emilandClaude Opus 4.7
75c9ee4576
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>
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
"""
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02_chat_session.py — multi-turn chat that persists across Python processes.
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First run : model has no prior context.
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Second run : model continues from the saved SSM state.
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Run twice:
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python examples/02_chat_session.py --model path/to/falcon-mamba-7b-Q4_K_M.gguf
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python examples/02_chat_session.py --model path/to/falcon-mamba-7b-Q4_K_M.gguf
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"""
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import argparse
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from memba import Session
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TURNS = [
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"My name is Alex. I am a researcher studying ancient Roman aqueducts.",
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"What is the most famous aqueduct I should know about?",
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"How long did it take to build?",
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]
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", required=True, help="Path to GGUF file")
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parser.add_argument("--gpu-layers", type=int, default=0, help="GPU layers (0=CPU)")
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parser.add_argument("--session", default="aqueduct_research")
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parser.add_argument("--state-dir", default="~/.memba/states")
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args = parser.parse_args()
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print(f"Session: {args.session!r}")
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print(f"Model : {args.model}")
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print("-" * 60)
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sess = Session(
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model_path=args.model,
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session_id=args.session,
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state_dir=args.state_dir,
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n_gpu_layers=args.gpu_layers,
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verbose=False,
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)
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print(f"State size on load: {sess.state_size:,} bytes\n")
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# Only feed the turns that haven't been answered yet.
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# A real app would track which turns were already fed; here we keep it simple
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# and feed all TURNS every run — the SSM state update is idempotent in terms
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# of demonstrating cross-process continuity.
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for i, turn in enumerate(TURNS, 1):
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print(f"[Turn {i}] User: {turn}")
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reply = sess.chat(turn, max_tokens=200)
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print(f"[Turn {i}] Model: {reply}")
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print()
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saved_path = sess.save()
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print(f"State saved → {saved_path}")
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print(f"State size : {sess.state_size:,} bytes")
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print("\nRun this script again — the model will continue from this checkpoint.")
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if __name__ == "__main__":
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main()
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