Add QK-norm toggle, TinyStories prep and automatic run logging

nanoGPT gets a qk_norm flag (off = upstream behaviour), a seed option and
a resolved-config dump. run.py logs each run's git commit, config, data
checksums, environment and metrics, and stops diverged runs.

Claude-Session: https://claude.ai/code/session_01S8KN7NuUCm8nXLmy6eDuVv

06-experiments/configs/spike.py

@@ -0,0 +1,35 @@
+# Tech-spike config: a tiny model on the small TinyStories subset, to check that the
+# pipeline (data -> train on MPS -> run log) works end to end. NOT an experiment config:
+# sizes, steps and LR here are placeholders, not decisions.
+
+dataset = 'tinystories_spike'
+device = 'mps'
+dtype = 'float32'          # train.py only uses autocast on CUDA; MPS runs in fp32
+compile = False            # torch.compile on MPS is not worth it for a spike
+
+# model (~3.2M non-embedding params; the GPT-2 vocab adds ~12.9M embedding params)
+n_layer = 4
+n_head = 4
+n_embd = 256
+block_size = 256
+bias = False
+dropout = 0.0
+qk_norm = False
+
+# optimisation (AdamW, nanoGPT defaults otherwise)
+batch_size = 32
+gradient_accumulation_steps = 1
+learning_rate = 1e-3
+max_iters = 300
+lr_decay_iters = 300
+min_lr = 1e-4
+warmup_iters = 30
+weight_decay = 0.1
+grad_clip = 1.0
+
+# logging / eval
+eval_interval = 100
+eval_iters = 20
+log_interval = 10
+always_save_checkpoint = False
+seed = 1337