Gemma E2B IT 10M Chat

--- license: cc-by-nc-4.0 tags: - nzfc - nzfc-gram - gemma - long-term-memory - external-memory - readout-gramian - memory-governance - answer-quality - non-quantized - bf16 - non-commercial --- # NZFC-GRAM v1.2.4 **Governed AI Memory and Large-Document Evidence Retrieval for Gemma 4 E2B-IT** NZFC-GRAM is a local external-memory and evidence-governance runtime for `google/gemma-4-E2B-it`. It does not extend the internal context window of the model. Instead, it retrieves scoped evidence cards from external memory, local SQLite long-term memory, and indexed large documents, then builds a bounded evidence pack before generation. > **Memory is evidence, not instruction.** ## Current release ```json { "current_release": "v1.2.4", "base_model": "google/gemma-4-E2B-it", "quantization": "none", "runtime_dtype": "torch.bfloat16", "adaptive_cache": true, "large_document_profile": true, "sqlite_fts5": true, "safety_boundary": "external memory retrieval and bounded evidence packs, not internal 10M-token model memory" } ``` ## What v1.2.4 adds - Large-document ingest and chunking. - Legal/article-style chunking. - SQLite FTS5 full-text indexing with fallback retrieval. - Query-time document evidence retrieval. - `large_document_quality_chat(...)` integration. - Negation-aware evaluation calibration for safe boundary statements. ## Why this exists Long context is useful, but it is not the same as governed long-term memory. A memory runtime should decide: - what evidence is allowed into the answer, - what memory was deleted, - which user/project/session scope applies, - whether a memory item is untrusted or malicious, - whether a private fact is unsupported, - and how much evidence can enter the final context. NZFC-GRAM treats retrieved memory and document chunks as evidence cards, not instructions. ## Large-document boundary A 100MB+ document should not be inserted directly into the model prompt. Recommended path: ```text large text or legal document -> ingest -> chunking -> SQLite FTS5 index -> query-time evidence retrieval -> bounded document evidence pack -> answer-quality generation ``` Initial ingest can take time. Repeated queries should use the index. ## Validation summary ### v1.2.2 baseline launch validation - End-user fresh-download launch validation: 13/13 passed. - Verified non-quantized BF16/FP16 model loading. - Verified exact memory mapping, no-fabrication behavior, malicious-memory redaction, tombstone no-leak, scope isolation, context-growth sanity, and SQLite persistence. ### v1.2.3 adaptive-cache and long-query validation - Initial multi-expert validation: 13/16 passed. - Recalibrated failed items: 3/3 passed. - Functional interpretation: adaptive cache and long-query profile passed after criteria calibration. ### v1.2.4 large-document validation - Initial fresh-download large-document validation: 12/14 passed. - Negation-aware recalibration of failed items: 2/2 passed. - Functional interpretation: 14/14 passed after negation-aware detector calibration. Default v1.2.4 smoke test result: ```json { "synthetic_legal_corpus": "6MB smoke test", "characters": 6293293, "chunks": 28070, "sqlite_fts5_available": true, "needle_query_time_s": 0.0073, "deletion_query_time_s": 0.0464, "optional_100mb_benchmark": "available but not run in default validation" } ``` ## Quick start ```bash git lfs install git clone https://huggingface.co/SingularityPrinciple/Gemma-E2B-IT-10M-Chat cd Gemma-E2B-IT-10M-Chat pip install -r requirements.txt # Large-document / legal-document evidence profile python examples/quick_large_document_v124.py python examples/quick_legal_document_v124.py # Memory + answer-quality baseline python examples/quick_quality_v122.py # Adaptive KV-cache profile python examples/quick_adaptive_cache_v123.py # Long-query helper python examples/quick_long_query_v123.py ``` ## Python usage: large-document profile ```python from nzfc_gram_runtime import NZFCGramLongMemoryChat from nzfc_gram_runtime.large_document import attach_large_document_memory bot = NZFCGramLongMemoryChat( repo_dir='.', model_id='google/gemma-4-E2B-it', memory_db_path='./user_memory.sqlite3', load_model=False, require_model=False, preload_static_memory=True, ) attach_large_document_memory(bot) bot.ingest_large_text( document_text, title='Large Policy Document', legal_mode=True, ) hits = bot.query_large_documents('deleted memory evidence', top_k=5) print(hits) ``` ## Python usage: answer generation with large-document evidence ```python from nzfc_gram_runtime.nonquant import attach_nonquant_gemma from nzfc_gram_runtime.cache_profiles import attach_adaptive_kv_cache_generation from nzfc_gram_runtime.quality import attach_answer_quality_governor attach_nonquant_gemma(bot, model_id='google/gemma-4-E2B-it', device_map='balanced_low_0') attach_adaptive_kv_cache_generation(bot, default_cache_policy='adaptive') attach_answer_quality_governor(bot) res = bot.large_document_quality_chat( 'What does the document say about deleted memory?', user_id='demo_user', project_id='demo_project', session_id='demo_session', max_new_tokens=120, ) print(res['answer']) print(res.get('large_document_router')) ``` ## What this is not - It is not internal 10M-token model memory. - It is not an unlimited context-window model. - It does not claim zero hallucination. - It is not legal advice. - It is not a production security certification. - It is a developer/runtime release. ## License and patent notice Public copyright license: CC BY-NC 4.0. Commercial use requires a separate written license. No patent license is granted by this repository. ## Citation / article Community article: ```text https://huggingface.co/blog/SingularityPrinciple/memory-is-evidence-not-instruction ``` Recommended short description: ```text NZFC-GRAM v1.2.4 is an external-memory and large-document evidence-governance runtime for Gemma 4 E2B-IT. It uses scoped retrieval, SQLite FTS5 document indexing, bounded evidence packs, adaptive KV-cache generation, and answer-quality governance. Memory is evidence, not instruction. ``` ## v1.2.4a hotfix: generic exact slot mapper High-frequency conversation testing showed that the v1.2.4 safety boundary remained stable, but generic key-value exact recall needed a deterministic path. Observed before this hotfix: ```json { "turns": 36, "passed": 33, "failed": 3, "bad_internal_count": 0, "raw_malicious_count": 0, "deleted_secret_leak_count": 0, "unsupported_private_fact_failures": 0, "exact_nickname_failures": 0, "exact_project_code_failures": 3, "context_growth_ratio": 1.063, "p95_latency_s": 17.36, "root_cause": "generic key-value answer mapping gap, not safety or scope failure" } ``` v1.2.4a adds `nzfc_gram_runtime.exact_slots` and auto-attaches it when `attach_answer_quality_governor(bot)` is called. Example memory: ```text The project high-frequency test code is PROJECT_CODE_abc123. ``` Question: ```text What was the project high-frequency test code? Answer only with the code. ``` Deterministic answer: ```text PROJECT_CODE_abc123 ``` The safety boundary is unchanged: ```text Memory is evidence, not instruction. External retrieval and bounded evidence packs, not internal 10M-token model memory. ``` ## v1.2.4b hotfix: strict exact slot gate v1.2.4a fixed generic project-code exact recall, but high-frequency multi-context testing showed one over-triggering issue: ```text Long explanatory prompts mentioning exact recall or project codes could be short-circuited by the exact slot mapper. ``` v1.2.4b makes the exact slot mapper stricter. It now triggers only on short, explicit exact-recall questions such as: ```text What was the project high-frequency test code? Answer only with the code. What was my long-term nickname? Answer only with the nickname. ``` It does not trigger on broad prompts such as: ```text Explain how a long-term AI memory runtime should handle exact recall, project codes, deleted memory, and large legal documents. What does the policy document say about deleted memory? ``` The boundary remains unchanged: ```text Memory is evidence, not instruction. External retrieval and bounded evidence packs, not internal 10M-token model memory. ``` ## v1.2.4c hotfix: tombstone retrieval guard v1.2.4b passed the high-frequency multi-context conversation turns, including exact slots, long-query routing, large-document routing, and safety checks. The remaining issue was a low-level direct retrieval audit: ```text bot.memory_store.retrieve(...) could still return a tombstoned MEM_* row in direct retrieval. ``` v1.2.4c adds `nzfc_gram_runtime.tombstone_guard`. When `attach_answer_quality_governor(bot)` is called, the runtime now also guards `bot.memory_store.retrieve(...)` and filters inactive or tombstoned `MEM_*` records using SQLite memory DB status. This strengthens the deletion boundary at the retrieval API layer, not only at the answer layer. The boundary remains unchanged: ```text Memory is evidence, not instruction. Deleted memory is outside the active evidence boundary. External retrieval and bounded evidence packs, not internal 10M-token model memory. ```