset
+- `Ctrl+V`: Preview dataset
+- `Ctrl+I`: Dataset information
+- `R`: Refresh dataset list
+
+### 🤖 Model Management
+- **Model Discovery**: Automatically find trained models
+- **LoRA Operations**: Merge LoRA adapters with base models
+- **Quantization**: Quantize models for deployment
+- **Evaluation**: Run model evaluation benchmarks
+- **Storage Info**: View model sizes and storage details
+
+**Key Shortcuts:**
+- `Ctrl+M`: Merge LoRA
+- `Ctrl+Q`: Quantize model
+- `Ctrl+E`: Evaluate model
+- `R`: Refresh model list
+
+### 💬 Inference & Testing
+- **Interactive Chat**: Chat interface for model testing
+- **Parameter Tuning**: Adjust inference parameters (temperature, top-p, max tokens)
+- **Model Loading**: Load and switch between different models
+- **Chat History**: Save and load conversation history
+- **Gradio Integration**: Launch Gradio web interface
+
+**Key Shortcuts:**
+- `Ctrl+Enter`: Send message
+- `Ctrl+C`: Clear chat
+- `Ctrl+L`: Load model
+- `Ctrl+S`: Save chat
+
+### 📈 System Monitoring
+- **Resource Monitoring**: Real-time CPU, GPU, and memory usage
+- **Process Management**: View and manage running processes
+- **Performance Graphs**: Historical usage charts with sparklines
+- **GPU Information**: Detailed GPU status and memory usage
+- **Temperature Monitoring**: System temperature tracking
+
+**Key Shortcuts:**
+- `R`: Refresh metrics
+- `Ctrl+K`: Kill selected process
+
+## Installation
+
+### Dependencies
+```bash
+pip install textual==1.0.0 rich==14.1.0
+```
+
+### Launch TUI
+```bash
+# From command line
+python -m axolotl.cli.main tui
+
+# From Python code
+from axolotl.tui.app import run
+run()
+```
+
+## Architecture
+
+### Screen Structure
+```
+AxolotlTUI (Main App)
+├── WelcomeScreen (Dashboard)
+├── ConfigScreen (Configuration Management)
+├── TrainingScreen (Training Management)
+├── DatasetScreen (Dataset Management)
+├── ModelScreen (Model Management)
+├── InferenceScreen (Inference & Testing)
+└── MonitorScreen (System Monitoring)
+```
+
+### Key Components
+- **BaseScreen**: Common functionality for all screens
+- **Screen Navigation**: Stack-based screen management
+- **Event Handling**: Reactive UI updates
+- **Background Tasks**: Non-blocking operations
+- **State Management**: Shared application state
+
+### Integration Points
+- **CLI Commands**: Seamless integration with existing axolotl CLI
+- **Configuration System**: Uses axolotl's native config loading
+- **Training Pipeline**: Integrates with axolotl training functions
+- **Model Loading**: Compatible with axolotl model management
+
+## Usage Examples
+
+### 1. Creating a New Configuration
+1. Launch TUI: `python -m axolotl.cli.main tui`
+2. Select "Configuration Management" or press `C`
+3. Press `Ctrl+N` for new configuration
+4. Edit the template configuration
+5. Press `Ctrl+V` to validate
+6. Press `Ctrl+S` to save
+
+### 2. Starting a Training Job
+1. Navigate to "Training Management" or press `T`
+2. Press `Ctrl+T` for new training job
+3. Select configuration file and launcher
+4. Monitor progress in real-time
+5. View loss curves and logs
+
+### 3. Interactive Model Testing
+1. Go to "Inference & Testing" or press `I`
+2. Load a trained model with `Ctrl+L`
+3. Adjust inference parameters as needed
+4. Start chatting with the model
+5. Save conversation with `Ctrl+S`
+
+## Navigation
+
+### Global Shortcuts
+- `Ctrl+Q`: Quit application
+- `Escape`: Go back/close current screen
+- `Tab`: Navigate between UI elements
+- `Enter`: Select/activate element
+- `Space`: Toggle switches/checkboxes
+
+### Screen Shortcuts
+Each screen has specific shortcuts displayed in the footer. Common patterns:
+- `Ctrl+[Letter]`: Primary actions
+- `R`: Refresh/reload
+- `F1-F12`: Function keys for advanced features
+
+## Customization
+
+### Themes
+The TUI uses Textual's theming system and can be customized by modifying the CSS in each screen class.
+
+### Adding New Screens
+1. Create a new screen class inheriting from `BaseScreen`
+2. Implement the `compose()` method for UI layout
+3. Add event handlers for user interactions
+4. Register the screen in the main app navigation
+
+### Extending Functionality
+- Add new widgets to existing screens
+- Implement custom data visualization
+- Integrate with external tools and APIs
+- Add new keyboard shortcuts
+
+## Troubleshooting
+
+### Common Issues
+1. **Import Errors**: Ensure textual and rich are installed
+2. **Permission Errors**: Check file system permissions for config directories
+3. **GPU Monitoring**: Install pynvml for GPU monitoring features
+4. **Config Validation**: Ensure axolotl dependencies are properly installed
+
+### Debug Mode
+Launch with debug logging:
+```bash
+TEXTUAL_LOG=DEBUG python -m axolotl.cli.main tui
+```
+
+### Performance
+- Use `Ctrl+\` to open Textual's debug console
+- Monitor memory usage with the system monitor
+- Disable auto-refresh for better performance on slower systems
+
+## Contributing
+
+The TUI is designed to be extensible. Contributions are welcome for:
+- New screen implementations
+- Enhanced visualizations
+- Better keyboard navigation
+- Additional integrations
+- Performance improvements
+
+See the main Axolotl repository for contribution guidelines.

src/axolotl/tui/__init__.py

@@ -0,0 +1 @@
+"""Axolotl Terminal User Interface (TUI)."""

src/axolotl/tui/app.py

@@ -0,0 +1,180 @@
+"""Main TUI application for Axolotl."""
+
+from textual import on
+from textual.app import App, ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.screen import Screen
+from textual.widgets import Button, Footer, Header, Static
+
+from axolotl.tui.screens.config import ConfigScreen
+from axolotl.tui.screens.datasets import DatasetScreen
+from axolotl.tui.screens.inference import InferenceScreen
+from axolotl.tui.screens.models import ModelScreen
+from axolotl.tui.screens.monitor import MonitorScreen
+from axolotl.tui.screens.training import TrainingScreen
+
+
+class WelcomeScreen(Screen):
+    """Welcome screen with main menu."""
+
+    BINDINGS = [
+        Binding("q", "quit", "Quit"),
+        Binding("c", "config", "Configuration"),
+        Binding("t", "training", "Training"),
+        Binding("d", "datasets", "Datasets"),
+        Binding("m", "models", "Models"),
+        Binding("i", "inference", "Inference"),
+        Binding("s", "monitor", "System Monitor"),
+    ]
+
+    def compose(self) -> ComposeResult:
+        """Compose the welcome screen."""
+        yield Header()
+        yield Container(
+            Static("🦾 Axolotl TUI", classes="title"),
+            Static(
+                "A Terminal User Interface for fine-tuning LLMs", classes="subtitle"
+            ),
+            Container(
+                Button("Configuration Management [C]", id="config", variant="primary"),
+                Button("Training Management [T]", id="training", variant="primary"),
+                Button("Dataset Management [D]", id="datasets", variant="primary"),
+                Button("Model Management [M]", id="models", variant="primary"),
+                Button("Inference & Testing [I]", id="inference", variant="primary"),
+                Button("System Monitor [S]", id="monitor", variant="primary"),
+                classes="menu-container",
+            ),
+            classes="welcome-container",
+        )
+        yield Footer()
+
+    def action_quit(self) -> None:
+        """Quit the application."""
+        self.app.exit()
+
+    def action_config(self) -> None:
+        """Navigate to config screen."""
+        self.app.push_screen(ConfigScreen())
+
+    def action_training(self) -> None:
+        """Navigate to training screen."""
+        self.app.push_screen(TrainingScreen())
+
+    def action_datasets(self) -> None:
+        """Navigate to datasets screen."""
+        self.app.push_screen(DatasetScreen())
+
+    def action_models(self) -> None:
+        """Navigate to models screen."""
+        self.app.push_screen(ModelScreen())
+
+    def action_inference(self) -> None:
+        """Navigate to inference screen."""
+        self.app.push_screen(InferenceScreen())
+
+    def action_monitor(self) -> None:
+        """Navigate to monitor screen."""
+        self.app.push_screen(MonitorScreen())
+
+    @on(Button.Pressed, "#config")
+    def on_config_pressed(self) -> None:
+        """Handle config button press."""
+        self.action_config()
+
+    @on(Button.Pressed, "#training")
+    def on_training_pressed(self) -> None:
+        """Handle training button press."""
+        self.action_training()
+
+    @on(Button.Pressed, "#datasets")
+    def on_datasets_pressed(self) -> None:
+        """Handle datasets button press."""
+        self.action_datasets()
+
+    @on(Button.Pressed, "#models")
+    def on_models_pressed(self) -> None:
+        """Handle models button press."""
+        self.action_models()
+
+    @on(Button.Pressed, "#inference")
+    def on_inference_pressed(self) -> None:
+        """Handle inference button press."""
+        self.action_inference()
+
+    @on(Button.Pressed, "#monitor")
+    def on_monitor_pressed(self) -> None:
+        """Handle monitor button press."""
+        self.action_monitor()
+
+
+class AxolotlTUI(App):
+    """Main Axolotl TUI Application."""
+
+    CSS = """
+    .title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .subtitle {
+        text-align: center;
+        padding: 1;
+        color: $text-muted;
+    }
+
+    .welcome-container {
+        align: center middle;
+        height: 100%;
+        width: 100%;
+    }
+
+    .menu-container {
+        layout: vertical;
+        align: center middle;
+        padding: 2;
+        width: auto;
+        height: auto;
+    }
+
+    .menu-container Button {
+        width: 35;
+        margin: 1;
+    }
+
+    WelcomeScreen {
+        align: center middle;
+    }
+    """
+
+    BINDINGS = [
+        Binding("ctrl+q", "quit", "Quit", priority=True),
+        Binding("escape", "back", "Back", priority=True),
+    ]
+
+    def on_mount(self) -> None:
+        """Called when the app is mounted."""
+        self.title = "Axolotl TUI"
+        self.sub_title = "Fine-tuning LLMs made easy"
+        self.push_screen(WelcomeScreen())
+
+    def action_quit(self) -> None:
+        """Quit the application."""
+        self.exit()
+
+    def action_back(self) -> None:
+        """Go back to previous screen."""
+        if len(self.screen_stack) > 1:
+            self.pop_screen()
+
+
+def run():
+    """Run the Axolotl TUI application."""
+    app = AxolotlTUI()
+    app.run()
+
+
+if __name__ == "__main__":
+    run()

src/axolotl/tui/dialogs/__init__.py

@@ -0,0 +1 @@
+"""TUI dialogs for Axolotl."""

src/axolotl/tui/dialogs/training.py

@@ -0,0 +1,112 @@
+"""Training dialogs for Axolotl TUI."""
+
+from pathlib import Path
+
+from textual import on
+from textual.app import ComposeResult
+from textual.containers import Container
+from textual.screen import ModalScreen
+from textual.widgets import Button, Input, Label, Select, Static
+
+
+class NewTrainingDialog(ModalScreen):
+    """Dialog for starting a new training job."""
+
+    CSS = """
+    NewTrainingDialog {
+        align: center middle;
+    }
+
+    .dialog-container {
+        background: $surface;
+        border: thick $primary;
+        padding: 2;
+        width: 60;
+        height: auto;
+    }
+
+    .dialog-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .form-field {
+        margin: 1 0;
+    }
+
+    .form-label {
+        margin: 0 0 1 0;
+        color: $text-muted;
+    }
+
+    .button-container {
+        layout: horizontal;
+        align: center middle;
+        margin: 2 0 0 0;
+    }
+
+    .button-container Button {
+        margin: 0 1;
+    }
+    """
+
+    def compose(self) -> ComposeResult:
+        """Compose the dialog."""
+        yield Container(
+            Static("Start New Training Job", classes="dialog-title"),
+            Container(
+                Label("Configuration File:", classes="form-label"),
+                Input(
+                    placeholder="Path to config YAML file",
+                    id="config-path",
+                    value="/workspace/configs/",
+                ),
+                classes="form-field",
+            ),
+            Container(
+                Label("Launcher:", classes="form-label"),
+                Select(
+                    [
+                        ("accelerate", "Accelerate (Recommended)"),
+                        ("torchrun", "TorchRun"),
+                        ("deepspeed", "DeepSpeed"),
+                    ],
+                    id="launcher",
+                    value="accelerate",
+                ),
+                classes="form-field",
+            ),
+            Container(
+                Button("Start Training", variant="primary", id="start"),
+                Button("Cancel", variant="default", id="cancel"),
+                classes="button-container",
+            ),
+            classes="dialog-container",
+        )
+
+    @on(Button.Pressed, "#start")
+    def handle_start(self) -> None:
+        """Handle start button press."""
+        config_input = self.query_one("#config-path", Input)
+        launcher_select = self.query_one("#launcher", Select)
+
+        config_path = config_input.value.strip()
+        if not config_path:
+            return
+
+        if not Path(config_path).exists():
+            return
+
+        result = {
+            "config_path": config_path,
+            "launcher": launcher_select.value,
+        }
+
+        self.dismiss(result)
+
+    @on(Button.Pressed, "#cancel")
+    def handle_cancel(self) -> None:
+        """Handle cancel button press."""
+        self.dismiss(None)

src/axolotl/tui/screens/__init__.py

@@ -0,0 +1 @@
+"""TUI screens for Axolotl."""

src/axolotl/tui/screens/base.py

@@ -0,0 +1,50 @@
+"""Base screen class for Axolotl TUI screens."""
+
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.screen import Screen
+from textual.widgets import Footer, Header, Static
+
+
+class BaseScreen(Screen):
+    """Base class for all Axolotl TUI screens."""
+
+    BINDINGS = [
+        Binding("escape", "back", "Back"),
+        Binding("q", "quit", "Quit"),
+    ]
+
+    def __init__(self, title: str = "Axolotl", subtitle: str = ""):
+        """Initialize the base screen.
+
+        Args:
+            title: The screen title
+            subtitle: Optional subtitle for the screen
+        """
+        super().__init__()
+        self.screen_title = title
+        self.screen_subtitle = subtitle
+
+    def compose(self) -> ComposeResult:
+        """Compose the base screen layout."""
+        yield Header()
+        yield Container(
+            Static(f"🦾 {self.screen_title}", classes="screen-title"),
+            (
+                Static(self.screen_subtitle, classes="screen-subtitle")
+                if self.screen_subtitle
+                else Static("")
+            ),
+            Container(id="content"),
+            id="main-container",
+        )
+        yield Footer()
+
+    def action_back(self) -> None:
+        """Go back to previous screen."""
+        self.app.pop_screen()
+
+    def action_quit(self) -> None:
+        """Quit the application."""
+        self.app.exit()

src/axolotl/tui/screens/config.py

@@ -0,0 +1,376 @@
+"""Configuration management screen for Axolotl TUI."""
+
+import os
+from pathlib import Path
+from typing import Optional
+
+import yaml
+from textual import on, work
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.reactive import reactive
+from textual.widgets import (
+    Button,
+    DirectoryTree,
+    Footer,
+    Header,
+    Label,
+    Log,
+    Static,
+    TextArea,
+)
+
+from axolotl.tui.screens.base import BaseScreen
+
+
+class ConfigScreen(BaseScreen):
+    """Configuration management screen."""
+
+    BINDINGS = [
+        Binding("ctrl+n", "new_config", "New Config"),
+        Binding("ctrl+o", "open_config", "Open Config"),
+        Binding("ctrl+s", "save_config", "Save Config"),
+        Binding("ctrl+v", "validate_config", "Validate"),
+        Binding("ctrl+e", "edit_mode", "Toggle Edit Mode"),
+    ]
+
+    CSS = """
+    .config-container {
+        layout: horizontal;
+        height: 100%;
+    }
+
+    .file-browser {
+        width: 30%;
+        border: solid $primary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .config-editor {
+        width: 70%;
+        border: solid $secondary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .config-form {
+        height: 80%;
+    }
+
+    .config-actions {
+        layout: horizontal;
+        height: 3;
+        align: center middle;
+        padding: 1;
+    }
+
+    .config-actions Button {
+        margin: 0 1;
+    }
+
+    TextArea {
+        height: 100%;
+    }
+
+    .validation-log {
+        height: 20%;
+        border: solid $warning;
+        padding: 1;
+    }
+
+    .screen-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .screen-subtitle {
+        text-align: center;
+        padding: 0 0 1 0;
+        color: $text-muted;
+    }
+    """
+
+    def __init__(self):
+        """Initialize the config screen."""
+        super().__init__(
+            title="Configuration Management",
+            subtitle="Create, edit, and validate Axolotl configurations",
+        )
+        self.current_config_path: Optional[Path] = None
+        self.edit_mode = reactive(False)
+        self.config_data = {}
+
+    def compose(self) -> ComposeResult:
+        """Compose the config screen layout."""
+        yield Header()
+        yield Container(
+            Static("🦾 Configuration Management", classes="screen-title"),
+            Static(
+                "Create, edit, and validate Axolotl configurations",
+                classes="screen-subtitle",
+            ),
+            Container(
+                Container(
+                    Label("Config Files"),
+                    DirectoryTree(
+                        (
+                            Path("/workspace/configs")
+                            if Path("/workspace/configs").exists()
+                            else Path.cwd()
+                        ),
+                        id="config-tree",
+                    ),
+                    classes="file-browser",
+                ),
+                Container(
+                    Container(
+                        TextArea(
+                            "",
+                            language="yaml",
+                            theme="monokai",
+                            id="config-editor",
+                            read_only=True,
+                        ),
+                        classes="config-form",
+                    ),
+                    Container(
+                        Button("New", id="new-config", variant="primary"),
+                        Button("Open", id="open-config", variant="primary"),
+                        Button("Save", id="save-config", variant="success"),
+                        Button("Validate", id="validate-config", variant="warning"),
+                        Button("Edit Mode", id="toggle-edit", variant="default"),
+                        Button("Load Example", id="load-example", variant="default"),
+                        classes="config-actions",
+                    ),
+                    Container(
+                        Log(id="validation-log"),
+                        classes="validation-log",
+                    ),
+                    classes="config-editor",
+                ),
+                classes="config-container",
+            ),
+            id="content",
+        )
+        yield Footer()
+
+    def on_mount(self) -> None:
+        """Called when the screen is mounted."""
+        tree = self.query_one("#config-tree", DirectoryTree)
+        tree.show_root = False
+        tree.guide_depth = 3
+
+        log = self.query_one("#validation-log", Log)
+        log.write_line("Ready to load configuration files...")
+
+    @on(DirectoryTree.FileSelected)
+    def handle_file_selected(self, event: DirectoryTree.FileSelected) -> None:
+        """Handle file selection from the directory tree."""
+        if event.path.suffix in [".yaml", ".yml"]:
+            self.load_config_file(event.path)
+
+    def load_config_file(self, path: Path) -> None:
+        """Load a configuration file."""
+        self.current_config_path = path
+        try:
+            with open(path, "r") as f:
+                content = f.read()
+                self.config_data = yaml.safe_load(content)
+
+            editor = self.query_one("#config-editor", TextArea)
+            editor.load_text(content)
+
+            log = self.query_one("#validation-log", Log)
+            log.clear()
+            log.write_line(f"✅ Loaded: {path.name}")
+
+        except Exception as e:
+            log = self.query_one("#validation-log", Log)
+            log.write_line(f"❌ Error loading {path.name}: {str(e)}")
+
+    @on(Button.Pressed, "#new-config")
+    def handle_new_config(self) -> None:
+        """Create a new configuration."""
+        template = """# Axolotl Configuration
+base_model:
+model_type:
+tokenizer_type:
+
+# Dataset Configuration
+datasets:
+  - path:
+    type:
+
+# Training Configuration
+output_dir: ./outputs
+num_epochs: 3
+micro_batch_size: 1
+gradient_accumulation_steps: 4
+learning_rate: 0.00002
+warmup_steps: 100
+eval_steps: 100
+save_steps: 500
+
+# LoRA Configuration (optional)
+adapter: lora
+lora_r: 8
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+
+# Training optimizations
+gradient_checkpointing: true
+flash_attention: true
+bf16: auto
+tf32: true
+
+# Logging
+logging_steps: 10
+wandb_project:
+wandb_entity:
+"""
+        editor = self.query_one("#config-editor", TextArea)
+        editor.load_text(template)
+        editor.read_only = False
+        self.edit_mode = True
+        self.update_edit_button()
+
+        log = self.query_one("#validation-log", Log)
+        log.clear()
+        log.write_line("📝 New configuration created. Edit and save when ready.")
+
+    @on(Button.Pressed, "#save-config")
+    def handle_save_config(self) -> None:
+        """Save the current configuration."""
+        editor = self.query_one("#config-editor", TextArea)
+        content = editor.text
+
+        if not content.strip():
+            log = self.query_one("#validation-log", Log)
+            log.write_line("⚠️ Cannot save empty configuration")
+            return
+
+        if not self.current_config_path:
+            default_path = Path("/workspace/configs/new_config.yaml")
+            default_path.parent.mkdir(parents=True, exist_ok=True)
+            self.current_config_path = default_path
+
+        try:
+            with open(self.current_config_path, "w") as f:
+                f.write(content)
+
+            log = self.query_one("#validation-log", Log)
+            log.write_line(f"💾 Saved: {self.current_config_path.name}")
+        except Exception as e:
+            log = self.query_one("#validation-log", Log)
+            log.write_line(f"❌ Error saving: {str(e)}")
+
+    @on(Button.Pressed, "#validate-config")
+    @work(thread=True)
+    async def handle_validate_config(self) -> None:
+        """Validate the current configuration."""
+        editor = self.query_one("#config-editor", TextArea)
+        content = editor.text
+
+        if not content.strip():
+            log = self.query_one("#validation-log", Log)
+            log.write_line("⚠️ No configuration to validate")
+            return
+
+        log = self.query_one("#validation-log", Log)
+        log.clear()
+        log.write_line("🔍 Validating configuration...")
+
+        try:
+            import tempfile
+
+            with tempfile.NamedTemporaryFile(
+                mode="w", suffix=".yaml", delete=False
+            ) as f:
+                f.write(content)
+                temp_path = f.name
+
+            from argparse import Namespace
+
+            from axolotl.cli.config import check_user_config
+
+            args = Namespace(
+                config=temp_path,
+                debug=False,
+                debug_text_only=False,
+                debug_num_examples=5,
+                accelerate_config=None,
+                multi_gpu=False,
+            )
+
+            check_user_config(args)
+
+            log.write_line("✅ Configuration is valid!")
+
+            os.unlink(temp_path)
+
+        except Exception as e:
+            log.write_line(f"❌ Validation failed: {str(e)}")
+            if "temp_path" in locals():
+                os.unlink(temp_path)
+
+    @on(Button.Pressed, "#toggle-edit")
+    def handle_toggle_edit(self) -> None:
+        """Toggle edit mode for the configuration."""
+        editor = self.query_one("#config-editor", TextArea)
+        self.edit_mode = not self.edit_mode
+        editor.read_only = not self.edit_mode
+        self.update_edit_button()
+
+        log = self.query_one("#validation-log", Log)
+        if self.edit_mode:
+            log.write_line("✏️ Edit mode enabled")
+        else:
+            log.write_line("👁️ View mode enabled")
+
+    @on(Button.Pressed, "#load-example")
+    async def handle_load_example(self) -> None:
+        """Load an example configuration."""
+        examples_dir = Path("/workspace/axolotl/examples")
+        if not examples_dir.exists():
+            log = self.query_one("#validation-log", Log)
+            log.write_line("⚠️ Examples directory not found")
+            return
+
+        yaml_files = list(examples_dir.glob("**/*.yml")) + list(
+            examples_dir.glob("**/*.yaml")
+        )
+        if yaml_files:
+            self.load_config_file(yaml_files[0])
+            log = self.query_one("#validation-log", Log)
+            log.write_line(f"📚 Loaded example: {yaml_files[0].name}")
+
+    def update_edit_button(self) -> None:
+        """Update the edit button appearance."""
+        button = self.query_one("#toggle-edit", Button)
+        if self.edit_mode:
+            button.variant = "warning"
+            button.label = "Edit Mode: ON"
+        else:
+            button.variant = "default"
+            button.label = "Edit Mode: OFF"
+
+    def action_new_config(self) -> None:
+        """Create a new configuration."""
+        self.handle_new_config()
+
+    def action_save_config(self) -> None:
+        """Save the current configuration."""
+        self.handle_save_config()
+
+    def action_validate_config(self) -> None:
+        """Validate the current configuration."""
+        self.handle_validate_config()
+
+    def action_edit_mode(self) -> None:
+        """Toggle edit mode."""
+        self.handle_toggle_edit()

src/axolotl/tui/screens/datasets.py

@@ -0,0 +1,440 @@
+"""Dataset management screen for Axolotl TUI."""
+
+import json
+from pathlib import Path
+from typing import Dict, Optional
+
+from textual import on, work
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.widgets import (
+    Button,
+    DataTable,
+    Footer,
+    Header,
+    Label,
+    Log,
+    ProgressBar,
+    Static,
+    TextArea,
+)
+
+from axolotl.tui.screens.base import BaseScreen
+
+
+class DatasetScreen(BaseScreen):
+    """Dataset management screen."""
+
+    BINDINGS = [
+        Binding("ctrl+p", "preprocess", "Preprocess"),
+        Binding("ctrl+v", "preview", "Preview"),
+        Binding("ctrl+i", "info", "Info"),
+        Binding("r", "refresh", "Refresh"),
+    ]
+
+    CSS = """
+    .dataset-container {
+        layout: horizontal;
+        height: 100%;
+    }
+
+    .dataset-list {
+        width: 40%;
+        border: solid $primary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .dataset-details {
+        width: 60%;
+        border: solid $secondary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .dataset-actions {
+        layout: horizontal;
+        height: 4;
+        align: center middle;
+        padding: 1;
+    }
+
+    .dataset-actions Button {
+        margin: 0 1;
+    }
+
+    DataTable {
+        height: 100%;
+    }
+
+    .preview-container {
+        height: 100%;
+        border: solid $primary;
+        padding: 1;
+    }
+
+    TextArea {
+        height: 100%;
+    }
+
+    .stats-container {
+        layout: vertical;
+        padding: 1;
+    }
+
+    .stat-row {
+        layout: horizontal;
+        padding: 0 0 1 0;
+    }
+
+    .stat-label {
+        width: 50%;
+        color: $text-muted;
+    }
+
+    .stat-value {
+        width: 50%;
+        text-align: right;
+        text-style: bold;
+    }
+
+    .screen-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .screen-subtitle {
+        text-align: center;
+        padding: 0 0 1 0;
+        color: $text-muted;
+    }
+
+    .progress-container {
+        padding: 1;
+        border: solid $warning;
+        margin: 1;
+    }
+    """
+
+    def __init__(self):
+        """Initialize the dataset screen."""
+        super().__init__(
+            title="Dataset Management",
+            subtitle="Browse, preview, and preprocess datasets",
+        )
+        self.datasets: Dict[str, Dict] = {}
+        self.selected_dataset: Optional[str] = None
+        self.preprocessing_active = False
+
+    def compose(self) -> ComposeResult:
+        """Compose the dataset screen layout."""
+        yield Header()
+        yield Container(
+            Static("🦾 Dataset Management", classes="screen-title"),
+            Static(
+                "Browse, preview, and preprocess datasets", classes="screen-subtitle"
+            ),
+            Container(
+                Container(
+                    Label("Available Datasets"),
+                    DataTable(id="dataset-table"),
+                    Container(
+                        Button("Load Dataset", id="load-dataset", variant="primary"),
+                        Button("Preprocess", id="preprocess", variant="success"),
+                        Button("Download", id="download", variant="default"),
+                        Button("Refresh", id="refresh", variant="default"),
+                        classes="dataset-actions",
+                    ),
+                    classes="dataset-list",
+                ),
+                Container(
+                    TextArea("", id="dataset-preview", read_only=True),
+                    Container(
+                        Static("Dataset Name:", classes="stat-label"),
+                        Static("-", id="stat-name", classes="stat-value"),
+                        Static("Type:", classes="stat-label"),
+                        Static("-", id="stat-type", classes="stat-value"),
+                        Static("Size:", classes="stat-label"),
+                        Static("-", id="stat-size", classes="stat-value"),
+                        Static("Samples:", classes="stat-label"),
+                        Static("-", id="stat-samples", classes="stat-value"),
+                        Static("Features:", classes="stat-label"),
+                        Static("-", id="stat-features", classes="stat-value"),
+                        Static("Format:", classes="stat-label"),
+                        Static("-", id="stat-format", classes="stat-value"),
+                        Static("Preprocessed:", classes="stat-label"),
+                        Static("-", id="stat-preprocessed", classes="stat-value"),
+                    ),
+                    Log(id="processing-log"),
+                    ProgressBar(total=100, id="preprocessing-progress"),
+                    classes="dataset-details",
+                ),
+                classes="dataset-container",
+            ),
+            id="content",
+        )
+        yield Footer()
+
+    def on_mount(self) -> None:
+        """Called when the screen is mounted."""
+        self.setup_dataset_table()
+        self.load_datasets()
+
+        log = self.query_one("#processing-log", Log)
+        log.write_line("Dataset manager ready.")
+
+    def setup_dataset_table(self) -> None:
+        """Setup the dataset table."""
+        table = self.query_one("#dataset-table", DataTable)
+        table.add_columns("Name", "Type", "Size", "Status")
+        table.cursor_type = "row"
+        table.zebra_stripes = True
+
+    @work(thread=True)
+    async def load_datasets(self) -> None:
+        """Load available datasets."""
+        # Check for local datasets
+        datasets_dir = Path("/workspace/datasets")
+        if datasets_dir.exists():
+            for dataset_path in datasets_dir.glob("*"):
+                if dataset_path.is_dir():
+                    self.datasets[dataset_path.name] = {
+                        "name": dataset_path.name,
+                        "path": str(dataset_path),
+                        "type": "local",
+                        "size": self.get_dir_size(dataset_path),
+                        "status": "available",
+                    }
+
+        # Check for HuggingFace datasets in configs
+        configs_dir = Path("/workspace/configs")
+        if configs_dir.exists():
+            for config_file in configs_dir.glob("*.yaml"):
+                try:
+                    import yaml
+
+                    with open(config_file) as f:
+                        config = yaml.safe_load(f)
+                        if "datasets" in config:
+                            for ds in config.get("datasets", []):
+                                if "path" in ds:
+                                    ds_name = ds["path"].split("/")[-1]
+                                    self.datasets[ds_name] = {
+                                        "name": ds_name,
+                                        "path": ds["path"],
+                                        "type": ds.get("type", "huggingface"),
+                                        "size": "Unknown",
+                                        "status": "remote",
+                                    }
+                except Exception:
+                    pass
+
+        self.refresh_dataset_table()
+
+    def get_dir_size(self, path: Path) -> str:
+        """Get human-readable directory size."""
+        total_size = sum(f.stat().st_size for f in path.rglob("*") if f.is_file())
+
+        for unit in ["B", "KB", "MB", "GB"]:
+            if total_size < 1024.0:
+                return f"{total_size:.2f} {unit}"
+            total_size /= 1024.0
+        return f"{total_size:.2f} TB"
+
+    def refresh_dataset_table(self) -> None:
+        """Refresh the dataset table."""
+        table = self.query_one("#dataset-table", DataTable)
+        table.clear()
+
+        for name, info in self.datasets.items():
+            table.add_row(
+                name[:30],
+                info["type"],
+                info["size"],
+                info["status"],
+            )
+
+    @on(DataTable.RowSelected)
+    def handle_dataset_selected(self, event: DataTable.RowSelected) -> None:
+        """Handle dataset selection from table."""
+        if event.cursor_row >= 0:
+            dataset_names = list(self.datasets.keys())
+            if event.cursor_row < len(dataset_names):
+                self.selected_dataset = dataset_names[event.cursor_row]
+                self.load_dataset_preview()
+                self.update_dataset_stats()
+
+    @work(thread=True)
+    async def load_dataset_preview(self) -> None:
+        """Load preview of selected dataset."""
+        if not self.selected_dataset:
+            return
+
+        dataset_info = self.datasets[self.selected_dataset]
+        preview_text = ""
+
+        try:
+            if dataset_info["type"] == "local" and Path(dataset_info["path"]).exists():
+                # Load first few samples from local dataset
+                sample_files = list(Path(dataset_info["path"]).glob("*.json"))[:3]
+                samples = []
+                for sample_file in sample_files:
+                    with open(sample_file) as f:
+                        samples.append(json.load(f))
+
+                preview_text = json.dumps(samples, indent=2)
+            else:
+                # Show dataset info for remote datasets
+                preview_text = json.dumps(dataset_info, indent=2)
+
+        except Exception as e:
+            preview_text = f"Error loading preview: {str(e)}"
+
+        preview = self.query_one("#dataset-preview", TextArea)
+        preview.load_text(preview_text)
+
+    def update_dataset_stats(self) -> None:
+        """Update dataset statistics display."""
+        if not self.selected_dataset:
+            return
+
+        info = self.datasets[self.selected_dataset]
+
+        self.query_one("#stat-name", Static).update(info["name"])
+        self.query_one("#stat-type", Static).update(info["type"])
+        self.query_one("#stat-size", Static).update(info["size"])
+        self.query_one("#stat-samples", Static).update("N/A")
+        self.query_one("#stat-features", Static).update("N/A")
+        self.query_one("#stat-format", Static).update("JSON")
+        self.query_one("#stat-preprocessed", Static).update("No")
+
+    @on(Button.Pressed, "#preprocess")
+    @work(thread=True)
+    async def handle_preprocess(self) -> None:
+        """Preprocess selected dataset."""
+        if not self.selected_dataset or self.preprocessing_active:
+            return
+
+        self.preprocessing_active = True
+        dataset_info = self.datasets[self.selected_dataset]
+
+        log = self.query_one("#processing-log", Log)
+        log.clear()
+        log.write_line(f"🔄 Starting preprocessing for {self.selected_dataset}...")
+
+        progress = self.query_one("#preprocessing-progress", ProgressBar)
+        progress.update(progress=0)
+
+        try:
+            import subprocess
+            import tempfile
+
+            # Create a temporary config for preprocessing
+            with tempfile.NamedTemporaryFile(
+                mode="w", suffix=".yaml", delete=False
+            ) as f:
+                config = {
+                    "datasets": [
+                        {
+                            "path": dataset_info["path"],
+                            "type": dataset_info.get("type", "alpaca"),
+                        }
+                    ],
+                    "output_dir": f"/tmp/preprocessed_{self.selected_dataset}",
+                }
+                import yaml
+
+                yaml.dump(config, f)
+                temp_config = f.name
+
+            # Run preprocessing
+            cmd = ["python", "-m", "axolotl.cli.preprocess", temp_config]
+            process = subprocess.Popen(
+                cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+
+            # Monitor progress
+            for line in process.stdout:
+                log.write_line(line.strip())
+                # Update progress bar based on output
+                if "Processing" in line:
+                    progress.advance(10)
+
+            process.wait()
+
+            if process.returncode == 0:
+                log.write_line("✅ Preprocessing completed successfully!")
+                dataset_info["status"] = "preprocessed"
+                progress.update(progress=100)
+            else:
+                log.write_line(
+                    f"❌ Preprocessing failed with code {process.returncode}"
+                )
+
+            import os
+
+            os.unlink(temp_config)
+
+        except Exception as e:
+            log.write_line(f"❌ Error during preprocessing: {str(e)}")
+        finally:
+            self.preprocessing_active = False
+            self.refresh_dataset_table()
+
+    @on(Button.Pressed, "#load-dataset")
+    async def handle_load_dataset(self) -> None:
+        """Load a new dataset."""
+        log = self.query_one("#processing-log", Log)
+        log.write_line("📦 Load dataset functionality coming soon...")
+
+    @on(Button.Pressed, "#download")
+    @work(thread=True)
+    async def handle_download(self) -> None:
+        """Download a remote dataset."""
+        if not self.selected_dataset:
+            return
+
+        dataset_info = self.datasets[self.selected_dataset]
+        if dataset_info["type"] != "huggingface":
+            return
+
+        log = self.query_one("#processing-log", Log)
+        log.clear()
+        log.write_line(f"📥 Downloading {self.selected_dataset} from HuggingFace...")
+
+        try:
+            from datasets import load_dataset
+
+            dataset = load_dataset(dataset_info["path"])
+            save_path = Path(f"/workspace/datasets/{self.selected_dataset}")
+            save_path.mkdir(parents=True, exist_ok=True)
+
+            dataset.save_to_disk(str(save_path))
+
+            log.write_line(f"✅ Downloaded to {save_path}")
+            dataset_info["type"] = "local"
+            dataset_info["status"] = "available"
+            dataset_info["path"] = str(save_path)
+            self.refresh_dataset_table()
+
+        except Exception as e:
+            log.write_line(f"❌ Download failed: {str(e)}")
+
+    @on(Button.Pressed, "#refresh")
+    def handle_refresh(self) -> None:
+        """Refresh dataset list."""
+        self.load_datasets()
+
+    def action_preprocess(self) -> None:
+        """Preprocess selected dataset."""
+        self.handle_preprocess()
+
+    def action_refresh(self) -> None:
+        """Refresh dataset list."""
+        self.handle_refresh()

src/axolotl/tui/screens/inference.py

@@ -0,0 +1,445 @@
+"""Inference and testing screen for Axolotl TUI."""
+
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from textual import events, on, work
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.widgets import (
+    Button,
+    Input,
+    Label,
+    Log,
+    Select,
+    Static,
+    TextArea,
+)
+
+from axolotl.tui.screens.base import BaseScreen
+
+
+class InferenceScreen(BaseScreen):
+    """Inference and testing screen."""
+
+    BINDINGS = [
+        Binding("ctrl+enter", "send_message", "Send"),
+        Binding("ctrl+c", "clear_chat", "Clear"),
+        Binding("ctrl+l", "load_model", "Load Model"),
+        Binding("ctrl+s", "save_chat", "Save Chat"),
+    ]
+
+    CSS = """
+    .inference-container {
+        layout: horizontal;
+        height: 100%;
+    }
+
+    .model-selector {
+        width: 30%;
+        border: solid $primary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .chat-interface {
+        width: 70%;
+        border: solid $secondary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .chat-history {
+        height: 70%;
+        border: solid $primary;
+        padding: 1;
+        margin: 0 0 1 0;
+    }
+
+    .input-area {
+        height: 20%;
+        border: solid $warning;
+        padding: 1;
+        margin: 0 0 1 0;
+    }
+
+    .chat-controls {
+        layout: horizontal;
+        height: 4;
+        align: center middle;
+        padding: 1;
+    }
+
+    .chat-controls Button {
+        margin: 0 1;
+    }
+
+    .model-info {
+        padding: 1;
+        border: solid $surface;
+        margin: 1 0;
+    }
+
+    .screen-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .screen-subtitle {
+        text-align: center;
+        padding: 0 0 1 0;
+        color: $text-muted;
+    }
+
+    TextArea {
+        height: 100%;
+    }
+
+    Log {
+        height: 100%;
+    }
+    """
+
+    def __init__(self):
+        """Initialize the inference screen."""
+        super().__init__(
+            title="Inference & Testing", subtitle="Interactive chat and model testing"
+        )
+        self.loaded_model: Optional[str] = None
+        self.chat_history: List[Dict[str, str]] = []
+
+    def compose(self) -> ComposeResult:
+        """Compose the inference screen layout."""
+        yield Container(
+            Static("🦾 Inference & Testing", classes="screen-title"),
+            Static("Interactive chat and model testing", classes="screen-subtitle"),
+            Container(
+                Container(
+                    Label("Model Selection"),
+                    Select(
+                        [("No model loaded", "none")],
+                        id="model-select",
+                        value="none",
+                    ),
+                    Container(
+                        Button("Load Model", id="load-model", variant="primary"),
+                        Button("Unload", id="unload-model", variant="default"),
+                        Button("Gradio UI", id="gradio-ui", variant="success"),
+                    ),
+                    Container(
+                        Static("No model loaded", id="model-status"),
+                        classes="model-info",
+                    ),
+                    Label("Inference Parameters"),
+                    Container(
+                        Label("Temperature:"),
+                        Input(value="0.7", id="temperature"),
+                        Label("Max Tokens:"),
+                        Input(value="256", id="max-tokens"),
+                        Label("Top P:"),
+                        Input(value="0.9", id="top-p"),
+                    ),
+                    classes="model-selector",
+                ),
+                Container(
+                    Container(
+                        Log(id="chat-history"),
+                        classes="chat-history",
+                    ),
+                    Container(
+                        TextArea(
+                            id="message-input",
+                        ),
+                        classes="input-area",
+                    ),
+                    Container(
+                        Button("Send [Ctrl+Enter]", id="send", variant="primary"),
+                        Button("Clear Chat", id="clear", variant="warning"),
+                        Button("Save Chat", id="save-chat", variant="default"),
+                        Button("Load Examples", id="load-examples", variant="default"),
+                        classes="chat-controls",
+                    ),
+                    classes="chat-interface",
+                ),
+                classes="inference-container",
+            ),
+            id="content",
+        )
+
+    def on_mount(self) -> None:
+        """Called when the screen is mounted."""
+        self.load_available_models()
+
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line("💬 Welcome to Axolotl Inference!")
+        chat.write_line("Load a model to start chatting.")
+
+    @work(thread=True)
+    async def load_available_models(self) -> None:
+        """Load list of available models."""
+        models = [("No model loaded", "none")]
+
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line("🔍 Scanning for available models...")
+
+        # Check for trained models
+        outputs_dir = Path("./outputs")
+        chat.write_line(f"Checking outputs directory: {outputs_dir.absolute()}")
+        if outputs_dir.exists():
+            found_models = 0
+            for model_dir in outputs_dir.glob("*"):
+                if model_dir.is_dir():
+                    # Look for various model file types
+                    model_files = (
+                        list(model_dir.glob("pytorch_model.bin"))
+                        + list(model_dir.glob("model.safetensors"))
+                        + list(model_dir.glob("*.bin"))
+                        + list(model_dir.glob("*.safetensors"))
+                    )
+                    if model_files:
+                        models.append((model_dir.name, str(model_dir)))
+                        found_models += 1
+            chat.write_line(f"Found {found_models} trained models in outputs/")
+        else:
+            chat.write_line("outputs/ directory not found")
+
+        # Add some example/demo models for testing
+        models.extend(
+            [
+                ("Demo: GPT-2 Small", "gpt2"),
+                ("Demo: TinyLlama", "TinyLlama/TinyLlama-1.1B-Chat-v1.0"),
+                ("Demo: Phi-2", "microsoft/phi-2"),
+            ]
+        )
+
+        select = self.query_one("#model-select", Select)
+        select.set_options(models)
+        chat.write_line(f"✅ Loaded {len(models)} models in dropdown")
+
+    @on(Button.Pressed, "#load-model")
+    @work(thread=True)
+    async def handle_load_model(self) -> None:
+        """Load selected model for inference."""
+        select = self.query_one("#model-select", Select)
+        if select.value == "none":
+            return
+
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line(f"🔄 Loading model: {select.value}")
+
+        status = self.query_one("#model-status", Static)
+        status.update("Loading...")
+
+        try:
+            # Simulate model loading (in real implementation, would load the actual model)
+            import time
+
+            time.sleep(2)  # Simulate loading time
+
+            self.loaded_model = select.value
+            status.update(f"✅ Loaded: {Path(select.value).name}")
+            chat.write_line("✅ Model loaded successfully!")
+            chat.write_line("You can now start chatting.")
+
+        except Exception as e:
+            status.update("❌ Failed to load")
+            chat.write_line(f"❌ Failed to load model: {str(e)}")
+
+    @on(Button.Pressed, "#send")
+    async def handle_send_message(self) -> None:
+        """Send message to model."""
+        if not self.loaded_model:
+            chat = self.query_one("#chat-history", Log)
+            chat.write_line("⚠️ Please load a model first")
+            return
+
+        message_input = self.query_one("#message-input", TextArea)
+        message = message_input.text.strip()
+
+        if not message:
+            return
+
+        # Add user message to chat
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line(f"👤 User: {message}")
+
+        # Clear input
+        message_input.clear()
+
+        # Add to history
+        self.chat_history.append({"role": "user", "content": message})
+
+        # Generate response (placeholder)
+        self.generate_response(message)
+
+    @on(TextArea.Changed, "#message-input")
+    def on_message_input_changed(self, event: TextArea.Changed) -> None:
+        """Handle changes to the message input."""
+        # This could be used for features like typing indicators
+        pass
+
+    def on_key(self, event: events.Key) -> None:
+        """Handle key events globally."""
+        # Check if we're focused on the message input and Ctrl+Enter is pressed
+        focused = self.focused
+        if focused and focused.id == "message-input" and event.key == "ctrl+enter":
+            event.prevent_default()
+            self.handle_send_message()
+
+    @work(thread=True)
+    async def generate_response(self, message: str) -> None:
+        """Generate model response."""
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line("🤖 Assistant: Thinking...")
+
+        try:
+            # Get inference parameters
+            float(self.query_one("#temperature", Input).value)
+            int(self.query_one("#max-tokens", Input).value)
+            float(self.query_one("#top-p", Input).value)
+
+            if not self.loaded_model or self.loaded_model == "none":
+                response = "I don't have a model loaded yet. Please load a model first using the 'Load Model' button."
+            elif self.loaded_model.startswith("gpt2"):
+                # Simple response for GPT-2
+                responses = [
+                    f"Thanks for your message: '{message}'. I'm a GPT-2 model running in demo mode.",
+                    "I understand you're testing the interface. GPT-2 models are great for experimentation!",
+                    "This is a simulated GPT-2 response. In a real setup, I'd generate text based on your input.",
+                    f"GPT-2 here! You said: '{message}'. I'd normally continue this conversation creatively.",
+                ]
+                import random
+
+                response = random.choice(responses)
+            elif "llama" in self.loaded_model.lower():
+                # Response for Llama models
+                response = f"🦙 LLaMA model here! You asked: '{message}'. I'm designed for helpful, harmless, and honest conversations. How can I assist you today?"
+            elif "phi" in self.loaded_model.lower():
+                # Response for Phi models
+                response = f"Phi model responding! Your message: '{message}'. I'm optimized for reasoning and code tasks. What would you like to explore?"
+            else:
+                # Generic response for other models
+                response = f"Model '{self.loaded_model}' responding to: '{message}'. I'm ready to help with your questions!"
+
+            # Simulate inference time
+            import time
+
+            time.sleep(0.5)
+
+            # Clear the "thinking" message and show response
+            chat.write_line(f"🤖 Assistant: {response}")
+
+            # Add to history
+            self.chat_history.append({"role": "assistant", "content": response})
+
+        except Exception as e:
+            chat.write_line(f"❌ Error generating response: {str(e)}")
+
+    @on(Button.Pressed, "#clear")
+    def handle_clear_chat(self) -> None:
+        """Clear chat history."""
+        chat = self.query_one("#chat-history", Log)
+        chat.clear()
+        self.chat_history = []
+        chat.write_line("💬 Chat cleared. Start a new conversation!")
+
+    @on(Button.Pressed, "#save-chat")
+    def handle_save_chat(self) -> None:
+        """Save chat history to file."""
+        if not self.chat_history:
+            chat = self.query_one("#chat-history", Log)
+            chat.write_line("⚠️ No chat history to save")
+            return
+
+        try:
+            import json
+            from datetime import datetime
+
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"chat_history_{timestamp}.json"
+
+            with open(filename, "w") as f:
+                json.dump(self.chat_history, f, indent=2)
+
+            chat = self.query_one("#chat-history", Log)
+            chat.write_line(f"💾 Chat saved to {filename}")
+
+        except Exception as e:
+            chat = self.query_one("#chat-history", Log)
+            chat.write_line(f"❌ Error saving chat: {str(e)}")
+
+    @on(Button.Pressed, "#load-examples")
+    def handle_load_examples(self) -> None:
+        """Load example prompts."""
+        examples = [
+            "Explain the concept of machine learning in simple terms.",
+            "Write a Python function to calculate fibonacci numbers.",
+            "What are the benefits of fine-tuning language models?",
+            "Describe the difference between supervised and unsupervised learning.",
+        ]
+
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line("📚 Example prompts:")
+        for i, example in enumerate(examples, 1):
+            chat.write_line(f"{i}. {example}")
+        chat.write_line("Copy and paste any example to try it out!")
+
+    @on(Button.Pressed, "#gradio-ui")
+    @work(thread=True)
+    async def handle_gradio_ui(self) -> None:
+        """Launch Gradio web interface."""
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line("🌐 Launching Gradio web interface...")
+
+        try:
+            import subprocess
+
+            if self.loaded_model:
+                cmd = [
+                    "python",
+                    "-m",
+                    "axolotl.cli.inference",
+                    self.loaded_model,
+                    "--gradio",
+                ]
+            else:
+                chat.write_line("⚠️ No model loaded. Loading default interface...")
+                cmd = ["python", "-m", "axolotl.cli.inference", "--gradio"]
+
+            subprocess.Popen(cmd)
+            chat.write_line("✅ Gradio interface launched! Check your browser.")
+
+        except Exception as e:
+            chat.write_line(f"❌ Error launching Gradio: {str(e)}")
+
+    @on(Button.Pressed, "#unload-model")
+    def handle_unload_model(self) -> None:
+        """Unload current model."""
+        self.loaded_model = None
+        status = self.query_one("#model-status", Static)
+        status.update("No model loaded")
+
+        select = self.query_one("#model-select", Select)
+        select.value = "none"
+
+        chat = self.query_one("#chat-history", Log)
+        chat.write_line("🔄 Model unloaded")
+
+    def action_send_message(self) -> None:
+        """Send message action."""
+        self.handle_send_message()
+
+    def action_clear_chat(self) -> None:
+        """Clear chat action."""
+        self.handle_clear_chat()
+
+    def action_load_model(self) -> None:
+        """Load model action."""
+        self.handle_load_model()
+
+    def action_save_chat(self) -> None:
+        """Save chat action."""
+        self.handle_save_chat()

src/axolotl/tui/screens/models.py

@@ -0,0 +1,373 @@
+"""Model management screen for Axolotl TUI."""
+
+from pathlib import Path
+from typing import Dict, Optional
+
+from textual import on, work
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container, ScrollableContainer
+from textual.widgets import (
+    Button,
+    DataTable,
+    Footer,
+    Header,
+    Label,
+    Log,
+    ProgressBar,
+    Static,
+    TabbedContent,
+    TabPane,
+)
+
+from axolotl.tui.screens.base import BaseScreen
+
+
+class ModelScreen(BaseScreen):
+    """Model management screen."""
+
+    BINDINGS = [
+        Binding("ctrl+m", "merge_lora", "Merge LoRA"),
+        Binding("ctrl+q", "quantize", "Quantize"),
+        Binding("ctrl+e", "evaluate", "Evaluate"),
+        Binding("r", "refresh", "Refresh"),
+    ]
+
+    CSS = """
+    .model-container {
+        layout: horizontal;
+        height: 100%;
+    }
+
+    .model-list {
+        width: 50%;
+        border: solid $primary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .model-operations {
+        width: 50%;
+        border: solid $secondary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .model-actions {
+        layout: horizontal;
+        height: 4;
+        align: center middle;
+        padding: 1;
+    }
+
+    .model-actions Button {
+        margin: 0 1;
+    }
+
+    DataTable {
+        height: 80%;
+    }
+
+    .screen-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .screen-subtitle {
+        text-align: center;
+        padding: 0 0 1 0;
+        color: $text-muted;
+    }
+    """
+
+    def __init__(self):
+        """Initialize the model screen."""
+        super().__init__(
+            title="Model Management",
+            subtitle="Manage trained models, merge LoRA adapters, and quantize models",
+        )
+        self.models: Dict[str, Dict] = {}
+        self.selected_model: Optional[str] = None
+
+    def compose(self) -> ComposeResult:
+        """Compose the model screen layout."""
+        yield Header()
+        with Container(id="content"):
+            yield Static("🦾 Model Management", classes="screen-title")
+            yield Static(
+                "Manage trained models, merge LoRA adapters, and quantize models",
+                classes="screen-subtitle",
+            )
+            with Container(classes="model-container"):
+                with Container(classes="model-list"):
+                    yield Label("Available Models")
+                    yield DataTable(id="model-table")
+                    with Container(classes="model-actions"):
+                        yield Button("Merge LoRA", id="merge-lora", variant="primary")
+                        yield Button("Quantize", id="quantize", variant="success")
+                        yield Button("Evaluate", id="evaluate", variant="warning")
+                        yield Button("Refresh", id="refresh", variant="default")
+                with Container(classes="model-operations"):
+                    with TabbedContent():
+                        with TabPane("Operations"):
+                            with Container():
+                                yield Log(id="operations-log")
+                                with Container():
+                                    yield Label("Operation Progress:")
+                                    yield ProgressBar(
+                                        total=100,
+                                        id="operation-progress",
+                                    )
+                        with TabPane("Model Info"):
+                            with ScrollableContainer():
+                                yield Static(
+                                    "Model information will appear here",
+                                    id="model-info",
+                                )
+        yield Footer()
+
+    def on_mount(self) -> None:
+        """Called when the screen is mounted."""
+        self.setup_model_table()
+        self.load_models()
+
+        log = self.query_one("#operations-log", Log)
+        log.write_line("Model manager ready.")
+
+    def setup_model_table(self) -> None:
+        """Setup the model table."""
+        table = self.query_one("#model-table", DataTable)
+        table.add_columns("Name", "Type", "Size", "Status")
+        table.cursor_type = "row"
+        table.zebra_stripes = True
+
+    @work(thread=True)
+    async def load_models(self) -> None:
+        """Load available models."""
+        # Check outputs directory for trained models
+        outputs_dir = Path("./outputs")
+        if outputs_dir.exists():
+            for model_dir in outputs_dir.glob("*"):
+                if model_dir.is_dir():
+                    self.models[model_dir.name] = {
+                        "name": model_dir.name,
+                        "path": str(model_dir),
+                        "type": "checkpoint",
+                        "size": self.get_dir_size(model_dir),
+                        "status": "available",
+                    }
+
+        self.refresh_model_table()
+
+    def get_dir_size(self, path: Path) -> str:
+        """Get human-readable directory size."""
+        try:
+            total_size = sum(f.stat().st_size for f in path.rglob("*") if f.is_file())
+
+            for unit in ["B", "KB", "MB", "GB"]:
+                if total_size < 1024.0:
+                    return f"{total_size:.2f} {unit}"
+                total_size /= 1024.0
+            return f"{total_size:.2f} TB"
+        except Exception:
+            return "Unknown"
+
+    def refresh_model_table(self) -> None:
+        """Refresh the model table."""
+        table = self.query_one("#model-table", DataTable)
+        table.clear()
+
+        for name, info in self.models.items():
+            table.add_row(
+                name[:30],
+                info["type"],
+                info["size"],
+                info["status"],
+            )
+
+    @on(DataTable.RowSelected)
+    def handle_model_selected(self, event: DataTable.RowSelected) -> None:
+        """Handle model selection from table."""
+        if event.cursor_row >= 0:
+            model_names = list(self.models.keys())
+            if event.cursor_row < len(model_names):
+                self.selected_model = model_names[event.cursor_row]
+                self.update_model_info()
+
+    def update_model_info(self) -> None:
+        """Update model information display."""
+        if not self.selected_model:
+            return
+
+        info = self.models[self.selected_model]
+        info_text = f"""
+Model Name: {info['name']}
+Path: {info['path']}
+Type: {info['type']}
+Size: {info['size']}
+Status: {info['status']}
+        """
+
+        self.query_one("#model-info", Static).update(info_text)
+
+    @on(Button.Pressed, "#merge-lora")
+    @work(thread=True)
+    async def handle_merge_lora(self) -> None:
+        """Merge LoRA adapters with base model."""
+        if not self.selected_model:
+            log = self.query_one("#operations-log", Log)
+            log.write_line("⚠️ No model selected")
+            return
+
+        model_info = self.models[self.selected_model]
+        log = self.query_one("#operations-log", Log)
+        log.clear()
+        log.write_line(f"🔄 Merging LoRA adapters for {self.selected_model}...")
+
+        progress = self.query_one("#operation-progress", ProgressBar)
+        progress.update(progress=0)
+
+        try:
+            import subprocess
+
+            cmd = ["python", "-m", "axolotl.cli.merge_lora", model_info["path"]]
+
+            process = subprocess.Popen(
+                cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+
+            for line in process.stdout:
+                log.write_line(line.strip())
+                progress.advance(10)
+
+            process.wait()
+
+            if process.returncode == 0:
+                log.write_line("✅ LoRA merge completed successfully!")
+                progress.update(progress=100)
+            else:
+                log.write_line(f"❌ LoRA merge failed with code {process.returncode}")
+
+        except Exception as e:
+            log.write_line(f"❌ Error during LoRA merge: {str(e)}")
+
+    @on(Button.Pressed, "#quantize")
+    @work(thread=True)
+    async def handle_quantize(self) -> None:
+        """Quantize selected model."""
+        if not self.selected_model:
+            log = self.query_one("#operations-log", Log)
+            log.write_line("⚠️ No model selected")
+            return
+
+        model_info = self.models[self.selected_model]
+        log = self.query_one("#operations-log", Log)
+        log.clear()
+        log.write_line(f"🔄 Quantizing {self.selected_model}...")
+
+        progress = self.query_one("#operation-progress", ProgressBar)
+        progress.update(progress=0)
+
+        try:
+            import subprocess
+
+            cmd = [
+                "python",
+                "-m",
+                "axolotl.cli.quantize",
+                model_info["path"],
+                "--output-dir",
+                f"{model_info['path']}_quantized",
+            ]
+
+            process = subprocess.Popen(
+                cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+
+            for line in process.stdout:
+                log.write_line(line.strip())
+                progress.advance(5)
+
+            process.wait()
+
+            if process.returncode == 0:
+                log.write_line("✅ Quantization completed successfully!")
+                progress.update(progress=100)
+            else:
+                log.write_line(f"❌ Quantization failed with code {process.returncode}")
+
+        except Exception as e:
+            log.write_line(f"❌ Error during quantization: {str(e)}")
+
+    @on(Button.Pressed, "#evaluate")
+    @work(thread=True)
+    async def handle_evaluate(self) -> None:
+        """Evaluate selected model."""
+        if not self.selected_model:
+            log = self.query_one("#operations-log", Log)
+            log.write_line("⚠️ No model selected")
+            return
+
+        model_info = self.models[self.selected_model]
+        log = self.query_one("#operations-log", Log)
+        log.clear()
+        log.write_line(f"🔄 Evaluating {self.selected_model}...")
+
+        progress = self.query_one("#operation-progress", ProgressBar)
+        progress.update(progress=0)
+
+        try:
+            import subprocess
+
+            cmd = ["python", "-m", "axolotl.cli.evaluate", model_info["path"]]
+
+            process = subprocess.Popen(
+                cmd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+
+            for line in process.stdout:
+                log.write_line(line.strip())
+                progress.advance(10)
+
+            process.wait()
+
+            if process.returncode == 0:
+                log.write_line("✅ Evaluation completed successfully!")
+                progress.update(progress=100)
+            else:
+                log.write_line(f"❌ Evaluation failed with code {process.returncode}")
+
+        except Exception as e:
+            log.write_line(f"❌ Error during evaluation: {str(e)}")
+
+    @on(Button.Pressed, "#refresh")
+    def handle_refresh(self) -> None:
+        """Refresh model list."""
+        self.load_models()
+
+    def action_merge_lora(self) -> None:
+        """Merge LoRA adapters."""
+        self.handle_merge_lora()
+
+    def action_quantize(self) -> None:
+        """Quantize model."""
+        self.handle_quantize()
+
+    def action_evaluate(self) -> None:
+        """Evaluate model."""
+        self.handle_evaluate()
+
+    def action_refresh(self) -> None:
+        """Refresh model list."""
+        self.handle_refresh()

src/axolotl/tui/screens/monitor.py

@@ -0,0 +1,414 @@
+"""System monitoring screen for Axolotl TUI."""
+
+import psutil
+from textual import on, work
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.widgets import (
+    Button,
+    DataTable,
+    Footer,
+    Header,
+    Label,
+    Log,
+    ProgressBar,
+    Sparkline,
+    Static,
+)
+
+from axolotl.tui.screens.base import BaseScreen
+
+
+class MonitorScreen(BaseScreen):
+    """System monitoring screen."""
+
+    BINDINGS = [
+        Binding("r", "refresh", "Refresh"),
+        Binding("ctrl+k", "kill_process", "Kill Process"),
+    ]
+
+    CSS = """
+    .monitor-container {
+        layout: vertical;
+        height: 100%;
+    }
+
+    .metrics-grid {
+        layout: horizontal;
+        height: 20%;
+        padding: 1;
+    }
+
+    .metric-card {
+        width: 25%;
+        border: solid $surface;
+        padding: 1;
+        margin: 0 1;
+    }
+
+    .metric-label {
+        text-style: bold;
+        color: $text-muted;
+        text-align: center;
+    }
+
+    .metric-value {
+        text-style: bold;
+        text-align: center;
+        padding: 1;
+    }
+
+    .charts-container {
+        height: 40%;
+        layout: horizontal;
+        padding: 1;
+    }
+
+    .chart-panel {
+        width: 50%;
+        border: solid $primary;
+        padding: 1;
+        margin: 0 1;
+    }
+
+    .processes-container {
+        height: 40%;
+        border: solid $warning;
+        padding: 1;
+        margin: 1;
+    }
+
+    DataTable {
+        height: 90%;
+    }
+
+    .process-controls {
+        layout: horizontal;
+        height: 4;
+        align: center middle;
+        padding: 1;
+    }
+
+    .process-controls Button {
+        margin: 0 1;
+    }
+
+    .screen-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .screen-subtitle {
+        text-align: center;
+        padding: 0 0 1 0;
+        color: $text-muted;
+    }
+
+    Sparkline {
+        height: 8;
+    }
+
+    ProgressBar {
+        margin: 1 0;
+    }
+    """
+
+    def __init__(self):
+        """Initialize the monitor screen."""
+        super().__init__(
+            title="System Monitor",
+            subtitle="Monitor system resources and running processes",
+        )
+        self.cpu_history = []
+        self.memory_history = []
+        self.gpu_history = []
+
+    def compose(self) -> ComposeResult:
+        """Compose the monitor screen layout."""
+        yield Header()
+        yield Container(
+            Static("🦾 System Monitor", classes="screen-title"),
+            Static(
+                "Monitor system resources and running processes",
+                classes="screen-subtitle",
+            ),
+            Container(
+                Container(
+                    Container(
+                        Static("CPU Usage", classes="metric-label"),
+                        Static("0%", id="cpu-usage", classes="metric-value"),
+                        ProgressBar(total=100, id="cpu-progress"),
+                        classes="metric-card",
+                    ),
+                    Container(
+                        Static("Memory", classes="metric-label"),
+                        Static("0%", id="memory-usage", classes="metric-value"),
+                        ProgressBar(total=100, id="memory-progress"),
+                        classes="metric-card",
+                    ),
+                    Container(
+                        Static("GPU Usage", classes="metric-label"),
+                        Static("0%", id="gpu-usage", classes="metric-value"),
+                        ProgressBar(total=100, id="gpu-progress"),
+                        classes="metric-card",
+                    ),
+                    Container(
+                        Static("Temperature", classes="metric-label"),
+                        Static("0°C", id="temperature", classes="metric-value"),
+                        classes="metric-card",
+                    ),
+                    classes="metrics-grid",
+                ),
+                Container(
+                    Container(
+                        Label("CPU History"),
+                        Sparkline([], id="cpu-sparkline"),
+                        classes="chart-panel",
+                    ),
+                    Container(
+                        Label("Memory History"),
+                        Sparkline([], id="memory-sparkline"),
+                        classes="chart-panel",
+                    ),
+                    classes="charts-container",
+                ),
+                Container(
+                    DataTable(id="process-table"),
+                    Log(id="gpu-info"),
+                    Log(id="system-logs"),
+                    classes="processes-container",
+                ),
+                classes="monitor-container",
+            ),
+            id="content",
+        )
+        yield Footer()
+
+    def on_mount(self) -> None:
+        """Called when the screen is mounted."""
+        self.setup_process_table()
+        self.start_monitoring()
+
+        # Initial system info
+        self.update_system_info()
+        self.update_gpu_info()
+
+    def setup_process_table(self) -> None:
+        """Setup the process table."""
+        table = self.query_one("#process-table", DataTable)
+        table.add_columns("PID", "Name", "CPU%", "Memory%", "Status")
+        table.cursor_type = "row"
+        table.zebra_stripes = True
+
+    def start_monitoring(self) -> None:
+        """Start the monitoring timer."""
+        self.set_interval(2.0, self.update_system_metrics)
+
+    @work(thread=True)
+    async def update_system_metrics(self) -> None:
+        """Update system metrics."""
+        try:
+            # CPU usage
+            cpu_percent = psutil.cpu_percent(interval=None)
+            self.cpu_history.append(cpu_percent)
+            if len(self.cpu_history) > 50:
+                self.cpu_history.pop(0)
+
+            # Memory usage
+            memory = psutil.virtual_memory()
+            memory_percent = memory.percent
+            self.memory_history.append(memory_percent)
+            if len(self.memory_history) > 50:
+                self.memory_history.pop(0)
+
+            # GPU usage (if available)
+            gpu_percent = self.get_gpu_usage()
+            self.gpu_history.append(gpu_percent)
+            if len(self.gpu_history) > 50:
+                self.gpu_history.pop(0)
+
+            # Temperature
+            temperature = self.get_temperature()
+
+            # Update UI
+            self.update_metrics_display(
+                cpu_percent, memory_percent, gpu_percent, temperature
+            )
+            self.update_sparklines()
+            self.update_process_table()
+
+        except Exception as e:
+            log = self.query_one("#system-logs", Log)
+            log.write_line(f"Error updating metrics: {str(e)}")
+
+    def get_gpu_usage(self) -> float:
+        """Get GPU usage percentage."""
+        try:
+            import pynvml
+
+            pynvml.nvmlInit()
+            handle = pynvml.nvmlDeviceGetHandleByIndex(0)
+            util = pynvml.nvmlDeviceGetUtilizationRates(handle)
+            return util.gpu
+        except Exception:
+            return 0.0
+
+    def get_temperature(self) -> str:
+        """Get system temperature."""
+        try:
+            temps = psutil.sensors_temperatures()
+            if temps:
+                for name, entries in temps.items():
+                    if entries:
+                        return f"{entries[0].current:.1f}°C"
+            return "N/A"
+        except Exception:
+            return "N/A"
+
+    def update_metrics_display(
+        self, cpu: float, memory: float, gpu: float, temp: str
+    ) -> None:
+        """Update metrics display."""
+        self.query_one("#cpu-usage", Static).update(f"{cpu:.1f}%")
+        self.query_one("#memory-usage", Static).update(f"{memory:.1f}%")
+        self.query_one("#gpu-usage", Static).update(f"{gpu:.1f}%")
+        self.query_one("#temperature", Static).update(temp)
+
+        self.query_one("#cpu-progress", ProgressBar).update(progress=cpu)
+        self.query_one("#memory-progress", ProgressBar).update(progress=memory)
+        self.query_one("#gpu-progress", ProgressBar).update(progress=gpu)
+
+    def update_sparklines(self) -> None:
+        """Update sparkline charts."""
+        if self.cpu_history:
+            cpu_sparkline = self.query_one("#cpu-sparkline", Sparkline)
+            cpu_sparkline.data = self.cpu_history
+
+        if self.memory_history:
+            memory_sparkline = self.query_one("#memory-sparkline", Sparkline)
+            memory_sparkline.data = self.memory_history
+
+    def update_process_table(self) -> None:
+        """Update the process table."""
+        table = self.query_one("#process-table", DataTable)
+        table.clear()
+
+        try:
+            # Get top processes by CPU usage
+            processes = []
+            for proc in psutil.process_iter(
+                ["pid", "name", "cpu_percent", "memory_percent", "status"]
+            ):
+                try:
+                    pinfo = proc.info
+                    if pinfo["cpu_percent"] > 0.1:  # Only show processes using CPU
+                        processes.append(pinfo)
+                except (psutil.NoSuchProcess, psutil.AccessDenied):
+                    pass
+
+            # Sort by CPU usage
+            processes.sort(key=lambda x: x["cpu_percent"], reverse=True)
+
+            # Add top 20 processes
+            for proc in processes[:20]:
+                table.add_row(
+                    str(proc["pid"]),
+                    proc["name"][:20],
+                    f"{proc['cpu_percent']:.1f}%",
+                    f"{proc['memory_percent']:.1f}%",
+                    proc["status"],
+                )
+
+        except Exception as e:
+            log = self.query_one("#system-logs", Log)
+            log.write_line(f"Error updating process table: {str(e)}")
+
+    def update_system_info(self) -> None:
+        """Update system information."""
+        try:
+            # System info
+            psutil.boot_time()
+            cpu_count = psutil.cpu_count()
+            memory = psutil.virtual_memory()
+
+            log = self.query_one("#system-logs", Log)
+            log.write_line(f"System started. CPU cores: {cpu_count}")
+            log.write_line(f"Total memory: {memory.total / (1024**3):.1f} GB")
+            log.write_line(f"Available memory: {memory.available / (1024**3):.1f} GB")
+
+        except Exception as e:
+            log = self.query_one("#system-logs", Log)
+            log.write_line(f"Error getting system info: {str(e)}")
+
+    def update_gpu_info(self) -> None:
+        """Update GPU information."""
+        try:
+            import pynvml
+
+            pynvml.nvmlInit()
+
+            device_count = pynvml.nvmlDeviceGetCount()
+            log = self.query_one("#gpu-info", Log)
+            log.clear()
+            log.write_line(f"Found {device_count} GPU(s)")
+
+            for i in range(device_count):
+                handle = pynvml.nvmlDeviceGetHandleByIndex(i)
+                name = pynvml.nvmlDeviceGetName(handle).decode()
+                memory_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+
+                log.write_line(f"\nGPU {i}: {name}")
+                log.write_line(
+                    f"Memory: {memory_info.used / (1024**3):.1f} / {memory_info.total / (1024**3):.1f} GB"
+                )
+                log.write_line(f"Free: {memory_info.free / (1024**3):.1f} GB")
+
+        except Exception as e:
+            log = self.query_one("#gpu-info", Log)
+            log.clear()
+            log.write_line(f"GPU info unavailable: {str(e)}")
+
+    @on(Button.Pressed, "#kill-process")
+    def handle_kill_process(self) -> None:
+        """Kill selected process."""
+        table = self.query_one("#process-table", DataTable)
+        if table.cursor_row >= 0:
+            try:
+                row = table.get_row_at(table.cursor_row)
+                pid = int(row[0])
+
+                process = psutil.Process(pid)
+                process.terminate()
+
+                log = self.query_one("#system-logs", Log)
+                log.write_line(f"Terminated process {pid}")
+
+            except Exception as e:
+                log = self.query_one("#system-logs", Log)
+                log.write_line(f"Error killing process: {str(e)}")
+
+    @on(Button.Pressed, "#refresh")
+    def handle_refresh(self) -> None:
+        """Refresh all metrics."""
+        self.update_system_info()
+        self.update_gpu_info()
+
+        log = self.query_one("#system-logs", Log)
+        log.write_line("Metrics refreshed")
+
+    @on(Button.Pressed, "#auto-refresh")
+    def handle_auto_refresh(self) -> None:
+        """Toggle auto refresh."""
+        log = self.query_one("#system-logs", Log)
+        log.write_line("Auto refresh is always enabled (every 2 seconds)")
+
+    def action_refresh(self) -> None:
+        """Refresh action."""
+        self.handle_refresh()
+
+    def action_kill_process(self) -> None:
+        """Kill process action."""
+        self.handle_kill_process()

src/axolotl/tui/screens/training.py

@@ -0,0 +1,545 @@
+"""Training management screen for Axolotl TUI."""
+
+import subprocess
+import threading
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from textual import on, work
+from textual.app import ComposeResult
+from textual.binding import Binding
+from textual.containers import Container
+from textual.widgets import (
+    Button,
+    DataTable,
+    Footer,
+    Header,
+    Label,
+    Log,
+    Sparkline,
+    Static,
+)
+
+from axolotl.tui.screens.base import BaseScreen
+
+
+@dataclass
+class TrainingJob:
+    """Represents a training job."""
+
+    id: str
+    config_path: str
+    status: str  # pending, running, completed, failed
+    start_time: Optional[datetime] = None
+    end_time: Optional[datetime] = None
+    process: Optional[subprocess.Popen] = None
+    log_file: Optional[str] = None
+    current_epoch: int = 0
+    total_epochs: int = 0
+    current_loss: float = 0.0
+    losses: List[float] = None
+
+    def __post_init__(self):
+        if self.losses is None:
+            self.losses = []
+
+
+class TrainingScreen(BaseScreen):
+    """Training management screen."""
+
+    BINDINGS = [
+        Binding("ctrl+t", "new_training", "New Training"),
+        Binding("ctrl+r", "resume_training", "Resume"),
+        Binding("ctrl+x", "stop_training", "Stop"),
+        Binding("ctrl+l", "view_logs", "View Logs"),
+        Binding("r", "refresh", "Refresh"),
+    ]
+
+    CSS = """
+    .training-container {
+        layout: vertical;
+        height: 100%;
+    }
+
+    .job-list-container {
+        height: 40%;
+        border: solid $primary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .job-details-container {
+        height: 60%;
+        padding: 1;
+    }
+
+    .control-panel {
+        layout: horizontal;
+        height: 4;
+        align: center middle;
+        padding: 1;
+        border: solid $secondary;
+        margin: 1;
+    }
+
+    .control-panel Button {
+        margin: 0 1;
+    }
+
+    .metrics-panel {
+        layout: horizontal;
+        height: 10;
+        border: solid $primary;
+        padding: 1;
+        margin: 1;
+    }
+
+    .metric-card {
+        width: 25%;
+        border: tall $surface;
+        padding: 1;
+        margin: 0 1;
+    }
+
+    .metric-label {
+        text-style: bold;
+        color: $text-muted;
+    }
+
+    .metric-value {
+        text-style: bold;
+        text-align: center;
+        padding: 1;
+    }
+
+    .log-viewer {
+        border: solid $warning;
+        padding: 1;
+        margin: 1;
+    }
+
+    #training-logs {
+        height: 100%;
+    }
+
+    DataTable {
+        height: 100%;
+    }
+
+    .screen-title {
+        text-align: center;
+        text-style: bold;
+        padding: 1;
+        color: $primary;
+    }
+
+    .screen-subtitle {
+        text-align: center;
+        padding: 0 0 1 0;
+        color: $text-muted;
+    }
+
+    .sparkline-container {
+        height: 5;
+        border: solid $success;
+        padding: 1;
+        margin: 1;
+    }
+    """
+
+    def __init__(self):
+        """Initialize the training screen."""
+        super().__init__(
+            title="Training Management",
+            subtitle="Launch, monitor, and manage training jobs",
+        )
+        self.jobs: Dict[str, TrainingJob] = {}
+        self.selected_job_id: Optional[str] = None
+        self.update_timer = None
+
+    def compose(self) -> ComposeResult:
+        """Compose the training screen layout."""
+        yield Header()
+        yield Container(
+            Static("🦾 Training Management", classes="screen-title"),
+            Static(
+                "Launch, monitor, and manage training jobs", classes="screen-subtitle"
+            ),
+            Container(
+                Container(
+                    Label("Active Training Jobs"),
+                    DataTable(id="job-table"),
+                    classes="job-list-container",
+                ),
+                Container(
+                    Button("New Training", id="new-training", variant="primary"),
+                    Button("Resume", id="resume-training", variant="success"),
+                    Button("Stop", id="stop-training", variant="error"),
+                    Button("View Logs", id="view-logs", variant="default"),
+                    Button("Clear Completed", id="clear-completed", variant="warning"),
+                    Button("Refresh", id="refresh", variant="default"),
+                    classes="control-panel",
+                ),
+                Container(
+                    Container(
+                        Static("Current Epoch", classes="metric-label"),
+                        Static("0 / 0", id="epoch-metric", classes="metric-value"),
+                        classes="metric-card",
+                    ),
+                    Container(
+                        Static("Loss", classes="metric-label"),
+                        Static("0.000", id="loss-metric", classes="metric-value"),
+                        classes="metric-card",
+                    ),
+                    Container(
+                        Static("Status", classes="metric-label"),
+                        Static("Idle", id="status-metric", classes="metric-value"),
+                        classes="metric-card",
+                    ),
+                    Container(
+                        Static("Duration", classes="metric-label"),
+                        Static(
+                            "00:00:00", id="duration-metric", classes="metric-value"
+                        ),
+                        classes="metric-card",
+                    ),
+                    classes="metrics-panel",
+                ),
+                Container(
+                    Label("Loss History"),
+                    Sparkline(
+                        [],
+                        id="loss-sparkline",
+                        summary_function=min,
+                    ),
+                    classes="sparkline-container",
+                ),
+                Container(
+                    Log(id="training-logs"),
+                    classes="log-viewer",
+                ),
+                classes="job-details-container",
+            ),
+            classes="training-container",
+            id="content",
+        )
+        yield Footer()
+
+    def on_mount(self) -> None:
+        """Called when the screen is mounted."""
+        self.setup_job_table()
+        self.start_update_timer()
+
+        log = self.query_one("#training-logs", Log)
+        log.write_line(
+            "Training manager ready. Select a configuration to start training."
+        )
+
+    def setup_job_table(self) -> None:
+        """Setup the job table."""
+        table = self.query_one("#job-table", DataTable)
+        table.add_columns("ID", "Config", "Status", "Epoch", "Loss", "Duration")
+        table.cursor_type = "row"
+        table.zebra_stripes = True
+
+    def start_update_timer(self) -> None:
+        """Start the periodic update timer."""
+        self.set_interval(2.0, self.update_job_status)
+
+    @work(thread=True)
+    async def update_job_status(self) -> None:
+        """Update job status periodically."""
+        for job_id, job in self.jobs.items():
+            if job.status == "running" and job.process:
+                poll = job.process.poll()
+                if poll is not None:
+                    if poll == 0:
+                        job.status = "completed"
+                    else:
+                        job.status = "failed"
+                    job.end_time = datetime.now()
+
+        self.refresh_job_table()
+        self.update_selected_job_metrics()
+
+    def refresh_job_table(self) -> None:
+        """Refresh the job table."""
+        table = self.query_one("#job-table", DataTable)
+        table.clear()
+
+        for job_id, job in self.jobs.items():
+            duration = self.calculate_duration(job)
+            table.add_row(
+                job_id[:8],
+                Path(job.config_path).name,
+                job.status,
+                f"{job.current_epoch}/{job.total_epochs}",
+                f"{job.current_loss:.4f}" if job.current_loss else "N/A",
+                duration,
+            )
+
+    def calculate_duration(self, job: TrainingJob) -> str:
+        """Calculate job duration."""
+        if not job.start_time:
+            return "00:00:00"
+
+        end_time = job.end_time or datetime.now()
+        duration = end_time - job.start_time
+        hours = int(duration.total_seconds() // 3600)
+        minutes = int((duration.total_seconds() % 3600) // 60)
+        seconds = int(duration.total_seconds() % 60)
+        return f"{hours:02d}:{minutes:02d}:{seconds:02d}"
+
+    def update_selected_job_metrics(self) -> None:
+        """Update metrics for selected job."""
+        if not self.selected_job_id or self.selected_job_id not in self.jobs:
+            return
+
+        job = self.jobs[self.selected_job_id]
+
+        self.query_one("#epoch-metric", Static).update(
+            f"{job.current_epoch} / {job.total_epochs}"
+        )
+        self.query_one("#loss-metric", Static).update(
+            f"{job.current_loss:.4f}" if job.current_loss else "N/A"
+        )
+        self.query_one("#status-metric", Static).update(job.status.upper())
+        self.query_one("#duration-metric", Static).update(self.calculate_duration(job))
+
+        if job.losses:
+            sparkline = self.query_one("#loss-sparkline", Sparkline)
+            sparkline.data = job.losses[-50:]  # Show last 50 loss values
+
+    @on(DataTable.RowSelected)
+    def handle_row_selected(self, event: DataTable.RowSelected) -> None:
+        """Handle job selection from table."""
+        if event.cursor_row >= 0:
+            job_ids = list(self.jobs.keys())
+            if event.cursor_row < len(job_ids):
+                self.selected_job_id = job_ids[event.cursor_row]
+                self.update_selected_job_metrics()
+                self.load_job_logs()
+
+    def load_job_logs(self) -> None:
+        """Load logs for selected job."""
+        if not self.selected_job_id or self.selected_job_id not in self.jobs:
+            return
+
+        job = self.jobs[self.selected_job_id]
+        if job.log_file and Path(job.log_file).exists():
+            try:
+                with open(job.log_file, "r") as f:
+                    content = f.read()
+                    log = self.query_one("#training-logs", Log)
+                    log.clear()
+                    for line in content.split("\n")[-100:]:  # Show last 100 lines
+                        if line.strip():
+                            log.write_line(line)
+            except Exception as e:
+                log = self.query_one("#training-logs", Log)
+                log.write_line(f"Error loading logs: {str(e)}")
+
+    @on(Button.Pressed, "#new-training")
+    async def handle_new_training(self) -> None:
+        """Start a new training job."""
+        from axolotl.tui.dialogs.training import NewTrainingDialog
+
+        dialog = NewTrainingDialog()
+        result = await self.app.push_screen_wait(dialog)
+
+        if result and "config_path" in result:
+            await self.start_training_job(
+                result["config_path"], result.get("launcher", "accelerate")
+            )
+
+    @work(thread=True)
+    async def start_training_job(
+        self, config_path: str, launcher: str = "accelerate"
+    ) -> None:
+        """Start a training job."""
+        import uuid
+        from datetime import datetime
+
+        job_id = str(uuid.uuid4())
+        log_file = f"/tmp/axolotl_training_{job_id}.log"
+
+        job = TrainingJob(
+            id=job_id,
+            config_path=config_path,
+            status="pending",
+            start_time=datetime.now(),
+            log_file=log_file,
+            total_epochs=3,  # Default, should parse from config
+        )
+
+        self.jobs[job_id] = job
+        self.selected_job_id = job_id
+
+        log = self.query_one("#training-logs", Log)
+        log.clear()
+        log.write_line(f"🚀 Starting training job {job_id[:8]}...")
+        log.write_line(f"Config: {config_path}")
+        log.write_line(f"Launcher: {launcher}")
+
+        try:
+            if launcher == "accelerate":
+                cmd = ["accelerate", "launch", "-m", "axolotl.cli.train", config_path]
+            else:
+                cmd = [
+                    "torchrun",
+                    "--nproc_per_node=1",
+                    "-m",
+                    "axolotl.cli.train",
+                    config_path,
+                ]
+
+            with open(log_file, "w") as f:
+                process = subprocess.Popen(
+                    cmd,
+                    stdout=f,
+                    stderr=subprocess.STDOUT,
+                    text=True,
+                    bufsize=1,
+                )
+
+            job.process = process
+            job.status = "running"
+
+            log.write_line("✅ Training started successfully!")
+            self.refresh_job_table()
+
+            self.monitor_training_output(job_id)
+
+        except Exception as e:
+            job.status = "failed"
+            job.end_time = datetime.now()
+            log.write_line(f"❌ Failed to start training: {str(e)}")
+            self.refresh_job_table()
+
+    def monitor_training_output(self, job_id: str) -> None:
+        """Monitor training output and extract metrics."""
+        if job_id not in self.jobs:
+            return
+
+        job = self.jobs[job_id]
+        if not job.log_file:
+            return
+
+        def tail_log():
+            import re
+            import time
+
+            with open(job.log_file, "r") as f:
+                f.seek(0, 2)  # Go to end of file
+                while job.status == "running":
+                    line = f.readline()
+                    if line:
+                        # Parse training metrics from log
+                        epoch_match = re.search(r"Epoch (\d+)/(\d+)", line)
+                        if epoch_match:
+                            job.current_epoch = int(epoch_match.group(1))
+                            job.total_epochs = int(epoch_match.group(2))
+
+                        loss_match = re.search(
+                            r"loss['\"]?\s*:\s*([\d.]+)", line, re.IGNORECASE
+                        )
+                        if loss_match:
+                            job.current_loss = float(loss_match.group(1))
+                            job.losses.append(job.current_loss)
+
+                        # Update log viewer
+                        self.call_from_thread(self.append_training_log, line.strip())
+                    else:
+                        time.sleep(0.5)
+
+        thread = threading.Thread(target=tail_log, daemon=True)
+        thread.start()
+
+    def append_training_log(self, line: str) -> None:
+        """Append line to training log."""
+        log = self.query_one("#training-logs", Log)
+        log.write_line(line)
+
+    @on(Button.Pressed, "#stop-training")
+    def handle_stop_training(self) -> None:
+        """Stop selected training job."""
+        if not self.selected_job_id or self.selected_job_id not in self.jobs:
+            log = self.query_one("#training-logs", Log)
+            log.write_line("⚠️ No job selected")
+            return
+
+        job = self.jobs[self.selected_job_id]
+        if job.status == "running" and job.process:
+            job.process.terminate()
+            job.status = "stopped"
+            job.end_time = datetime.now()
+
+            log = self.query_one("#training-logs", Log)
+            log.write_line(f"🛑 Training job {job.id[:8]} stopped")
+            self.refresh_job_table()
+
+    @on(Button.Pressed, "#resume-training")
+    async def handle_resume_training(self) -> None:
+        """Resume a stopped training job."""
+        if not self.selected_job_id or self.selected_job_id not in self.jobs:
+            log = self.query_one("#training-logs", Log)
+            log.write_line("⚠️ No job selected")
+            return
+
+        job = self.jobs[self.selected_job_id]
+        if job.status in ["stopped", "failed"]:
+            await self.start_training_job(job.config_path)
+
+    @on(Button.Pressed, "#clear-completed")
+    def handle_clear_completed(self) -> None:
+        """Clear completed jobs from the list."""
+        completed_jobs = [
+            job_id
+            for job_id, job in self.jobs.items()
+            if job.status in ["completed", "failed", "stopped"]
+        ]
+
+        for job_id in completed_jobs:
+            del self.jobs[job_id]
+
+        self.refresh_job_table()
+        log = self.query_one("#training-logs", Log)
+        log.write_line(f"🧹 Cleared {len(completed_jobs)} completed jobs")
+
+    @on(Button.Pressed, "#refresh")
+    def handle_refresh(self) -> None:
+        """Refresh the job list and metrics."""
+        self.refresh_job_table()
+        self.update_selected_job_metrics()
+        if self.selected_job_id:
+            self.load_job_logs()
+
+    @on(Button.Pressed, "#view-logs")
+    def handle_view_logs(self) -> None:
+        """View full logs for selected job."""
+        if not self.selected_job_id or self.selected_job_id not in self.jobs:
+            return
+
+        job = self.jobs[self.selected_job_id]
+        if job.log_file and Path(job.log_file).exists():
+            import subprocess
+
+            subprocess.run(["less", job.log_file])
+
+    def action_new_training(self) -> None:
+        """Start a new training job."""
+        self.handle_new_training()
+
+    def action_stop_training(self) -> None:
+        """Stop selected training job."""
+        self.handle_stop_training()
+
+    def action_resume_training(self) -> None:
+        """Resume selected training job."""
+        self.handle_resume_training()
+
+    def action_refresh(self) -> None:
+        """Refresh the display."""
+        self.handle_refresh()

src/axolotl/utils/collators/__init__.py

@@ -1,19 +1,11 @@
-"""Shared axolotl collators for multipack, mamba, multimodal, etc."""
+"""
+shared axolotl collators for multipack, mamba, multimodal
+"""
 
-from .batching import (
+from .batching import (  # noqa: F401
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     PretrainingBatchSamplerDataCollatorForSeq2Seq,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
-from .mamba import MambaDataCollator
-from .streaming import StreamingDataCollator
-
-__all__ = [
-    "BatchSamplerDataCollatorForSeq2Seq",
-    "DataCollatorForSeq2Seq",
-    "PretrainingBatchSamplerDataCollatorForSeq2Seq",
-    "V2BatchSamplerDataCollatorForSeq2Seq",
-    "MambaDataCollator",
-    "StreamingDataCollator",
-]
+from .mamba import MambaDataCollator  # noqa: F401

src/axolotl/utils/collators/streaming.py

@@ -1,146 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, List
-
-import torch
-from transformers import PreTrainedTokenizerBase, default_data_collator
-from transformers.utils import PaddingStrategy
-
-from axolotl.prompters import Prompter
-from axolotl.utils.dict import DictDefault
-
-
-@dataclass
-class StreamingDataCollator:
-    tokenizer: PreTrainedTokenizerBase
-    cfg: DictDefault
-    prompter: Prompter | None = None
-    padding: bool | str | PaddingStrategy = True
-    max_length: int | None = None
-    pad_to_multiple_of: int | None = None
-    label_pad_token_id: int = -100
-    return_tensors: str = "pt"
-
-    def __post_init__(self):
-        if self.max_length is None:
-            self.max_length = self.cfg.sequence_len
-
-    def __call__(self, raw_batch: List[dict]) -> dict[str, Any]:
-        processed_samples = []
-
-        for raw_sample in raw_batch:
-            formatted_sample = raw_sample
-            if self.prompter:
-                formatted_sample = self._apply_prompt_formatting(raw_sample)
-
-            tokenized_sample = self._tokenize_sample(formatted_sample)
-
-            if len(tokenized_sample["input_ids"]) > self.max_length:
-                tokenized_sample = self._truncate_sample(tokenized_sample)
-
-            if tokenized_sample.get("input_ids"):
-                processed_samples.append(tokenized_sample)
-
-        return self._pad_and_batch(processed_samples)
-
-    def _apply_prompt_formatting(self, raw_sample: dict) -> dict:
-        formatted_text = self.prompter.build_prompt(
-            instruction=raw_sample.get("instruction", ""),
-            input=raw_sample.get("input", ""),
-            output=raw_sample.get("output", ""),
-        )
-        return {"text": formatted_text}
-
-    def _tokenize_sample(self, sample: dict) -> dict:
-        text = sample.get("text", sample.get("content", ""))
-
-        if not text:
-            instruction = sample.get("instruction", "")
-            input_text = sample.get("input", "")
-            output_text = sample.get("output", "")
-
-            parts = []
-            if instruction:
-                parts.append(f"Instruction: {instruction}")
-            if input_text:
-                parts.append(f"Input: {input_text}")
-            if output_text:
-                parts.append(f"Output: {output_text}")
-            text = "\n".join(parts)
-
-        if not text:
-            return {"input_ids": [], "attention_mask": [], "labels": []}
-
-        tokenized = self.tokenizer(
-            text,
-            truncation=False,
-            padding=False,
-            return_tensors=None,
-        )
-
-        tokenized["labels"] = tokenized["input_ids"].copy()
-        return tokenized
-
-    def _truncate_sample(self, tokenized_sample: dict) -> dict:
-        max_len = self.max_length
-        for key in ["input_ids", "attention_mask", "labels"]:
-            if key in tokenized_sample:
-                tokenized_sample[key] = tokenized_sample[key][:max_len]
-        return tokenized_sample
-
-    def _pad_and_batch(self, processed_samples: List[dict]) -> dict[str, Any]:
-        if not processed_samples:
-            processed_samples = [
-                {
-                    "input_ids": [self.tokenizer.eos_token_id],
-                    "attention_mask": [1],
-                    "labels": [self.tokenizer.eos_token_id],
-                }
-            ]
-
-        batch_samples = []
-        for sample in processed_samples:
-            batch_sample = {}
-            for key, value in sample.items():
-                if key in ["input_ids", "attention_mask", "labels"]:
-                    batch_sample[key] = torch.tensor(value, dtype=torch.long)
-            batch_samples.append(batch_sample)
-
-        if self.padding:
-            max_len_in_batch = max(len(sample["input_ids"]) for sample in batch_samples)
-
-            for sample in batch_samples:
-                current_len = len(sample["input_ids"])
-                pad_len = max_len_in_batch - current_len
-
-                if pad_len > 0:
-                    pad_token_id = (
-                        self.tokenizer.pad_token_id or self.tokenizer.eos_token_id
-                    )
-
-                    sample["input_ids"] = torch.cat(
-                        [
-                            sample["input_ids"],
-                            torch.full((pad_len,), pad_token_id, dtype=torch.long),
-                        ]
-                    )
-                    sample["attention_mask"] = torch.cat(
-                        [
-                            sample["attention_mask"],
-                            torch.zeros(pad_len, dtype=torch.long),
-                        ]
-                    )
-                    sample["labels"] = torch.cat(
-                        [
-                            sample["labels"],
-                            torch.full(
-                                (pad_len,), self.label_pad_token_id, dtype=torch.long
-                            ),
-                        ]
-                    )
-
-        batch = {}
-        for key in ["input_ids", "attention_mask", "labels"]:
-            if key in batch_samples[0]:
-                batch[key] = torch.stack([sample[key] for sample in batch_samples])
-
-        return batch

src/axolotl/utils/data/sft.py

@@ -9,7 +9,6 @@ from datasets import (
     Dataset,
     DatasetDict,
     IterableDataset,
-    IterableDatasetDict,
     load_dataset,
 )
 from transformers import PreTrainedTokenizer, ProcessorMixin
@@ -44,18 +43,6 @@ from axolotl.utils.trainer import (
 LOG = get_logger(__name__)
 
 
-def _determine_streaming_mode(cfg: DictDefault) -> bool:
-    """Determine if we should use streaming mode based on config."""
-    if cfg.streaming is not None:
-        return cfg.streaming
-
-    # Default to streaming for pretraining datasets
-    if cfg.pretraining_dataset:
-        return True
-
-    return False
-
-
 @retry_on_request_exceptions(max_retries=3, delay=5)
 def prepare_datasets(
     cfg: DictDefault,
@@ -74,52 +61,11 @@ def prepare_datasets(
     Returns:
         Tuple of (train_dataset, eval_dataset, total_steps, prompters).
     """
-    streaming_mode = _determine_streaming_mode(cfg)
-
-    if streaming_mode:
-        if cfg.pretraining_dataset:
-            return _prepare_streaming_pretraining_dataset(cfg, tokenizer, processor)
-        else:
-            return _prepare_streaming_sft_dataset(cfg, tokenizer, processor)
-    else:
-        if cfg.pretraining_dataset:
-            return _prepare_pretraining_dataset(
-                cfg, tokenizer, processor, preprocess_iterable=False
-            )
-        else:
-            return _prepare_standard_dataset(
-                cfg, tokenizer, processor, preprocess_iterable=False
-            )
-
-
-def _prepare_streaming_sft_dataset(
-    cfg: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    processor: ProcessorMixin | None,
-) -> tuple[IterableDataset, Dataset | None, int, list[Prompter | None]]:
-    LOG.info("Loading streaming datasets")
-
-    raw_datasets = _load_raw_datasets_for_streaming(cfg, split="train")
-
-    eval_dataset = None
-    if cfg.test_datasets:
-        eval_raw_datasets = _load_raw_datasets_for_streaming(
-            cfg, split="test", dataset_configs=cfg.test_datasets
+    if cfg.pretraining_dataset:
+        return _prepare_pretraining_dataset(
+            cfg, tokenizer, processor, preprocess_iterable
         )
-        eval_dataset = _process_eval_dataset_minimal(
-            eval_raw_datasets, cfg, tokenizer, processor
-        )
-    elif cfg.val_set_size:
-        LOG.info("Validation splits not supported for streaming datasets")
-
-    if not cfg.max_steps:
-        raise ValueError("max_steps must be set when using streaming datasets")
-
-    total_num_steps = cfg.max_steps
-    LOG.info(f"Maximum steps: {total_num_steps}")
-
-    prompters = [None] * len(cfg.datasets) if cfg.datasets else []
-    return raw_datasets, eval_dataset, total_num_steps, prompters
+    return _prepare_standard_dataset(cfg, tokenizer, processor, preprocess_iterable)
 
 
 def _prepare_standard_dataset(
@@ -427,7 +373,7 @@ def _load_and_process_single_dataset(
     d_base_type, d_prompt_style = _parse_dataset_type(dataset_config.type)
 
     # Select the appropriate split
-    if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
+    if isinstance(dataset, DatasetDict):
         if dataset_config.split and dataset_config.split in dataset:
             dataset = dataset[dataset_config.split]
         elif split in dataset:
@@ -566,78 +512,3 @@ def _load_and_prepare_datasets(
         train_dataset, eval_dataset = _handle_test_dataset_split(dataset, cfg)
 
     return train_dataset, eval_dataset, prompters
-
-
-def _load_raw_datasets_for_streaming(
-    cfg: DictDefault, split: str = "train", dataset_configs: list | None = None
-) -> IterableDataset:
-    configs = (
-        dataset_configs
-        if dataset_configs is not None
-        else (cfg.datasets if split == "train" else cfg.test_datasets)
-    )
-
-    if not configs:
-        raise ValueError(f"No dataset configurations found for split '{split}'")
-
-    datasets = []
-    for dataset_config in datasets_with_name_generator(configs):
-        raw_dataset = load_dataset_with_config(
-            dataset_config, cfg.hf_use_auth_token, streaming=True
-        )
-
-        if isinstance(raw_dataset, (DatasetDict, IterableDatasetDict)):
-            if dataset_config.split and dataset_config.split in raw_dataset:
-                raw_dataset = raw_dataset[dataset_config.split]
-            elif split in raw_dataset:
-                raw_dataset = raw_dataset[split]
-            else:
-                raise ValueError(
-                    f"no {split} split found for dataset {dataset_config.path}, "
-                    "you may specify a split with 'split: ...'"
-                )
-
-        datasets.append(raw_dataset)
-
-    if len(datasets) == 1:
-        return datasets[0]
-    else:
-        return merge_datasets(datasets, cfg)
-
-
-def _process_eval_dataset_minimal(
-    raw_dataset: IterableDataset,
-    cfg: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    processor: ProcessorMixin | None,
-) -> Dataset | None:
-    LOG.info("Eval dataset processing skipped for streaming")
-    return None
-
-
-def _prepare_streaming_pretraining_dataset(
-    cfg: DictDefault,
-    tokenizer: PreTrainedTokenizer,
-    processor: ProcessorMixin | None,
-) -> tuple[IterableDataset, Dataset | None, int, list[Prompter | None]]:
-    pretraining_config = _extract_pretraining_config(cfg)
-
-    train_dataset = load_dataset_with_config(
-        pretraining_config, cfg.hf_use_auth_token, streaming=True
-    )
-
-    if isinstance(train_dataset, (DatasetDict, IterableDatasetDict)):
-        if pretraining_config.split and pretraining_config.split in train_dataset:
-            train_dataset = train_dataset[pretraining_config.split]
-        elif "train" in train_dataset:
-            train_dataset = train_dataset["train"]
-        else:
-            raise ValueError("no train split found for pretraining dataset")
-
-    if not cfg.max_steps:
-        raise ValueError("max_steps must be set when using streaming datasets")
-
-    total_num_steps = cfg.max_steps
-    LOG.info(f"Maximum steps: {total_num_steps}")
-
-    return train_dataset, None, total_num_steps, []

src/axolotl/utils/data/utils.py

@@ -190,18 +190,12 @@ def handle_long_seq_in_dataset(
     Returns:
         Filtered dataset with long sequences removed.
     """
-    if hasattr(dataset, "column_names") and dataset.column_names:
-        if "input_ids" not in dataset.column_names:
-            LOG.warning(
-                "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
-                "expected for reward modeling."
-            )
-            return dataset
-    else:
-        # For IterableDataset, we can't check columns upfront, so skip for streaming
-        if isinstance(dataset, IterableDataset):
-            LOG.info("Skipping drop_long_seq for streaming datasets (not compatible)")
-            return dataset
+    if "input_ids" not in dataset.column_names:
+        LOG.warning(
+            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
+            "expected for reward modeling."
+        )
+        return dataset
 
     drop_long = functools.partial(
         drop_long_seq,

src/axolotl/utils/schemas/config.py

@@ -932,34 +932,6 @@ class AxolotlInputConfig(
 
     fix_untrained_tokens: int | list[int] | None = None
 
-    streaming: bool | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Whether to use streaming datasets (IterableDataset) for processing large datasets that don't fit in memory. When True, data is loaded on-demand during training without upfront preprocessing. Requires max_steps to be set. Pre-training datasets default to streaming unless explicitly set to False."
-        },
-    )
-
-    streaming_dataset_mixing_strategy: str | None = Field(
-        default="round_robin",
-        json_schema_extra={
-            "description": "Strategy for mixing multiple streaming datasets: 'round_robin' (equal sampling), 'weighted' (use streaming_mixing_weights), or 'random' (random sampling with equal probability)."
-        },
-    )
-
-    streaming_mixing_weights: list[float] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Weights for weighted mixing strategy when using multiple streaming datasets. Must sum to 1.0 and have same length as datasets list. Only used when streaming_dataset_mixing_strategy='weighted'."
-        },
-    )
-
-    streaming_buffer_per_dataset: int | None = Field(
-        default=1000,
-        json_schema_extra={
-            "description": "Buffer size per dataset when mixing multiple streaming datasets. Higher values may improve mixing quality but use more memory."
-        },
-    )
-
     # INTERNALS - document for now, generally not set externally
     is_preprocess: bool | None = None
     preprocess_iterable: bool | None = None

src/axolotl/utils/schemas/validation.py

@@ -1337,30 +1337,6 @@ class GRPOVllmValidationMixin:
 
 
 # pylint: disable=too-many-ancestors
-class StreamingValidationMixin:
-    """Validation methods related to streaming datasets."""
-
-    @model_validator(mode="after")
-    def check_streaming_requires_max_steps(self):
-        """Ensure max_steps is set when using streaming datasets."""
-        # Check if streaming is explicitly enabled
-        streaming_enabled = getattr(self, "streaming", None) is True
-
-        # Check if pretraining dataset exists (defaults to streaming)
-        has_pretraining = getattr(self, "pretraining_dataset", None) is not None
-        streaming_default_for_pretraining = (
-            has_pretraining and getattr(self, "streaming", None) is None
-        )
-
-        # If streaming is enabled (explicitly or by default for pretraining)
-        if streaming_enabled or streaming_default_for_pretraining:
-            max_steps = getattr(self, "max_steps", None)
-            if not max_steps:
-                raise ValueError("max_steps must be set when using streaming datasets")
-
-        return self
-
-
 class ValidationMixin(
     DatasetValidationMixin,
     AttentionValidationMixin,
@@ -1371,7 +1347,6 @@ class ValidationMixin(
     SystemValidationMixin,
     ChatTemplateValidationMixin,
     PretrainingValidationMixin,
-    StreamingValidationMixin,
     ModelCompatibilityValidationMixin,
     ComplexValidationMixin,
     GRPOVllmValidationMixin,