Add memba MVP: C++ core, Python SDK, CLI, examples, experiments

C++ core (libmemba.so):
- include/memba/state.h — C API (state_new/free/save/load/get_size)
- src/state.cpp — MEMB file format: magic, version, SHA-256 model_id,
  CRC-32, opaque llama_state_*_data() blob
- src/cli.cpp — minimal demo binary with greedy sampler
- CMakeLists.txt + build.sh with llama.cpp submodule, CUDA auto-detect

Python SDK (memba):
- core.py — file I/O via llama-cpp-python's exposed C functions,
  unwraps _LlamaContext to access raw context pointer (≥0.3.x)
- session.py — high-level Session with auto-save/load, ChatML wrapper
  for instruct models, raw mode for base models
- cli.py — typer-based: chat (REPL), run (one-shot), list, rm, info

Examples:
- 01_basic_save_load.py, 02_chat_session.py

Experiments (throwaway POCs documenting product-direction findings):
- recall_poc.py — git log → state → cross-process query
- mood_poc.py — batch sentiment trajectory, Mamba vs Transformer
- mood_stream_poc.py, mood_batch_poc.py — variants
- diag_saveload.py — minimal save/load isolation test
- README.md documents the headline finding: save/load is byte-identical,
  but Falcon-Mamba-7B-Instruct does not retain facts across conversation
  turns even in-process — limits viable products to single-prompt analysis
  and persona priming.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

experiments/mood_poc.py

@@ -0,0 +1,73 @@
+"""
+mood_poc.py — sentiment-trajectory test on Mamba vs Transformer.
+
+A chat log is constructed with a deliberate emotional arc:
+  msg 1-5   : optimistic / energetic
+  msg 6-10  : frustrated, hitting friction
+  msg 11-15 : burnt out, angry
+
+Both models see the same prompt and answer 3 questions:
+  Q1. Current mood at message 15
+  Q2. Trajectory from start to end
+  Q3. Approximate message number where mood shifted
+
+Pass criterion:  model identifies negative trend AND points at a shift
+between msgs 6-11.  Generic "they seem fine" or "they were happy throughout"
+counts as failure.
+"""
+
+from __future__ import annotations
+from llama_cpp import Llama
+
+MAMBA = "/home/emil/Desktop/Coding/AI/Memba/falcon-mamba-7B-instruct-Q4_K_M.gguf"
+GEMMA = "/home/emil/Programs/Llama.cpp/llama.cpp/models/gemma-4-E4B-it-Q8_0.gguf"
+
+CHAT_LOG = [
+    "Morning team! Coffee in hand, ready to tackle the auth refactor today.",
+    "Just pushed PR #234 fixing the token validation bug. Should be a quick merge.",
+    "Code review comments came in fast, all good catches. Iterating now.",
+    "Basic flow working locally, tests passing. Feeling good about this.",
+    "Heading to lunch, hopefully wrap this up by EOD.",
+    "Back. CI is failing on something unrelated, looking into it.",
+    "OK the 'unrelated' thing is actually related. Auth tests use a stale fixture.",
+    "Why does the fixture rebuild take 12 minutes. Every. Single. Time.",
+    "Cancelled the run twice now. Going to bypass and run tests locally.",
+    "Local passes, CI fails. Classic.",
+    "Two hours gone on this fixture issue. Not even what I was supposed to be doing.",
+    "Now there's a merge conflict with main because someone restructured migrations.",
+    "Whoever shipped those migrations on a Friday afternoon, I will find you.",
+    "Closing the laptop. Will fight this tomorrow.",
+    "Actually no. One more try before I sleep.",
+]
+
+PROMPT = """You are observing a person's chat messages from one workday. Here they are in order:
+
+""" + "\n".join(f"[msg {i+1:>2}] {m}" for i, m in enumerate(CHAT_LOG)) + """
+
+Now answer these THREE questions, briefly and directly:
+
+Q1: What is this person's mood at message 15 (the last one)? One short sentence.
+Q2: How did their mood change from message 1 to message 15? One short sentence.
+Q3: Around which message number does the mood clearly shift from positive to negative? Just give the number.
+
+Format your answer as:
+A1: ...
+A2: ...
+A3: ..."""
+
+
+def run(label: str, model_path: str) -> None:
+    print(f"\n{'='*60}\n  {label}\n{'='*60}")
+    llm = Llama(model_path=model_path, n_ctx=4096, n_gpu_layers=-1, verbose=False)
+    out = llm.create_chat_completion(
+        messages=[{"role": "user", "content": PROMPT}],
+        max_tokens=300,
+        temperature=0.3,   # low temp so we test capability, not creativity
+    )
+    print(out["choices"][0]["message"]["content"])
+    del llm   # free GPU memory before loading next
+
+
+if __name__ == "__main__":
+    run("FALCON-MAMBA-7B-INSTRUCT (SSM)", MAMBA)
+    run("GEMMA-4-E4B-IT (Transformer)", GEMMA)