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>
22 lines
662 B
Python
22 lines
662 B
Python
"""
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memba — persistent memory layer for SSM-based LLMs.
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Quickstart
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----------
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from memba import Session
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s = Session("falcon-mamba-7b-Q4_K_M.gguf", session_id="research")
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print(s.chat("The Transformer architecture was introduced in 2017."))
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s.save()
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# Later — same session, picks up where it left off
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s2 = Session("falcon-mamba-7b-Q4_K_M.gguf", session_id="research")
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print(s2.chat("Who were the authors?"))
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"""
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from .session import Session
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from .core import save_state, load_state, get_state_size, compute_model_id
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__all__ = ["Session", "save_state", "load_state", "get_state_size", "compute_model_id"]
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__version__ = "0.1.0"
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