Fix linting errors - remove unused imports

Author: haasonsaas

README.md

@@ -84,7 +84,12 @@ This project contains two main components:
 | `data_loader.py` | JSONL data loader with proper Tinker renderers, loss masking, validation, and deduplication. |
 | `data_selector.py` | Utilities for mining hard examples based on evaluation failures. |
 | `hyperparam_utils.py` | Tinker's recommended LR formula and warmup/cosine scheduler. |
-| `simple_eval.py` | Minimal working evaluator for demo (replace with Inspect AI for production). |
+| `simple_eval.py` | Minimal working evaluator for demo (fallback if Inspect AI unavailable). |
+| `inspect_eval.py` | Inspect AI task integration with Tinker sampling adapter. |
+| `logger.py` | Structured JSON logging for metrics and events. |
+| `checkpoint_manager.py` | Checkpoint save/resume for interrupted runs. |
+| `error_handling.py` | Retry logic with exponential backoff and rate limiting. |
+| `mock_tinker.py` | Mock Tinker client for offline demos and CI. |
 | `requirements.txt` | Dependencies required to run the script. |
 | `tests/` | Unit and integration tests for all components. |
 
@@ -148,6 +153,14 @@ See [DEMO.md](DEMO.md) for a detailed walkthrough of what happens during the dem
 
    The script will fine‑tune the specified base model using LoRA, run evaluations, and iteratively improve the model until it meets your quality targets or a maximum number of rounds. If EvalOps integration is enabled, each evaluation round will be automatically submitted to your EvalOps workspace for tracking and analysis.
 
+6. **Resume an interrupted run:**
+
+   ```bash
+   python trainer_with_eval.py --config eval_loop_config.json --resume
+   ```
+
+   Continues from the latest checkpoint in `runs/` directory.
+
 ## EvalOps Integration
 
 This project includes built-in integration with [EvalOps](https://evalops.dev) to automatically track evaluation results across training rounds. The `evalops_client.py` module provides a lightweight Python SDK that:
@@ -216,7 +229,11 @@ The test suite includes:
 - Uses `save_weights_for_sampler()` for evaluation (not `save_state()` which includes optimizer state)
 - Supports Tinker's recommended LR formula: `LR = 5e-5 × 10 × (2000/H_m)^P_m` with model-specific exponents
 - Includes warmup + cosine decay scheduler for stable training
-- Gracefully falls back when tinker-cookbook unavailable (for testing/development)
+- Inspect AI integration with fallback to simple evaluator
+- Structured JSON logging to `runs/<timestamp>/metrics.jsonl`
+- Checkpoint resume with `--resume` flag
+- Mock mode for offline demos and CI (set `TINKER_MOCK=1`)
+- Gracefully falls back when tinker-cookbook unavailable
 
 ## Disclaimer