refactor: replace custom PEFT pipeline with Transformers+PEFT example

Remove the custom peft/ directory (LoRA/DoRA adapters, trainer, data
pipeline) in favor of a lightweight fine-tuning example that uses the
standard HuggingFace Transformers + PEFT ecosystem.

The new example at timesfm-forecasting/examples/finetuning/ demonstrates
LoRA fine-tuning via TimesFm2_5ModelForPrediction and the peft library,
based on the approach by @kashif at HuggingFace.

- Remove peft/ (8 files)
- Add timesfm-forecasting/examples/finetuning/finetune_lora.py
- Add timesfm-forecasting/examples/finetuning/README.md
- Update README.md to reference new example
- Clean up .gitignore (remove peft_checkpoints/)

timesfm-forecasting/examples/finetuning/README.md

@@ -0,0 +1,102 @@
+# Fine-Tuning TimesFM 2.5 with LoRA
+
+Parameter-efficient fine-tuning of
+[TimesFM 2.5](https://huggingface.co/google/timesfm-2.5-200m-transformers)
+using **HuggingFace Transformers** and **PEFT (LoRA)**.
+
+This approach is based on the fine-tuning workflow by
+[@kashif](https://github.com/kashif) at HuggingFace
+([notebook](https://github.com/huggingface/notebooks/blob/main/examples/timesfm2_5.ipynb)).
+
+## How It Works
+
+TimesFM 2.5 is available as a standard
+[Transformers](https://github.com/huggingface/transformers) model
+(`TimesFm2_5ModelForPrediction`). This means it supports the full Transformers
+ecosystem out of the box, including:
+
+- **PEFT adapters** — LoRA, QLoRA, etc. via the
+  [`peft`](https://github.com/huggingface/peft) library
+- **All attention backends** — eager, SDPA, Flash Attention 2/3, Flex Attention
+- **Standard `from_pretrained` / `save_pretrained` workflow**
+
+The model's forward pass natively computes a training loss when `future_values`
+are provided, so fine-tuning requires nothing more than a standard PyTorch
+training loop.
+
+## Quick Start
+
+### Install
+
+```bash
+pip install transformers accelerate peft pandas pyarrow scikit-learn
+```
+
+### Train
+
+```bash
+# Fine-tune with default settings on the retail sales dataset
+python finetune_lora.py
+
+# Custom hyperparameters
+python finetune_lora.py \
+    --epochs 20 \
+    --batch_size 64 \
+    --lr 5e-5 \
+    --lora_r 8 \
+    --lora_alpha 16 \
+    --context_len 64 \
+    --horizon_len 13 \
+    --output_dir my-retail-adapter
+```
+
+### Evaluate
+
+```bash
+# Evaluate a previously trained adapter (skip training)
+python finetune_lora.py --eval_only --output_dir timesfm2_5-retail-lora
+```
+
+## Key Concepts
+
+### No External Normalisation
+
+TimesFM 2.5 applies its own internal instance normalisation (RevIN). **Do not**
+normalise your data externally — feed raw values and let the model handle it.
+
+### Random Window Sampling
+
+Following [Chronos-2](https://github.com/amazon-science/chronos-forecasting),
+each training example is a random `(context, horizon)` window sliced from one of
+the input series. This is more data-efficient than always using the same
+fixed window per series.
+
+### LoRA Target Modules
+
+Using `target_modules="all-linear"` applies LoRA to every linear layer in the
+model. With `r=4` this adds only ~0.6% trainable parameters (~1.4M out of
+~232M), which is enough to meaningfully adapt the model to a new domain.
+
+## CLI Options
+
+| Flag | Default | Description |
+|------|---------|-------------|
+| `--model_id` | `google/timesfm-2.5-200m-transformers` | HuggingFace model ID |
+| `--context_len` | `64` | Context length for training windows |
+| `--horizon_len` | `13` | Forecast horizon in time steps |
+| `--epochs` | `10` | Training epochs |
+| `--batch_size` | `32` | Batch size |
+| `--lr` | `1e-4` | Learning rate |
+| `--lora_r` | `4` | LoRA rank |
+| `--lora_alpha` | `8` | LoRA alpha |
+| `--lora_dropout` | `0.05` | LoRA dropout |
+| `--num_samples` | `5000` | Random training windows to pre-sample |
+| `--output_dir` | `timesfm2_5-retail-lora` | Where to save the adapter |
+| `--seed` | `42` | Random seed |
+| `--eval_only` | — | Skip training; evaluate existing adapter |
+
+## Acknowledgements
+
+The Transformers integration and fine-tuning approach were developed by
+[@kashif](https://github.com/kashif) at HuggingFace. See the original notebook:
+<https://github.com/huggingface/notebooks/blob/main/examples/timesfm2_5.ipynb>