feat: add model comparisons and sanitize session files

- Rename gamma to glm5 and model to minimax-m2.7
- Add model_comparison/ directory with head-to-head analyses
- Sanitize all session.jsonl files: remove absolute paths and usernames
- Remove __pycache__ artifacts
- Add .gitignore

minimax-m2.7/fuse/PROMPT.md

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+Design and implement a high-performance fused softmax + top-k kernel in CUDA (or CUDA-like pseudocode).
+
+Requirements:
+- Input: logits [B, T, V]
+- Output:
+    - top-k indices per (B, T)
+    - top-k probabilities (after softmax)
+
+Constraints:
+1. Do NOT materialize the full softmax matrix in global memory.
+2. Must be numerically stable (log-sum-exp).
+3. Minimize global memory reads/writes.
+4. Use shared memory where appropriate.
+5. Handle large V (e.g., 50k+) efficiently.
+
+Deliver:
+- Kernel pseudocode or CUDA code
+- Memory access pattern explanation
+- Warp-level optimization strategy
+- Complexity analysis (bandwidth vs compute bound)
+- Comparison to naive implementation