testing/README.md

testing

Everything that decides whether a bankml version ships, and what each version produced. A speed figure counts only if every oracle passed on the same code: the same bits first, then the speed.

Run

cargo test --release                                  # unit tests (in the modules) + testing/cli.rs, offline
BANKML_GGML_LIB=/path/to/llama-b11192 testing/release_gate.sh   # the full gate → testing/results/<version>.txt

The gate runs, and stops at the first failure: the build; the unit and CLI tests, and the C API's (bankml-capi); clippy -D warnings over the workspace; the licence headers (spdx_check.py); the Python suites — the guard, the UI data layer (test_ui.py), the console (test_console.py), the PostgreSQL connector (test_connectors.py, a throwaway cluster), the iNFT path (test_chain.py, a throwaway anvil), the model importer (test_models.py with a real carrier on spare ports); the Rust-against-Python guard agreement; the C library and the printf oracle. Then, when BANKML_GGML_LIB points at the llama.cpp b11192 release and the models are in .models/: the schema and content grammars re-recorded from llama.cpp's own code (with LLAMA_SRC), libstdc++'s sort orders (with g++), every #[ignore]d oracle, both kernel A/Bs, the prefill tile, the memory floor and both whole-model decode budgets, and the live checks against a running serve --native. Last, the converter against llama.cpp's convert_hf_to_gguf.py for each directory under .models/convert/, and the name heuristics (with BANKML_LLAMA_SRC).

The gate's stages, in order

Each stage prints a ## name line in the record. The #[ignore]d tests in the for t in … loop run one at a time (cargo test --release -- --ignored --exact <path> --nocapture --test-threads=1); each is described in the tests table.

stage	runs	checks
the oracle loop	oracle_tokenizer … oracle_ggml_b11192_real_ternary_bonsai_8b, the O4 oracles, then ab_vs_ggml, ab_vs_ggml_q2_0, bench_q1_0_prefill_act, bench_memory_floor, decode_budget_q1_0, decode_budget_q2_0	bits first, then the speed figures
serve_oracle_ollama_shape	serve_oracle.py --bankml	/api/chat == /v1 == llama-server's record, and again after unload and reload (Bonsai-8B)
o4_live	serve_oracle.py, json_oracle.py, json_schema_oracle.py, each --bankml STEM [NAME]	the same three live checks on Bonsai-1.7B, SmolLM2-135M-Instruct and mindx-gen39 (asked for as mindx-gen39)
persona_oracle_live	persona_oracle.py --bankml	the created mindx-gen39 with promote.py's persona layer: /api/chat, /api/generate, /v1, /api/ps
penalty_oracle_live	penalty_oracle.py --bankml mindx-gen39-F16 mindx-gen39	the penalties through /v1 and /api/chat; refusals as 400s with llama-server's message
sampler_oracle_live	penalty_oracle.py --kind sampler --bankml …	typical-p, top-n-σ, XTC, dynamic temperature and DRY through /v1 (typical-p also /api)
context_oracle_live	context_oracle.py --bankml Bonsai-1.7B-Q1_0	the context limit as llama-server's: stop at the full context, past it its 400 body
slot_oracle_live	slot_oracle.py Bonsai-1.7B-Q1_0	slot save, restore and erase: an answer after a restore equals an empty slot's, across a restart
session_oracle_live	session_oracle.py --bankml Bonsai-1.7B-Q1_0	interleaved conversations through llama-server's host prompt cache; simultaneous requests queued
kv_oracle_live	kv_oracle.py --bankml Bonsai-1.7B-Q1_0	the q8_0 KV cache against llama-server --cache-type-k/v q8_0
logprobs_oracle_live	logprobs_oracle.py --bankml Bonsai-1.7B-Q1_0	/v1 logprobs, plain and streamed: every logprob the same float
json_oracle_live	json_oracle.py --bankml	JSON mode and grammars: /v1 (once streamed) and /api/chat with format: "json"
json_schema_oracle_live	json_schema_oracle.py --bankml	JSON schemas: /v1 (once streamed) and /api/chat with format: <schema>
capi_chat_oracle	capi/capi_oracle.py --chat	bankml_chat == serve --native (ternary) == llama-server's record (1-bit); the refusals
oracle_convert_b11192 <dir>	convert::tests::oracle_convert_b11192 per .models/convert/<dir>/ with <dir>.oracle.gguf	bankml convert byte-identical to llama.cpp's converter
oracle_name_heuristics	convert_oracle.py --names, then convert::tests::oracle_name_heuristics	the name heuristics against gguf-py's

Each live stage starts target/release/bankml serve --native on spare ports and replays a record taken earlier from llama-server b11192 (the oracle script's --record mode, or its default mode for the older scripts); the records live in .models/oracle-*/, outside git.

What is here

file	what
release_gate.sh	the gate above (also appends to live.log)
pinned.sh	runs a benchmark on a fixed amount of processor and memory: pinned cores (taskset), a hard RAM cap with no swap (a user cgroup), and the machine's load recorded before and after
spdx_check.py	every source file carries the SPDX licence of its layer (LICENSING.md); in the gate
live.sh	runs one step with its output appended to live.log, which sAGI/savante.py --mode view shows live
cli.rs	end-to-end tests of the bankml binary (a cargo integration test): verdicts and exit codes, hostile headers, pin, verify, and serve against a mock llama-server (receipts, answer hashes, refusals)
forward_oracle.py	the forward pass's operations from the SHIPPED ggml, driven through ctypes as llama.cpp's Qwen3 graph drives them (get_rows on the Q1_0 embedding, rms_norm, mul), on 300 real token ids, for oracle_forward_*
template_oracle.py	records llama.cpp's own chat-template rendering (/apply-template) of 317 conversations — every rule of the Bonsai / Qwen3 template in bankml's scope, and 300 random ones — for oracle_chat_template
tokenizer_oracle.py	records llama.cpp's own /tokenize answers (a running llama-server) on a corpus — every doc, Savante's canon, edge cases and a seeded 2,000-string Unicode fuzz set, with special tokens parsed and not — for oracle_tokenizer
ggml_oracle.py	writes an oracle: llama.cpp b11192's own answers (its exported symbols, in-process) on a real GGUF — dequantized tensors, q8_0 rows, dot products
gguf_guard.py, test_gguf_guard.py	the Python guard the Rust one was ported from (vendored from minaiml), and its suite
test_chain.py	the iNFT path on a throwaway anvil devnet: deploy iNFT_7857, simulate, refusals, unsigned tx, mint, read-back, load, lineage
test_connectors.py	the PostgreSQL connector against a throwaway PostgreSQL 16 cluster with pgvector: publish, verified load, tamper refusal, injection as data, THOT generations
test_models.py	the model importer against a loopback server and a synthetic GGUF: the sha256 pin (tampered downloads discarded), the open-source licence gate, resume, the guard, Ollama adoption by link, URL and search parsing; with BANKML_TEST_CARRIER=1, a real carrier switch and rollback on spare ports
test_ui.py	the Savante UI's data layer, offline: CIDs, Merkle commitments, inclusion proofs (and their failures), RAGE search, metrics, .memory, and the view server's routes
test_console.py	0.3.7: the bankML console (sAGI/console.py) with no engine: its routes, its security (loopback Host, same-origin JSON POSTs, CSP), the SELF block as the model reads it (every value with its unit, "not measured" for a null), the persona's doctrine root, and the Infotags metadata (ERC-721 attributes, an RFC 6962 root over the log's lines); python3 testing/test_console.py
capi/printf_oracle.c	0.3.2: the C API's bankml_log (a C-variadic function defined in Rust) against libc snprintf, byte for byte, on every supported conversion; every unsupported one must give its marker and read no argument it cannot type
capi/chat.c, capi/capi_oracle.py	0.3.2 (0.3.4: also the O4 models against their records): a C program embedding libbankml; --chat compares bankml_chat with a live serve --native (ternary) and llama-server's record (1-bit), and checks the refusals; --printf builds and runs the printf oracle
grammar_oracle.cpp, grammar_oracle.py	0.3.3: llama.cpp b11192's own grammar sampler through libllama's public API on the pinned vocabulary: every token's piece and end flag, then the whole-vocabulary mask before every token of 196 runs (12 grammars — llama-server's JSON-mode grammar with its prefill, every grammar in llama.cpp's grammars/, three written for token terminals, edges and UTF-8 — × inputs × the tokenizer's tokens and one token per byte) and where llama.cpp rejects; for oracle_grammar_masks
json_oracle.py	0.3.3: --record MODEL launches llama-server b11192 (Savante's flags, --verbose) and records its /v1/chat/completions answers under response_format: json_object (and two user grammars on the 1-bit model), greedy and seeded, tokens and grammar included, for oracle_json_mode*; --bankml sends a subset through a live serve --native on /v1 (streamed once) and Ollama's /api/chat with format: "json"
f16_oracle.py	0.3.4: the shipped ggml's mul_mat on F16 weights through ctypes (one column → ggml_vec_dot_f16, two or more → llamafile's tinyBLAS, and the fallbacks): SmolLM2's real matrices by 1–28 columns and synthetic shapes for the tails; every F16 tensor widened by ggml_fp16_to_fp32_row; for oracle_ggml_b11192_f16
schema_oracle.cpp, schema_oracle.py	O6b (0.3.5: each template, schemas-<stem>.jsonl beside the Qwen3 schemas.jsonl): llama.cpp b11192's own json_schema_to_grammar and the chat path's schema grammar (common_chat_templates_apply, jinja, the pinned template read from the GGUF header, thinking off), called inside the release's libllama-common.so — no model loaded, no server — over 173 schemas: llama.cpp's own test cases (json_schema_cases.json, 81) and json_schema_corpus.json (Pydantic-shaped schemas like mindX's, the model oracle's schemas, edges and every refusal path); for oracle_schema_grammars
json_schema_oracle.py	O6b (0.3.5: every native model, --bankml STEM [NAME], and answers cut by max_tokens): --record MODEL records llama-server b11192's answers under JSON schemas (response_format: json_schema, top-level json_schema, json_object with a schema; objects, enums, ranges, $defs, a pattern, top-level array / enum / integer), greedy and seeded, with each request's exact text, for oracle_json_schema*; --bankml sends a subset through a live serve --native on /v1 (streamed once) and Ollama's /api/chat with format: <schema>
convert_oracle.py	O5: --record DIR OUT runs llama.cpp b11192's convert_hf_to_gguf.py --outtype f16; --bankml DIR ORACLE runs bankml convert and compares the sha256; --compare A B diffs two GGUFs key by key and tensor by tensor; --chkhsh DIR measures llama.cpp's pre-tokenizer hash and bankml's vocabulary+merges sha256 (the evidence for convert.rs's KNOWN_TOKENIZERS); --names OUT writes gguf-py's name heuristics for oracle_name_heuristics
pins/	O5: the FORK.json of each conversion proven byte-identical (SmolLM2-135M-Instruct, mindx-gen39), with every input's sha256 and the source revision
content_oracle.cpp, content_oracle.py	0.3.5: llama.cpp b11192's own common_chat_parse with the parser llama-server builds per request (libllama-common, no model), on every prefix of every recorded constrained answer and edge cases, per template; for oracle_json_content (the content rule, and the raw-text answer for an empty parse)
persona_oracle.py	0.3.5 (O5 end to end): --record MODELFILE DIR creates mindx-gen39 from promote.py's persona Modelfile two ways (FROM the merged directory, which must be named mindx-gen39 for the pin; and FROM mindx-gen39 in place) in scratch registries under .models/oracle-persona/, then records llama-server b11192 given the persona as the system message; --bankml asks the created model the user's turns alone through /api/chat, /api/generate and /v1 (each from an empty slot) and checks /api/ps; for oracle_persona_layer
guard_agree.py	runs both guards on every synthetic case and any real file given; exit 0 only if the JSON is identical
model_oracle.py	P3: the whole Qwen3 (and since 0.3.4 Llama, F16, tied-embedding) graph from the shipped ggml through ctypes, one layer at a time: per token and layer the sha256 of l_out, then result_norm and the logits; python3 testing/model_oracle.py GGUF LIBDIR [OUT], for oracle_forward_model*
greedy_oracle.py	P3: llama-server b11192's greedy tokens for short chat prompts (/apply-template, /tokenize, /completion, cache off); --long (≥ 64-token prompts, the tiled kernel) and --deep (past 256 cells, the split-KV kernel); python3 testing/greedy_oracle.py [URL] [N_PREDICT] [--long | --deep], for oracle_greedy_llama_server*
sample_oracle.py	P3: llama-server's seeded sampling (top-k, top-p, min-p, temperature), each case with the parameters the server read back (generation_settings); python3 testing/sample_oracle.py [URL] [N_PREDICT], for oracle_sample_llama_server and oracle_llama_server_*
serve_oracle.py	0.3.0: Savante-style conversations through a FRESH llama-server's /v1/chat/completions, turn after turn (text, counts, timings.cache_n), for oracle_native_serve*; --bankml [STEM [NAME]] is the live Ollama-shape check (0.3.1, 0.3.4)
train_oracle.py	mindXtrain's own Python (author stage data/scripts.py, score stage eval/imprint.py) on every persona and 3,000 random sets, for oracle_train_script and oracle_train_imprint; run with mindXtrain's interpreter: ~/mindxtrain/.venv/bin/python testing/train_oracle.py [~/mindxtrain]
penalty_oracle.py	O2 (0.3.6): llama-server's penalties sampler (repeat, frequency, presence over repeat_last_n, the prompt in the window), greedy and seeded, refusals kept with their message, for oracle_penalties*; 0.3.7: --kind sampler records the rest of the chain (typical-p, top-n-σ, XTC, dynamic temperature, DRY) for oracle_samplers*; --bankml STEM [NAME] replays every case live through /v1 and every fourth through /api/chat
sort_oracle.cpp	0.3.7: libstdc++'s own std::sort on 876 float-keyed index arrays (sizes 0–1,000, heavy ties, sorted, reversed, equal), the order typical-p leaves equal scores in; g++ -O2 -o target/sort_oracle testing/sort_oracle.cpp && target/sort_oracle > .models/oracle-sort/cases.txt (the gate does this), for oracle_std_sort
context_oracle.py	0.3.8: the context limit with context shift off, at --ctx 256: a full context stops with "length", a prompt that does not fit gets llama-server's 400 body; --record STEM (needs BANKML_GGML_LIB), then --bankml STEM
slot_oracle.py	0.3.8: POST /slots/0?action=save|restore|erase on serve --native; the oracle is the engine itself (an answer after a restore == an empty slot's, cache_n > 0, across a restart) and llama-server's refusals; python3 testing/slot_oracle.py [STEM]
session_oracle.py	0.3.8: the single-slot contract: three conversations taking turns through llama-server's host prompt cache (text, counts, cache_n, turn by turn), and four simultaneous requests all answered as if asked alone; --record STEM, then --bankml STEM
kv_oracle.py	0.3.9: the q8_0 KV cache: llama-server --cache-type-k q8_0 --cache-type-v q8_0 against serve --native with BANKML_CACHE_TYPE=q8_0 (greedy, seeded, a long prefill, a two-turn conversation); --record STEM, then --bankml STEM
logprobs_oracle.py	0.3.8: /v1 logprobs and top_logprobs: ids, texts and bytes equal, every logprob the same 32-bit float; streamed cases (stream-*) compare every chunk's delta and entries; refusals with the same status and message; --record STEM, then --bankml STEM
decode_ab.py	0.3.9: decode speed against llama-server b11192 in pairs (fresh servers each round, alternating order, each side's own timings); a round whose answers are not token-identical is refused; run pinned: BANKML_PIN_CPUS=1,2,3 BANKML_PIN_MEM=3000M testing/pinned.sh python3 testing/decode_ab.py Bonsai-8B-Q1_0 [rounds] [threads]. Not in the gate (it needs a quiet machine)
json_schema_cases.json, json_schema_corpus.json	the schema oracle's inputs: llama.cpp's own 81 test cases, and the Pydantic-shaped corpus with edges and refusals
results/<version>.txt	each release's record
experiments/	kernels that were measured and not adopted, with their numbers, for re-running elsewhere

The tests, and where they live

Rust unit tests sit next to the code they test. The ones marked real need the model files and the b11192 release, so they are #[ignore]d and run through the gate (cargo test --release -- --ignored <name> --nocapture --test-threads=1).

test	where	checks
guard cases t1–t9, fail-closed extras, nested arrays, KV overflow, tensor spans	gguf.rs	the three low-bit traps, and hostile headers refuse instead of crashing
real_bonsai_1_7b_q1_0 (real)	gguf.rs	the guard's report on the real file
FIPS vectors, FORK.json pin	sha256.rs	SHA-256 and the pin scanner
verify_runs_guard_then_pin	bankml.rs	the verify gate
Modelfile parser (Ollama's quoting, mindX's own Modelfiles), quote round trip, spec refusals, manifest digest, layer rules, create over a pin, create → tags → show → copy → delete through the HTTP router	create.rs	O5: bankml create and the Ollama endpoints, with no model loaded
name heuristics, regexes, permutation, metadata order; oracle_convert_b11192 (real), oracle_name_heuristics (real)	convert.rs	O5: bankml convert against llama.cpp b11192's convert_hf_to_gguf.py, byte for byte (convert_oracle.py)
create_writes_a_layer_over_a_pin, convert_refuses_with_the_reason	cli.rs	O5's CLI: exit codes, the manifest, the refusals
JSON reader, chunked/sized bodies	serve.rs	what serve parses from llama-server
f16 (all 65,536 values, and against F16C hardware), layouts, q8_0 rounding, AVX2 == scalar model (3,500 cases), mat_vec/mat_mul/*_par == per-pair	q1_0.rs	the 1-bit kernel against its scalar model of ggml
oracle_ggml_b11192_real_bonsai_1_7b, oracle_ggml_b11192_real_bonsai_8b_q1_0 (real)	q1_0.rs	every Q1_0 weight of Bonsai-1.7B and Bonsai-8B dequantized bit-exact; q8_0 rows and dot products bit-exact against ggml
ab_vs_ggml, decode_budget_q1_0 (real)	q1_0.rs	speed against ggml's own 1-bit kernel, in one process on the same bits; one token's 253 matmuls at 1–4 threads
layouts, portable and AVX2 paths == ggml model (3,300 cases), *_par == single thread	q2_0.rs	the ternary kernel against its scalar model
oracle_ggml_b11192_real_ternary_bonsai_8b (real)	q2_0.rs	every Q2_0 weight of Ternary-Bonsai-8B bit-exact; dot products against ggml's haswell and x64 builds
ab_vs_ggml_q2_0, decode_budget_q2_0 (real)	q2_0.rs	speed against ggml's ternary kernel
act_tile_bit_exact, bench_q1_0_prefill_act	q1_0.rs	the 0.0.4 prefill tile: bits, then speed
pool coverage, bench_pool_overhead, bench_memory_floor	par.rs	the thread pool, its wake-up cost, the machine's read bandwidth
reads_this_machine_and_this_process, stat_with_spaces_and_parens_in_the_name, busy_loop_is_seen_as_cpu	sys.rs	bankml's psutil: /proc memory, rss, CPU time and %
heads_are_bounded, only_loopback_hosts, unpaired_surrogates_become_replacement_characters, file_identity_changes_are_seen	serve.rs	the gateway's limits, Host check, JSON decoding and the model-identity re-check
keep_alive_as_ollama_reads_it, options_map_onto_the_engine, refusals_say_why, ndjson_framing, stop_strings_end_the_answer, names_resolve_to_pins, timestamps_are_rfc3339	bankML/ollama.rs	0.3.1: Ollama's API — keep_alive (5m, 1h, seconds, 0, −1), option mapping and refusals, NDJSON lines, stop strings held back across pieces, registry names and aliases
oracle_forward_attention (real)	forward.rs	P3 steps five and six: layer 0's kqv_out, attn_out, ffn_inp, l_out (f16 cache, causal flash attention, wo, residual, the FFN block) bit-exact against the shipped ggml, 112 of 112 rows
oracle_forward_model (real)	forward.rs	P3 step seven: the whole model (36 layers' l_out, result_norm, all 151,669 logits × 28 tokens) bit-exact against the shipped ggml, 1,064 of 1,064 rows
oracle_greedy_llama_server (real)	forward.rs	end to end: bankml's greedy tokens == llama-server b11192's on 6 of 6 chat prompts (164 tokens)
oracle_forward_model_ternary, oracle_greedy_llama_server_ternary (real)	forward.rs	P3 step eight: the ternary model, whole graph bit-exact (1,064 of 1,064 rows) and llama-server's greedy tokens on 6 of 6 prompts
oracle_forward_attention_tiled, oracle_greedy_llama_server_long (real)	bankML/forward.rs	P3 step nine: ggml's tiled flash attention on a 150-row micro-batch (150 of 150 rows) and llama-server's tokens on 6 of 6 long prompts
oracle_forward_attention_split, oracle_greedy_llama_server_deep (real)	bankML/forward.rs	P3 step ten: ggml's split-KV decode kernel (14 of 14 rows, 257–1,000 cells, 3 and 4 threads) and llama-server's tokens on 3 continuations running to ~300 cells (600 tokens)
oracle_sample_llama_server (real)	bankML/forward.rs, bankML/sampler.rs	P3 step eleven: llama-server's seeded sampling, 40 of 40 continuations (1,175 tokens) identical
gpu_q1_0_mat_vec_bit_exact	bankML/gpu/kernels.rs	both Q1_0 GPU kernels on every usable card, bit-exact against the CPU kernel (skipped without a GPU)
oracle_train_script, oracle_train_imprint	bankML/train/	mindXtrain's author and score stages, identical to mindXtrain's Python (84 of 84; 3,000 of 3,000)
oracle_native_serve (real)	bankML/native.rs	0.3.0: three Savante-style conversations through llama-server's own chat endpoint, replayed through bankML's native engine: text, counts and prompt-cache reuse identical on 9 of 9 turns
serve_oracle_ollama_shape (real, gate)	testing/serve_oracle.py --bankml	0.3.1: the same conversations through a live serve --native: /api/chat (NDJSON for the first), /v1/chat/completions, and /v1 again after unload and reload, each equal to the others and to llama-server's record, turn by turn
grammar::tests::*	bankML/grammar.rs	0.3.3: the server's JSON grammar parses; parse errors; a toy vocabulary's masks and accepts; partial UTF-8 across tokens; the C decoder's reading; requests resolved as llama-server resolves response_format/json_schema/grammar and Ollama's format; the JSON content rule, growth-only
oracle_grammar_masks (real)	bankML/grammar.rs	0.3.3: all 151,669 token pieces and the end set; then every recorded whole-vocabulary mask (1,645) and rejection (116) of 196 runs identical to llama.cpp b11192's grammar sampler; times one mask
oracle_json_mode, oracle_json_mode_ternary (real)	bankML/native.rs	0.3.3: llama-server's answers under JSON mode (and two GBNF grammars on the 1-bit model), greedy and seeded, from an empty cache: the same tokens, raw text, content, finish and counts; the server's grammar and generation prompt are bankML's
json_oracle_live (real, gate)	testing/json_oracle.py --bankml	0.3.3: a live serve --native: /v1 with response_format (once streamed) and grammar, and Ollama's /api/chat with format: "json", each equal to llama-server's record
schema::tests::*	bankML/schema.rs	O6b: nlohmann's JSON reading and printing (key order, duplicates, 1 vs 1.0, exponents); {"type": "object"} converts to JSON mode's constant; llama.cpp's own 81 test-json-schema-to-grammar cases with their expected grammars (each also parsed); refusals with llama.cpp's messages
oracle_schema_grammars (gate)	bankML/schema.rs	O6b: 173 of 173 schemas give llama.cpp b11192's grammar text byte for byte, bare and wrapped for the chat path (148 grammars each), or its refusal with the same message (24 bare, 20 wrapped); the one schema llama.cpp turns into a grammar that is not UTF-8 is refused by bankML; every grammar parses
oracle_json_schema, oracle_json_schema_ternary (real)	bankML/native.rs	O6b: llama-server's answers under JSON schemas, replayed from an empty cache: tokens, raw text, content, finish and counts; the server's grammar and generation prompt are bankML's
json_schema_oracle_live (real, gate)	testing/json_schema_oracle.py --bankml	O6b: a live serve --native: /v1 with each schema shape (once streamed) and Ollama's /api/chat with format: <schema>, each equal to llama-server's record
printf::tests::*, bankml_log_equals_snprintf, null_safe_and_refusing	capi/src/	0.3.2: the formatter on a typed argument queue (a read of the wrong type fails), the Rust-defined variadic bankml_log against libc snprintf called from Rust, NULL safety and refusals
printf oracle (gate)	testing/capi/printf_oracle.c	0.3.2: from C, bankml_log == libc snprintf on every supported case (47 of 47), every unsupported case marked (17 of 17), %n never written
capi_chat_oracle (real, gate)	testing/capi/capi_oracle.py --chat	0.3.2: bankml_chat == serve --native turn by turn on Ternary-Bonsai-8B (text, streamed pieces, counts, cache reuse, receipt hashes) and == llama-server's record on Bonsai-8B Q1_0; Bonsai-1.7B and an unpinned file refused
oracle_forward_swiglu_sweep	forward.rs	SwiGLU with ggml's vectorized expf on 24,600 values over ±120 and the edges, every output's bits
oracle_forward_qkv_rope (real)	forward.rs	P3 step four: layer 0's Qcur/Kcur/Vcur (projections, head norms, YaRN RoPE) bit-exact against the shipped ggml, 140 of 140 rows, positions 0–27 and to 63,214
oracle_forward_embed_norm (real), rms_norm_of_a_constant_row	forward.rs	P3 step three: inp_embd and attn_norm-0 bit-exact against the shipped ggml on 300 of 300 tokens
oracle_chat_template (real), a_short_conversation, python_split_semantics	chat.rs	P3 step two: every recorded conversation byte-identical to llama.cpp b11192's rendering (317 of 317)
oracle_tokenizer (real), pretokenizer_shapes, byte_chars_are_gpt2s	tokenizer.rs	P3 step one: every recorded case token-identical to llama.cpp b11192 (4,258 of 4,258; re-recorded in 0.3.4 on the grown corpus, 4,346 of 4,346, after the </s> fix)
avx2_equals_the_scalar_models, the_two_reductions_differ, mad_and_scale_match_the_scalar_definitions	bankML/f16.rs	0.3.4: the F16 kernels' AVX2 paths equal their scalar definitions (tails, fallbacks, 1–6 columns); ggml's two F16 reductions give different bits, so the path matters
oracle_ggml_b11192_f16 (real)	bankML/f16.rs	0.3.4: 211 of 211 F16 tensors widened and 552,268 of 552,268 mul_mat elements bit-exact against the shipped ggml (61 tinyBLAS products, 26 vec_dot_f16)
oracle_forward_model_bonsai_1_7b, oracle_forward_model_llama_f16 (real)	bankML/forward.rs	0.3.4: the whole model bit-exact — Bonsai-1.7B (tied embeddings) 840 of 840 rows; SmolLM2-135M-Instruct and mindx-gen39 (Llama, F16, tied) 800 of 800 each
oracle_llama_server_bonsai_1_7b, oracle_llama_server_llama_f16 (real)	bankML/forward.rs	0.3.4: llama-server's tokens on each model: greedy short, long and deep (SmolLM2, mindx-gen39), short (Bonsai-1.7B), seeded 40 of 40 each
oracle_native_serve_o4 (real)	bankML/native.rs	0.3.4: the Savante conversations (9 of 9 turns) and JSON mode (23 of 23) on Bonsai-1.7B, SmolLM2-135M-Instruct and mindx-gen39
oracle_tokenizer_smollm, smollm_pretokenizer_shapes	bankML/tokenizer.rs	0.3.4: SmolLM2's vocabulary and smollm pre-tokenizer, 4,346 of 4,346 cases
oracle_chat_template_chatml, chatml_templates	bankML/chat.rs	0.3.4: SmolLM2-Instruct's template and mindx-gen39's ChatML, 317 of 317 each
o4_live (real, gate)	testing/serve_oracle.py --bankml STEM [NAME], testing/json_oracle.py --bankml STEM [NAME]	0.3.4: the live Ollama-shape and JSON-mode checks on Bonsai-1.7B, SmolLM2-135M-Instruct and mindx-gen39 (asked for as mindx-gen39)
type_ids_match_mainline, verified_json_escapes_what_the_header_says	bankml.rs	type ids; /bankml's JSON stays valid for a hostile model name
hardware_path_equals_portable_on_every_length	sha256.rs	SHA-NI equals the portable rounds (lengths 0–1,000, 4 KiB, 64 KiB, split updates)
oracle_json_schema_o4 (real)	bankML/native.rs	0.3.5: answers under JSON schemas on Bonsai-1.7B, SmolLM2-135M-Instruct and mindx-gen39 (each template's own schema grammar), 56 of 56 each
oracle_json_content (gate)	bankML/grammar.rs	0.3.5: the content of a constrained answer, against llama.cpp's own common_chat_parse (content_oracle.py): every prefix of every recorded answer and edge cases, 30,063 of 30,063 texts per template
oracle_persona_layer (real)	bankML/create.rs	0.3.5: mindx-gen39 created with promote.py's persona Modelfile, asked the user's turns alone, gives llama-server's tokens for the persona as the system message (27 of 27, two ways)
oracle_penalties, oracle_penalties_8b (real)	bankML/native.rs	0.3.6: the repeat, frequency and presence penalties, greedy and seeded, token-identical to llama-server (56 of 56 on each of mindx-gen39, Bonsai-1.7B and Bonsai-8B), refusals with its message (12 of 12)
oracle_samplers, oracle_samplers_8b (real)	bankML/native.rs	0.3.7: typical-p, top-n-σ, XTC, dynamic temperature and DRY, alone and together (76 of 76 on mindx-gen39 and on Bonsai-1.7B, 16 of 16 refusals each; Bonsai-8B in oracle_samplers_8b, whose count the CHANGELOG does not yet give)
oracle_std_sort (gate), partial_sort_orders_the_top, mt19937_reference_value	bankML/sampler.rs	0.3.7: bankML's port of libstdc++'s std::sort leaves 876 of 876 arrays in the same order as sort_oracle.cpp; the partial sort and the generator
oracle_ggml_b11192_q8_0_kv_kernels (real)	bankML/forward.rs	0.3.9: the q8_0 KV cache's kernels against the shipped haswell library: quantize_row_q8_0 byte for byte and vec_dot_q8_0_q8_0 bit for bit (4,000 each, saturating cases included)
rates_and_energy_are_measured_or_null, the_ring_keeps_the_last_ones	bankML/metrics.rs	0.3.7: a measurement is taken or null, never estimated; the ring of 256
interleaved_conversations_find_their_prefix_again	bankML/prompt_cache.rs	0.3.8: the host prompt cache hands back the state that keeps more of the prompt
fit_messages_as_ollama	bankML/native.rs	0.3.5: num_ctx as Ollama's chatPrompt applies it (oldest messages dropped first)
logprob_texts_as_llama_server_writes_them	bankML/serve.rs	0.3.8: logprob texts and bytes, incomplete UTF-8 cut as llama-server cuts it

Results

version	record	headline
0.3.6	results/0.3.6.txt	O2: the repeat, frequency and presence penalties token-identical to llama-server (56 / 56 on each of three models; live 85 / 85); the last three stages run with BANKML_GPU=off, marked in the record
0.3.5	results/0.3.5.txt	O6b and O5: JSON schemas (173 schemas × 3 templates against llama.cpp's own code; answers 28 / 28, 11 / 11, 56 / 56 × 3), the content rule (30,063 / 30,063 per template), bankml create with mindX's persona layer (27 / 27, two ways); the last three stages run with BANKML_GPU=off, marked in the record
0.3.4	results/0.3.4.txt	O4: mindX's own mindx-gen39, SmolLM2-135M-Instruct (Llama, F16) and Bonsai-1.7B (tied embeddings) token-identical to llama-server on every oracle family; F16 products bit-exact against ggml
0.3.3	results/0.3.3.txt	JSON mode: llama-server's grammar, prefill and redraw; token-identical greedy and seeded
0.3.2	results/0.3.2.txt	the C API (libbankml): bankml_chat identical to serve --native and llama-server's record; bankml_log identical to libc snprintf; Rust 1.99 passing every bit-exact oracle
0.3.1	results/0.3.1.txt	Ollama's API on serve --native: /api/chat identical to /v1 and llama-server's record, unload/reload unchanged
0.3.0	results/0.3.0.txt	milestone: Savante answered by bankML's own forward pass; conversations identical to llama-server's (9 of 9 turns)
0.2.14	results/0.2.14.txt	the GPU in the forward pass, every token oracle exact with it working; the exact FMA; the on-card oracle catches an unfused driver
0.2.13	results/0.2.13.txt	GPU kernels bit-exact on the Vega 3; mindXtrain author and score stages identical
0.2.12	results/0.2.12.txt	batched prefill (checked by every prefill oracle); the GPU component's discovery
0.2.11	results/0.2.11.txt	P3 step eleven: sampling — llama-server's chain, same seed, same tokens (40 of 40)
0.2.10	results/0.2.10.txt	P3 step ten: long contexts — the split-KV kernel (14 of 14) and 600 tokens past 256 cells identical
0.2.9	results/0.2.9.txt	P3 step nine: long prompts — the tiled kernel (150 of 150) and llama-server's tokens on 6 of 6 long prompts
0.2.8	results/0.2.8.txt	P3 step eight: the ternary model, bit-exact and token-identical; about 8× llama-server end to end
0.2.7	results/0.2.7.txt	P3 step seven: the whole model bit-exact (1,064 of 1,064 rows); greedy generation token-identical to llama-server (6 of 6 prompts)
0.2.6	results/0.2.6.txt	P3 step six: the FFN block; layer 0 complete and bit-exact (112 of 112 rows), SwiGLU sweep 24,600 of 24,600
0.2.5	results/0.2.5.txt	P3 step five: layer 0's attention, wo and the residual bit-exact against the shipped ggml (84 of 84 rows)
0.2.4	results/0.2.4.txt	P3 step four: Q/K/V, head norms and YaRN RoPE bit-exact against the shipped ggml (140 of 140 rows)
0.2.3	results/0.2.3.txt	P3 step three: the embedding and the first RMS norm bit-exact against the shipped ggml (300 of 300 tokens)
0.2.2	results/0.2.2.txt	P3 step two: the chat template byte-identical to llama.cpp on 317 of 317 conversations; the ternary headline re-measured with the model resident (0.231 s, 9.45×)
0.2.1	results/0.2.1.txt	P3 step one: a Rust tokenizer token-identical to llama.cpp on 4,258 of 4,258 recorded cases
0.2.0	results/0.2.0.txt	milestone — docs pass (23 corrections), fourth audit fixed, the Q2_0 kernel prepared for llama.cpp (bit-exact 200,000/200,000)
0.1.9	results/0.1.9.txt	the system prompt's KV saved across restarts (first answer 132 s → 15 s, identical); history counted in engine tokens, never silently dropped
0.1.8	results/0.1.8.txt	SHA-NI pin 5.5×; a history window that keeps the cache warm; CPU/RAM sliders; sys.rs; pinned.sh; MIT OR Apache-2.0
0.1.7	results/0.1.7.txt	the second audit; hardened serve (limits, Host, request hash); RFC 6962 commitments; bge-m3 embedding
0.1.6	results/0.1.6.txt	the first audit of 0.1.5 fixed, each finding with a test
0.1.5	results/0.1.5.txt	the model importer (catalogue, Hugging Face, Ollama; open source only; sha256-pinned); metrics charts; Savante reads the thesis
0.1.4	results/0.1.4.txt	DreamKnobs in view mode (emerge on play, same voice chain); the timer counts real seconds
0.1.3	results/0.1.3.txt	Savante's own voice (Cori body, Jaimla's 182 Hz, SAVANTE resonance; slower, steadier); PLAY fixed and verified in a browser; lead PLAY, pause/resume, continue-until-stopped; 5 DreamKnobs emerging on play; oscilloscope; 3D depth
0.1.2	results/0.1.2.txt	Savante speaks: introduction (7 chapters) and voice examples in her voice; DreamKnob SPEED/FM; HF + GitHub links; view-mode Listen
0.1.1	results/0.1.1.txt	aivatar card (every persona aspect, verifiable identity), chosen aivatar (ledgered, THOT facet), drop zones + ⇄ + refined resize handles
0.1.0	results/0.1.0.txt	milestone — the full gate: every kernel oracle, A/Bs, budgets, and the UI, PostgreSQL and iNFT-devnet suites
0.0.9	results/0.0.9.txt	custom agents (keccak256 doctrine root = Savante's), THOT manifests (= the spec's 3 test vectors), PostgreSQL publish/load (throwaway-cluster tests)
0.0.8	results/0.0.8.txt	.history with ragebar (RAGE), Responses (copy, save to .memory, proof), .memory, Metrics, commitments + inclusion proofs; docs to 0.0.8
0.0.7	results/0.0.7.txt	LAN view (stdlib), drag and resize layout, response timer and times in .history, usage.md (no Rust change)
0.0.6	results/0.0.6.txt	bankml serve (P0) with receipts; Savante UI, view / interact; iNFT ledger checked 12/12
0.0.5	results/0.0.5.txt	three ternary experiments, bit-exact, none reliably faster (no kernel change); kernels at the Zen+ instruction limit; live log
0.0.4	results/0.0.4.txt	memory floor 15–17 GB/s (kernels are compute-bound); 1-bit prefill 1.27–1.33× ggml; two rejected decode experiments
0.0.3	results/0.0.3.txt	thread pool; 8B 1-bit oracle; ternary token matmuls 0.23–0.25 s at 3 threads (9.5–9.9× ggml), with the model resident
0.0.2	results/0.0.2.txt	audit: guard hardened (a crashing input now refuses), soundness fix, verify
0.0.1	results/0.0.1.txt	kernels bit-exact; ternary 9.5–9.8× ggml per matmul

0.3.7, 0.3.8 and 0.3.9 are unreleased; their measurements are in CHANGELOG.md until each has a gate record.

Tools beside the gate (tools/)

file	what
tools/cards.py	draws the README's speed cards (docs/cards/*.svg) from a gate record, so a card cannot drift from what was measured; python3 tools/cards.py [testing/results/<version>.txt] (default: the newest)
tools/makecards.py	draws a page's share cards (1200×630, 1200×1200 and a 180×180 touch icon) with the words given as arguments; needs Pillow and the Noto Sans fonts
tools/seo.py	audits a page's search and sharing metadata as a crawler and a link preview read them, and checks each card's real size against the declared one; standard library only; python3 tools/seo.py URL, or page.html --base URL / --offline
tools/bashmoji.sh	the installer's glyphs and colours, vendored from cryptoAGI/bashmoji