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v1.0.0-rc2 (2026-08-26)

Everything here came out of going back over rc1 looking for defects rather than waiting for them to arrive. Nothing in it changes what the runtime computes.

One answer changed shape. get_signature_defs/1 returned the names it read out of the model as atoms. Atoms are never reclaimed, so a node that kept loading models with names of their own grew the atom table until it came down. They are binaries now, which is what signature_keys/1, signature_inputs/2 and signature_outputs/2 next door have always answered. Code that matched #{serving_default := _} wants #{<<"serving_default">> := _}.

Chinese was being thrown away. BERT spaces out CJK ideographs before splitting on whitespace. The Swift file this tokenizer was ported from skips that step, which it can afford to because it serves one English model. Here a whole sentence arrived as a single word, ran past wordpiece's two hundred character limit and came back as [UNK]: a sentence whose every character was in the vocabulary was answered as nothing at all. tflite_beam_basic_tokenizer now does what BERT does, kana and hangul left alone for BERT's stated reason.

Tokenizing was paying for a process round trip per character. is_punctuation/1 called into the table's process for every code point, and that call plus the path lookup feeding it was 5.6us of the 5.6us a character cost: the tokenizing itself did not register. The table is read once per call now, from a persistent_term. Ordinary text went from 5.56us to 0.21us a character; the wordpiece accumulator and the two in the basic tokenizer were quadratic in their input and are not. 24,000 characters of Chinese went from 923ms and one wrong token to 13ms and 24,000 right ones.

Five ways to end a process that should have been answers. A malformed unicode table, a predict/2 input that was not binary data, an input element inside a list or map that was not, a table the punctuation set could not read, and an interpreter server that could not allocate: each of these matched against data it had not checked and took the caller, or the server, with it.

Four native guards that were being skipped. get_signature_defs/1 read the signature vectors without the in-use lock every other accessor takes. Three call sites reached past get_resource to enif_get_resource and so skipped the ownership check with it, one of them the rebuild that deletes and replaces the whole interpreter. Both buffer model constructors copied into whatever enif_alloc answered without looking at it, and it answers null. A rebuild that threw partway left the resource holding an interpreter LiteRT had already destroyed.

Downloads. A path component that arrived as a binary, which is every component arriving from Elixir, was not checked for .. at all: the comparison was against a string and filename:split/1 keeps the representation it is given. A component that lands outside the cache through a symlink is refused too, and a name that is not UTF-8 is accepted, because on a filesystem that promises no encoding that is a name and not an escape. An https download that cannot be verified is now refused rather than made without verifying: TFLITE_BEAM_CACERT names a store and is honoured or reported, and TFLITE_BEAM_UNSAFE_HTTPS is how someone asks for the old behaviour.

Edge TPU options were read and discarded. get_edge_tpu_context/1 accepted the options map and never looked at it, so performance, DFU and queue length all took their defaults while the caller was handed a context and an ok. They are read and checked now, and coral_get_edgetpu_context_options/1 reads back what the device was actually asked for. libcoral only forwards them for a device named with an index, so "usb:0" carries options where "" does not.

The suite went from 113 cases to 131. Every fix here has one, and every one of them was checked by putting the defect back.

v1.0.0-rc1 (2026-08-26)

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The runtime now comes from LiteRT rather than from TensorFlow. That is the whole of why this is 1.0.0 and not 0.4.0: ~> treats a two part 0.x requirement as everything below 1.0.0, so shipping this as 0.4.0 would have moved every existing ~> 0.3 user onto a different upstream on their next deps.update, without their asking. Under 1.0.0 they stay where they are until they say otherwise. The 0.4.0 release candidates carried the memory safety work and no 0.4.0 final was published; everything in them is here.

Nothing in the Erlang API was removed or renamed, the seven precompiled targets are the same seven, and each one asks for exactly the glibc it asked for in v0.3.12. What did change is one answer, described first below.

Changed

tflite_version/0 answers LiteRT's version, which is <<"2.2.0">>, where it used to answer TensorFlow's <<"2.21.0">>. The two are separate version lines and the numbers are not comparable: LiteRT's 2.2.0 is newer than TensorFlow's 2.21.0, not older. A delegate plugin loaded through tflite_beam_delegate:external/1 must match this number, and upstream offers no binary stable delegate interface, so a mismatch is undefined behaviour rather than an error. Rebuild plugins against LiteRT 2.2.0.
TfLite is built from tflite/ in the LiteRT tree instead of tensorflow/lite/ in TensorFlow's. TensorFlow is still fetched, because LiteRT's own build reaches into it for compiler/mlir/lite, TSL and XLA, but it is a build dependency now rather than the source of the runtime. See tensorflow_version/0 for which release that is.

Added

tflite_beam:source_tree/0 answers litert. It exists because nothing about a shared object says which sources it came from, and the ways to end up holding the wrong one are quiet: a precompiled artifact fetched because priv/ happened to be empty, a stale copy in _build, a local build whose includes resolved against TensorFlow because that tree is on the path for LiteRT's own reasons. Each of those links, builds, and passes most of a test suite. The C++ behind this function names a type only LiteRT's schema defines, so a binary built from anything else does not compile, and a release from before the move has no such function to ask.
tflite_beam:tensorflow_version/0 answers the TensorFlow release the build pulled in, <<"2.21.0-rc0">>. Worth having when something reads wrong, not for matching a plugin against.
The two 8 bit float types are reported, under the names Nx uses: {f, 8} for E5M2 and {f8_e4m3fn, 8} for E4M3FN. Neither goes out as a bare width, because the width is the one thing they share: reading E4M3FN bytes as E5M2 does not fail, it answers a different number, and 16#78 is 32768 under one and 256 under the other.

Fixed

A model path that is not there says so. Routing the loader through the verifier in v0.4.0-rc6 made every failure report "not a valid flatbuffer", including a path with a typo in it, which is both the commonest way to get here and the least accurate thing to say about it. A missing or unreadable file now names the reason the operating system gave, and the test that covers it no longer settles for {error, _}.

v0.4.0-rc6 (2026-08-24)

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Eleven faults on the path a caller actually takes, three of which end the emulator rather than return an error, and one of those writes integers of the caller's choosing past the end of a stack buffer. Four more answered with the wrong value or quietly dropped one. All were found by auditing the binding against TfLite's own contracts rather than by a crash report, and each is held by a test that fails without its fix.

Added

Model metadata now reports custom_metadata. The field has been on every subgraph in the schema all along and nothing here ever read it, so whatever a model author put there was unreachable.
tflite_beam:xnnpack_max_tensor_dims/0 reports the widest tensor the delegate in this build can describe, or nil where no delegate imposes a width, as the armv6 and armv7l targets do. It is the number the resize guard below enforces, so the rule is one a caller can ask about rather than discover by being refused. CMake lifts the value out of XNNPACK's own header at configure time, which keeps the guard, this function and the delegate from ever disagreeing.

Fixed

Resizing an input tensor past the delegate's width is refused. XNNPACK holds a tensor's dimensions in a std::array<size_t, XNN_MAX_TENSOR_DIMS> and bounds the count only when it first decides to take the graph. Nothing rechecks it on the reshape that resize_input_tensor/3 reaches, so every dimension past the sixth was written off the end of that array, and what it wrote were the integers passed in from Erlang. Rank 7 and 8 tripped the stack protector, rank 10 took SIGBUS, and a dimension of 16#12345678 reached SIGSEGV. Only the unsafe transition is refused: a tensor already wider than the bound was never delegated and can still be reshaped, and a build without the delegate refuses nothing. The upstream code is unchanged in LiteRT, so the guard stays after the source tree moves.
Reading a tensor by index no longer depends on it having a name. TfLite leaves the name null on the scratch tensors an op allocates for itself, and the name helper ran strlen on that null, so walking a graph took the emulator down on any model with one. A detection model reaches it at index 261.
A truncated or corrupt model is refused instead of walked. build_from_buffer and build_from_file used the constructors that do not verify, so a model cut short segfaulted inside the NIF before returning anything. Both verify now, at the cost of a linear scan next to a copy the loader already made.
A tensor whose content is FeatureProperties keeps its metadata. That table is an empty marker in the schema, so there is nothing in it that can fail, but reading one was treated as a failure and discarded the whole content map built around it, content_properties_type and range included.
A score thresholding unit reports global_score_threshold under its own name. It was reported as default_score, which is a real and different field on the ScoreCalibrationOptions beside it, so the name did not merely read oddly, it named something else.
An absent optional file no longer empties a tensor's process_units. The tokenizer options treated a missing vocab_file as a failure, and the loop over the units gives up on the first failure, so a SentencePiece tokenizer shipping only its model discarded every unit on that tensor, unrelated ones included. The Bert and regex options had the same shape.
set_num_threads/2 accepts the values TfLite documents. Its own contract is ">= 0, or just -1 to let TFLite runtime set the value", and the interpreter refused everything below 1, so it turned away the one value that asks TfLite to choose. tflite_beam_interpreter_builder:set_num_threads/2 next to it always passed the integer straight through, and its spec has been widened from pos_integer() to match.
The model cache creates nested directories. Every HuggingFace repository id is owner/name and goes in as the cache subdirectory, but the cache called the non recursive file:make_dir/1, which fails when the parent is missing. All 88 models in the contrib catalogue were undownloadable.
Writing to a tensor takes exactly its size. A short binary used to be copied as far as it went and reported as success, leaving the rest of the tensor holding whatever the arena held before and producing an answer computed partly from that. A long one was truncated just as quietly. Both are refused now, and the error names both sizes.
predict/2 reports a bad input instead of crashing. The code that collected what went wrong while filling the inputs appended each failure with R/binary, which raises badarg on the {error, Binary} that set_data/2 actually returns. So the one path that had a real reason to report crashed instead of reporting it, and it crashed hardest where it mattered most, since every refusal from the interpreter guard arrives there. Inside tflite_beam_interpreter_server that badarg took the whole process with it, so one malformed request from one client destroyed the served model for everybody.
predict/2 no longer answers from a failed invoke. It discarded what invoke/1 returned and read the output tensors regardless, so a refused or failed run handed back the previous one's answer. Concurrently that is the fault the interpreter guard exists to close, arriving by a different door: 14 wrong answers in 400 calls before, 6 after. The rest is the gap between feeding, running and reading, which no per-call guard can close and tflite_beam_interpreter_server can, measured at 400 correct in 400.

v0.4.0-rc5 (2026-08-22)

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A NIF fault takes the emulator with it, and this release is almost entirely about the places where one could. Every defect below was found by reading the binding against TfLite's own contracts and then proving it with a test that fails without the fix, rather than by waiting for a crash report.

Fixed

A tensor handle now keeps its interpreter alive. It holds a pointer into that interpreter's arena and nothing kept the interpreter around on its behalf, so a handle could outlive what it pointed at and read freed memory. Erlang gives no warning before that happens: the compiler stops counting a variable as live at its last mention, so an interpreter someone fetched a tensor from and then never named again is collectable while the tensor is still in use.
Handles are retired when what they borrow moves. allocate_tensors/1, both resizes and a second build/2 all relocate tensors, and TfLite says plainly that Invoke may too. A handle taken before any of those now reports that it has been retired instead of reading through a stale pointer. Invoke is checked rather than assumed: only handles whose index no longer resolves to the pointer they hold are retired, so the ordinary fetch, set, invoke, read sequence still works.
Every entry point that touches an interpreter takes the same guard, not only the ones that write. Sixteen read-only calls held nothing while build/2 was free to delete the interpreter underneath them. Both handle types also read their liveness before taking that guard and never again, which left a window a rebuild fitted into exactly.
Cancelling still works during an invoke, which is the only time it is worth anything, but can no longer run while a rebuild is deleting the interpreter it is about to reach into.
Three places took a reference on a resource and then ran an allocating step before anything recorded it. A failure in between stranded the reference, and in one case left a mutex locked so that every later reader of that registry waited forever.
Every exported entry point is now behind an exception guard. It had been put on the twenty that were seen to allocate, and that claim was already false for the other sixty-four.
error_reporter_default_error_reporter/0 wrote through a null resource on the one path that already knew it was out of memory.
Six smaller ones: a byte written past the end of every error binary, five places the binding could read or write out of bounds, model metadata walked without being verified first, a leak in the signature runner registry, an error reporter a model could outlive, and get_associated_file/2 calling map:from_list when the module is maps.

Changed

tflitetensor_to_binary and tflitetensor_set_data moved to a dirty scheduler. Both copy the whole tensor, so their cost belongs to the model rather than being fixed: a 64 MB tensor takes 3.85 ms to read and 1.57 ms to write, well past what a normal scheduler should be holding. get_signature_runner and read_all_metadata moved for the same reason.
scripts/generate_checksums.sh now names every target it expects rather than counting them, and refuses to write a manifest that is missing one or that contains a tarball from another release. A manifest covering six of the seven targets is worse than none: the seventh refuses to install and the six that verify give no hint why, which is the shape of the 0.4.0-rc4 slip. The installer still refuses a file the manifest does not name, because the alternative would disable verification for a whole release at once.

Added

tflite_beam_lifetime_SUITE, seventeen cases that each stand for a defect that reached the repository. Every one was checked against a build without its fix: four of them abort the node there, one deadlocks, and the rest report the wrong answer or leak between five and seven megabytes.
A fault-injection facility for the windows that only open when an allocation fails, which no test can otherwise reach. It refuses to arm unless TFLITE_BEAM_ENABLE_FAULT_INJECTION is set in the environment before the node starts, so it is not something an application can reach by accident.

v0.4.0-rc4 (2026-08-21)

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Two processes sharing one interpreter used to get each other's answers -- silently, without a crash, 147 times in 400 on a real model. Most of this release is about that, and about checking that a downloaded binary is the one we published.

Added

tflite_beam_interpreter_server, an interpreter that lives inside a process so that feeding it, running it and reading the result back is one step nothing can interleave with. Concurrent callers are serialised by the process and each gets the answer to its own input.
The direct API mirrors TfLite's C API faithfully, and that is the problem it is answering: nothing in the C API says those three calls have to be treated as one operation. Two processes taking turns badly get each other's results -- measured on a real model, 147 wrong answers in 400 calls, silently and without a crash. The direct API is unchanged for callers who would rather serialise access themselves.
tflite_beam_interpreter:controlling_process/1,2, following gen_tcp:controlling_process/2: while an interpreter belongs to nobody any process may take it, and once it belongs to someone only that process may hand it on. Every other process is then refused. A controlling process that dies releases it, since an interpreter has no equivalent of a socket being closed. Interpreters start out belonging to nobody, which is how they have always behaved.

Changed

Calls into one interpreter that genuinely overlap in time are now refused instead of being allowed to race. Two processes sharing an interpreter used to reach TfLite on two OS threads at once with nothing in the way; the second one is now told. This is the only change here that alters existing behaviour, and only for code that was already racing.

Security

Precompiled tarballs are checked against a sha256 manifest before being unpacked. They were written to disk and extracted unverified, while every comparable BEAM package -- evision, xla, emlx -- verifies. The manifest, checksum.term, ships inside the package, because a checksum fetched alongside the thing it vouches for vouches for nothing.
A tarball that does not match is deleted and the build fails, rather than being left in the cache to fail identically forever. The cached path is checked too: a tarball that was already on disk has no more claim to being the right one than a freshly fetched one. A checkout with no manifest -- a git tag, whose tarballs are built after it exists -- says so loudly and carries on, since the manifest is the trust root rather than something to fetch.

v0.4.0-rc3 (2026-08-19)

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Delegates. A delegate is now a thing you can hold, configure and attach, rather than something TfLite did to your model without telling you -- and any vendor's delegate library can be loaded at runtime, the Edge TPU included.

Still a release candidate: the one behaviour change here, XNNPACK moving from TfLite's invisible lazy application to an explicit attachment at build time, is worth having in the open before it becomes 0.4.0.

Added

tflite_beam_interpreter_builder:add_delegate/2,3, and the delegate resource behind it. This is the attachment point rather than a usable feature yet: nothing in this release constructs a delegate, so the constructors arrive with the delegate kinds themselves. A delegate is kept alive by the builder and by every interpreter built from it, for as long as either needs it, which is why there is no way to detach or free one -- an early release is exactly the use-after-free class that 0.4.0-rc2 spent its time removing.
add_delegate/3 takes #{on_decline => error | fallback}. TfLite reports a delegate that cannot take the graph, but leaves it runnable, as kTfLiteApplicationError, and then discards the whole interpreter -- so without this a delegate that merely does not fit turns build/2 into an error with no interpreter at all, where a C++ caller would still hold a working CPU one. error, the default, keeps that loud. fallback builds again without the delegates that were added with it and answers {ok, delegate_declined}; nothing else is retried.
tflite_beam_delegate:available/0, reporting which delegate kinds were compiled into this build. It answers "was it compiled in", not "is a device present" -- those have different answers on the same binary. It lists xnnpack everywhere except armv6 and armv7l, where XNNPACK is not compiled in at all, and external on every target, since loading a plugin needs nothing but the dynamic loader.
tflite_beam_delegate:xnnpack/0,1, with num_threads, flags and weight_cache_file_path. Flags are atoms mapped by name -- qs8, force_fp16, disable_subgraph_reshaping and the rest -- and are added to XNNPACK's defaults rather than replacing them, because TfLite spells turning a default off as its own flag. Nothing positional would be right in any case: one bit in the middle of the range is unassigned.
tflite_beam_coral:edge_tpu_delegate/0,1, which reaches an Edge TPU the same way as any other delegate. libedgetpu has always been a TfLite delegate plugin -- the bundled runtime exports tflite_plugin_create_delegate and tflite_plugin_destroy_delegate -- so this is external/2 pointed at it, plus a default path to the copy in priv/libedgetpu. Pass lib_path to name a runtime installed elsewhere, which is how a build made without Coral support can still reach a device.
What it buys over make_edge_tpu_interpreter/2, which is unchanged and still works: that function builds its own interpreter internally, so nothing set on a builder ever reaches it -- neither set_num_threads/2 nor any other delegate. Going through the plugin puts an Edge TPU interpreter on the ordinary builder path. Both routes were checked to produce byte-identical output on a USB Coral accelerator with libedgetpu 0.1.14 on macOS arm64, and asking for a device that is not attached is an ordinary error rather than a crash.
tflite_beam_delegate:external/1,2, which loads a delegate out of any shared library implementing TfLite's plugin interface -- Edge TPU, a GPU delegate built elsewhere, a vendor delegate this library has never heard of. Options are handed over as strings, since that is the whole of the plugin ABI, so atoms and integers are converted and at most 256 pairs fit.
It does not go through TfLiteExternalDelegateCreate. That function returns a pointer into a wrapper whose delegate it fills in only when the library loaded and the plugin returned a delegate, so a missing file, a library that is not a plugin, or a plugin that declines -- no device attached, say -- all hand back a non-null delegate whose Prepare is indeterminate. Attaching one of those jumps through a wild function pointer and takes the emulator with it. The plugin is loaded here instead, which has no such gap and gives every failure a name, including the plugin's own explanation of why it refused.
tflite_beam_ops_builtin_builtin_resolver:new/1 takes #{apply_default_delegates => boolean()}, deciding whether TfLite may apply its own delegates lazily inside allocate_tensors/1.

Changed

XNNPACK is now attached explicitly, by tflite_beam_interpreter_builder:build/2, instead of being applied invisibly by TfLite inside allocate_tensors/1. The acceleration is the same and so is the output; what changes is that the delegation is visible in the execution plan as soon as build/2 returns rather than only after allocation, and that it can be configured or declined at all. set_num_threads/2 still reaches XNNPACK: the delegate is built with the builder's thread count, or with one thread when it was never set, which is what TfLite's own default has always been.
Attach your own delegate and the default is not added. Ask the resolver for #{apply_default_delegates => true} and TfLite goes back to delegating by itself. On armv6 and armv7l, where XNNPACK is not compiled in, nothing is attached and nothing errors.
tflite_beam_interpreter_builder:build/2 and tflite_beam_interpreter:allocate_tensors/1 now run on a dirty CPU scheduler. Every delegate's Prepare and all of TfLite's graph partitioning happen inside those two, which is more than a regular scheduler should be asked to hold. coral_make_edgetpu_interpreter/2, which does build, delegate and allocate in one call, was already classified this way.

Documented

An interpreter, and any delegate attached to it, belongs to one process at a time. This was already true -- invoke/1 has run on a dirty scheduler for a long time, and there is no lock anywhere in the bindings -- it was simply never written down.

v0.4.0-rc2 (2026-08-19)

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Bug fixes only, no new API. Three of these interlock: a build that fails without saying so produces an empty interpreter, a guard that was supposed to reject empty interpreters waves it through, and the next accessor call takes the VM down with it. None of the three needs anything unusual to reach.

Changed

tflite_beam_interpreter_builder:build/2 returns {error, Reason} when the build fails. It returned ok unconditionally and discarded the status TFLite handed it, so a model that could not be built reported success and left an empty interpreter behind. Code that matched ok = build(...) on a model that was quietly failing will now fail at that match, which is the point.
In tflite_elixir this reaches TFLiteElixir.InterpreterBuilder.build!/2, which starts raising through deferror where callers used to meet a MatchError further down. That suite has no negative test for build/2 -- every call site in it is a happy path -- so nothing there will notice the difference.

Fixed

Reaching into an interpreter that a failed build/2 had emptied killed the VM with SIGSEGV. Every resource accessor set an error term when it found a null value and then returned the resource anyway, while every caller tests only the returned pointer, so the guard passed and the next line dereferenced null. All eight of them now return nothing, and the calls that used to crash return {error, Reason}.
build/2 no longer leaves previously fetched tensors pointing into freed memory. TFLite destroys the interpreter it is building into on the way in -- before it can fail, so this applies to failed builds too -- but the tensor handles cached by tflite_beam_interpreter:tensor/2 were never cleared. Fetching a tensor and then building again was a use-after-free.
Tensor handles now report that their interpreter has gone instead of reading freed memory. The interpreter marked each cached tensor when it was torn down, but nothing ever read that mark: all six NIFs taking a tensor checked only that its pointer was non-null, which a dangling pointer is.
This is visible in one more place than the two above: a handle does not keep its interpreter alive, so reading through one whose interpreter has already been collected now returns {error, Reason}' where it used to return whatever was left in the freed memory. Keep the interpreter reachable for as long as its tensors are in use -- which is what the code doing this correctly already does, or it would have been crashing. ### Added - A test suite,rebar3 ct, covering model loading, the builder, interpreters, tensors, invocation and signature runners, along with the failure cases above. It runs in CI on Linux x86_64 and macOS arm64. The four model fixtures it uses come from TensorFlow's own testdata and live intest/, which is not part of the published package. ## v0.4.0-rc1 (2026-08-19) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.4.0-rc1) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.4.0-rc1) A release candidate. Everything here is new surface rather than changed behaviour, but it is a lot of it at once, so it is worth a look before it becomes 0.4.0. Depend on it explicitly -- a pre-release is not picked up by a requirement like"~> 0.3". ### Added - Signature runners. A model's signatures could be listed withinterpreter:signaturekeys/1andget_signature_defs/1, but there was no way to run one, so tensors still had to be addressed by index and the order of a model's outputs guessed at.tflite_beam_interpreter:get_signature_runner/2now returns a runner, andtflite_beam_signature_runnerdrives it: names and counts of its inputs and outputs, reading and writing them by name, resizing them, allocating, invoking and cancelling. Passingnilas the key asks for the primary subgraph, which works on models that declare no signatures at all, so this is usable with older exports too. A runner belongs to the interpreter that handed it out and holds a reference to it, so it stays usable even after the interpreter's own term is collected. Like the interpreter it is not safe to use from several processes at once. -tflite_beam_interpreter:enable_cancellation/1andcancel/1. An invocation runs on a dirty scheduler and could not be interrupted;cancel/1does not block and is safe to call from another process, so a long inference can now be given up on. Withoutenable_cancellation/1beforehand, cancelling is an error. -tflite_beam_interpreter:release_non_persistent_memory/1, which hands back the memory that is only needed while invoking. Invoking again reallocates it, trading time for memory on devices short of the latter. -tflite_beam_interpreter:reset_variable_tensors/1, resetting all of a model's variable tensors. Only a single-tensor version existed. -tflite_beam_interpreter:get_allow_fp16_precision_for_fp32/1andset_allow_fp16_precision_for_fp32/2. -tflite_beam_interpreter:signature_inputs/2,signature_outputs/2,get_subgraph_index_from_signature/2andsubgraphs_size/1, which describe a model's signatures and subgraphs without having to build a runner. -tflite_beam_interpreter:resize_input_tensor/3andresize_input_tensor_strict/3. Input shapes could not be changed at all before, so a model with a variable dimension could only ever be fed whatever shape it was exported with. Callallocate_tensors/1again afterwards. The strict variant only touches dimensions the model left unknown. -tflite_beam_flatbuffer_model:verify_and_build_from_buffer/1,2. A verifying counterpart existed for files but not for buffers, so a model already in memory could only be built unchecked. ### Fixed - Theminimum_runtimefield of thetflite_beam_flatbuffer_modelrecord held a boolean. Three of the four places that fill the record askedflatbuffer_model_initializedfor it, so anyone reading the field to decide whether a runtime is new enough was readingtrue. - Building a model from a buffer no longer leaks the copy of that buffer when the model turns out not to parse. ## v0.3.12 (2026-08-19) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.12) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.12) ### Fixed - Interpreters, interpreter builders and the resources they borrow (the op resolver and the flatbuffer model) now hold real references to each other. Previously only a hand-rolled counter recorded the link, so the VM was free to collect a resource that was still in use, and the destructor's decrement landed in whatever resource had been given that memory next. On arm64 this brought down the emulator with SIGBUS. - Resources are no longer read uninitialised.enif_alloc_resourcehands back raw memory, so fields that looked initialised in the struct definition were not, andNifResTfLiteTensorcould reachdeleteon a wild pointer. - Everytflite::FlatBufferModeland everytflite::Interpreterwas leaked; both are now released with the resource that owns them. - Creating an Edge TPU interpreter no longer leaks its resource when the interpreter cannot be built or its tensors cannot be allocated. - Tensor resources are no longer leaked. Each one was created with a reference that nobody ever gave back, on top of the one the interpreter's cache holds, so none of them could be freed. Failing partway through reading a tensor leaked one as well. - Edge TPU context resources are no longer leaked, for the same reason: the reference fromenif_alloc_resourcewas never released. -allocate_tensorsno longer reportsunknown errorfor three of the statuses TFLite can return. A model carrying ops the interpreter cannot resolve -- an Edge TPU model given to a plain builtin resolver, say -- now saysUnresolvedOpsinstead. The mapping lived in two places, one of which had drifted; there is now only one. - The Edge TPU itself is handed back when nothing is using it any more. Contexts were parked in a global map that was written to and never read, purely so theirshared_ptrcould not run out, which held the device until the VM exited. Each context resource now owns its share directly, and an Edge TPU interpreter holds a reference to the context it delegates to, so the device outlives every interpreter built on it and is released once the last one is gone. ## v0.3.11 (2026-08-15) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.11) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.11) ### Fixed - Platform-specific binaries now go to the consuming app's_build/<target><env>/lib/tflitebeam/privinstead ofdeps/tflitebeam/priv, so switchingMIX_TARGETno longer picks up another target'stflite_beam.so.rm -rf deps/tflite_beamis no longer needed when cross-compiling ([#73](https://github.com/cocoa-xu/tflite_beam/issues/73)). - Building from source no longer fails on hosts that have gflags installed system-wide (e.g.brew install gflags). glog resolved gflags throughfind_package, which picked up the system copy and collided with the targets the bundled gflags had already defined. ## v0.3.10 (2026-06-30) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.10) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.10) ### Changed - [deps] Use libedgetpu v0.1.14. - Use tensorflow v2.21.0. ## v0.3.9 (2025-04-03) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.9) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.9) ### Changed - [deps] Use libedgetpu v0.1.12. - Use tensorflow v2.19.0. ## v0.3.8 (2025-02-10) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.8) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.8) ### Changed - [deps] Use libedgetpu v0.1.10. - Use tensorflow v2.18.0. ## v0.3.7 (2024-09-03) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.7) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.7) ### Fixed - fixed project build directory ## v0.3.6 (2024-03-17) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.6) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.6) ### Changed - [deps] Use libedgetpu v0.1.9. - Use tensorflow v2.16.1. - Use libusb v1.0.27. - Use Erlang/OTP 25.x for precompiled binaries. This unified the required Erlang/OTP NIF version to2.16for precompiled binaries. - Detect and useHTTP_PROXY,HTTPS_PROXY,http_proxyandhttps_proxywhen fetch preocmpiled binary from GitHub. ## v0.3.5 (2024-01-24) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.5) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.5) ### Changed - Precompiled version for armv6 devices. - RemovedTFBEAM_XNNPACK_ENABLE_ARM_I8MMoption as it should work as long as a newer C compiler is used. - Updated metadata_schema to 1.5.0 ## v0.3.4 (2024-01-23) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.4) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.4) ### Changed - [deps] Use libedgetpu v0.1.8. - Use tensorflow v2.15.0. ## v0.3.3 (2023-07-21) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.3) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.3) ### Changed - [deps] Use libedgetpu v0.1.7. - Use tensorflow v2.13.0. ## v0.3.2 (2023-04-03) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.2) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.2) ### Fixed - [precompiled-nerves] Guess correctTARGET_ARCHfromTARGET_CPU. ## v0.3.1 (2023-04-03) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.1) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.1) ### Fixed - [deps] Use libedgetpu v0.1.6. ### Changed - [examples] Examples moved to [cocoa-xu/tflite_elixir](https://github.com/cocoa-xu/tflite_elixir). ## v0.3.0 (2023-04-02) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.3.0) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.3.0) ### Breaking Change - This repo will now be the TensorFlow Lite Erlang bindings. For Elixir bindings, please visit [cocoa-xu/tflite_elixir](https://github.com/cocoa-xu/tflite_elixir). ### Fixed - [erlang] Generate correct error message from a list of errors. - [c_src] Initialize resource pointers withnullptr. - Implemented tokenizers for MobileBERT (#57) by @cocoa-xu. - [make] Ensure priv dir exist. ## v0.2.1 (2023-04-02) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.2.1) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.2.1) ### Changed - [deps] Use TensorFlow Lite version 2.11.1. ### Fixed - [erlang] Generate correct error message from a list of errors. - [c_src] Initialize resource pointers withnullptr. - Implemented tokenizers for MobileBERT (#57) by @cocoa-xu. - [make] Ensure priv dir exist. ## v0.2.0 (2023-03-30) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.2.0) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.2.0) ### Breaking Changes - Renamed root namespace fromTFLiteElixirtoTFLiteBEAM### Changes -bufferwill be copied and managed when usingTFLiteBEAM.FlatBufferModel.build_from_buffer/1. -TFLiteBEAM.TFLiteTensor.dims/1returns a list (following TensorFlow Lite's C++ API convention) whileTFLiteBEAM.TFLiteTensor.shape/1returns a tuple (folllowingnx's convention.) ### Added - Erlang support. - [example] added pose estimation example (#43) by @mnishiguchi - [example] use thunder model instead of lightning in pose estimation (#45) by @mnishiguchi - [example] added audio classification example - Experimental high-level moduleTFLiteBEAM.ImageClassification. ```elixir iex> alias TFLiteBEAM.ImageClassification iex> {:ok, pid} = ImageClassification.start("test/test_data/mobilenet_v2_1.0_224_inat_bird_quant.tflite") iex> ImageClassification.predict(pid, "test/test_data/parrot.jpeg") %{class_id: 923, score: 0.70703125} iex> ImageClassification.set_label_from_associated_file(pid, "inat_bird_labels.txt") :ok iex> ImageClassification.predict(pid, "test/test_data/parrot.jpeg") %{class_id: 923, label: "Ara macao (Scarlet Macaw)", score: 0.70703125} iex> ImageClassification.predict(pid, "test/test_data/parrot.jpeg", top_k: 3) [ %{class_id: 923, label: "Ara macao (Scarlet Macaw)", score: 0.70703125}, %{ class_id: 837, label: "Platycercus elegans (Crimson Rosella)", score: 0.078125 }, %{ class_id: 245, label: "Coracias caudatus (Lilac-breasted Roller)", score: 0.01953125 } ] ``` ## v0.1.7 (2023-03-22) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.1.7) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.1.7) ### Breaking Changes - DeprecatedTFLiteElixir.Interpreter.allocate_tensors!/1- Deprecated Access behaviour forTFLiteElixir.FlatBufferModel### Fixed - Properly implementedTFLiteElixir.FlatBufferModel.read_all_metadata/1`. ```elixir iex> filename = Path.join([__DIR, "test", "test_data", "mobilenet_v2_1.0_224_inat_bird_quant.tflite"]) iex> %FlatBufferModel{} = model = FlatBufferModel.build_from_buffer(File.read!(filename)) iex> TFLiteElixir.FlatBufferModel.read_all_metadata(model) %{ TFLITE_METADATA: %{ description: "Identify the most prominent object in the image from a known set of categories.", min_parser_version: "1.0.0", name: "ImageClassifier", subgraph_metadata: [ %{ input_tensor_metadata: [ %{ content: %{ content_properties: %{color_space: "RGB"}, content_properties_type: "ImageProperties" }, description: "Input image to be classified.", name: "image", process_units: [ %{ options: %{mean: [127.5], std: [127.5]}, options_type: "NormalizationOptions" } ], stats: %{max: [255.0], min: [0.0]} } ], output_tensor_metadata: [ %{ associated_files: [ %{ description: "Labels for categories that the model can recognize.", name: "inat_bird_labels.txt", type: "TENSOR_AXIS_LABELS" } ], description: "Probabilities of the labels respectively.", name: "probability", stats: %{max: [255.0], min: [0.0]} } ] } ] }, min_runtime_version: "1.5.0" } `` ### Changed - ImproveTFLiteElixir.TFLiteTensor.to_nx/2(#33) by @cocoa-xu - [doc] Improve doc for to_nx (#31) by @mnishiguchi ### Added - Implemented -FlatBufferModel.{list_associated_files/1,get_associated_file/2}-TFLiteElixir.Interpreter.signature_keys/1-TFLiteElixir.Interpreter.execution_plan/1-TFLiteElixir.Interpreter.new_from_buffer/1-TFLiteElixir.Interpreter.tensors_size/1-TFLiteElixir.Interpreter.variables/1-TFLiteElixir.Interpreter.set_variables/2-TFLiteElixir.Interpreter.set_inputs/2-TFLiteElixir.Interpreter.set_outputs/2- [example] object detection example (#40) by @mnishiguchi ## v0.1.6 (2023-03-19) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.1.6) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.1.6) ### Fixed - [edgetpu] Improved edgetpu context handling, and bumped libedgetpu_runtime_version to v0.1.5. Fixed [#30](https://github.com/cocoa-xu/tflite_beam/issues/30) ### Added - [example] artistic-style-transfer example (#27) @mnishiguchi ## v0.1.5 (2023-03-18) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.1.5) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.1.5) ### Breaking Changes - Deprecated functions: -TFLiteElixir.FlatBufferModel.initialized!/1-TFLiteElixir.FlatBufferModel.get_minimum_runtime!/1-TFLiteElixir.TFLiteTensor.tensor!-TFLiteElixir.TFLiteTensor.to_nx!-TFLiteElixir.TFLiteTensor.to_binary!-TFLiteElixir.FlatBufferModel.build_from_buffer!-TFLiteElixir.FlatBufferModel.get_full_signature_list-TFLiteElixir.Coral.get_edge_tpu_context/1now takes keyword options. ### Changes - [example] Improve Inference on TPU notebook (#15) @mnishiguchi - [example] Improve Inference on TPU notebook (#16) @mnishiguchi - Alias modules in tflite_interpreter (#17) @mnishiguchi - Rename elixir files based on module names (#18) @mnishiguchi - add moduledocs (#19) @mnishiguchi ### Fixed - Fixed a few places that could lead to segmentation fault. - [example] Fixed broken ESRGAN link, Visualize the result section in the "Super Resolution" notebook. Lock downtflite_elixirandevisionversion (#29) @mnishiguchi. - [typespec] Fixed typespec forTFLiteElixir.Coral.edge_tpu_devices/0(#22) @mnishiguchi. ### Added - [test] Unit tests forTFLiteElixir.Interpreter,TFLiteElixir.InterpreterBuilderandTFLiteElixir.Ops.Builtin.BuiltinResolver. - [example] Added intro text to super_resolution_example. (#26) @mnishiguchi. -TFLiteElixir.FlatBufferModel.error_reporter/1. -TFLiteElixir.FlatBufferModel.verify_and_build_from_file/2## v0.1.4 (2023-03-14) [Browse the Repository](https://github.com/cocoa-xu/tflite_beam/tree/v0.1.4) | [Released Assets](https://github.com/cocoa-xu/tflite_beam/releases/tag/v0.1.4) ### Breaking Changes - Snake case functions (#21) @mnishiguchi ### Changes - [example] Improve Inference on TPU notebook (#15) @mnishiguchi - [example] Improve Inference on TPU notebook (#16) @mnishiguchi - Alias modules in tflite_interpreter (#17) @mnishiguchi - Rename elixir files based on module names (#18) @mnishiguchi - add moduledocs (#19) @mnishiguchi ### Fixed - Fix compilation logic when not using precompiled binaries. ### Added - ImplementedTFLiteElixir.reset_variable_tensor/1. - Add support for armv6. ### Misc - Simple workaround for cortex-a53 and cortex-a57,vcvtaq_s32_f32`. ## v0.1.3 (2023-03-09) Browse the Repository | Released Assets ### Changes - Bump TFLite version to v2.11.0. ## v0.1.2 (2023-03-08) Browse the Repository | Released Assets First release on hex.pm.

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