Add support for Arcee #1377
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Add support for Arcee #1377
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* ONNX Runtime improvements (experimental native webgpu; fix iOS) (#1231) * customize the wasm paths * update implementation * allow using 'webgpu' in nodejs binding * update version of onnxruntime-node * Upgrade onnxruntime-web to same version as onnxruntime-node * Update list of supported devices --------- Co-authored-by: Joshua Lochner <[email protected]> * customize the wasm paths (#1250) * customize the wasm paths * update implementation * [internal] Add is_decoder option to session retrieval for preferred output location * Update tests * Formatting * Bump ort versions * Bump onnxruntime-node version * Bump versions * Bump ORT versions * Bump versions * Only check webgpu fp16 for non-node environments * Fix * Assume node supports webgpu * Update ORT node support comment * Relax test strictness * Update conversion script versions * Downgrade onnxslim * cleanup * Update package-lock.json * Update onnxruntime versions * Update post-build script * Use built-in session release function * Call garbage collection after each tokenizer test * Do not double-throw error * Fix race-condition in build process with file removal * Update versions * Bump jinja version * [version] Update to 3.6.3 * Bump jinja version to support new features * [version] Update to 3.6.3 * Add support for LFM2 models (#1367) * Use prefix in lfm2 output location (#1369) * Update package-lock.json * Run `npm audit fix` * Add special tokens in text-generation pipeline if tokenizer requires (#1370) * Add special tokens in text-generation pipeline if tokenizer requires * Fix logits processors tests * Update bundles.test.js * Update comment * Formatting * Add support for ModernBERT Decoder (#1371) * Use from/to buffer instead of string Actually fixes #1343 * Add support for Voxtral (#1373) * Support longform voxtral processing (#1375) * [version] Update to 3.7.0 * Add support for Arcee (#1377) * Optimize tensor.slice() (#1381) * Optimize tensor.slice() The performance of executing `tensor.slice()` is super poor, especially for the 'logits' tensor with large dimensions. ``` const logits = outputs.logits.slice(null, -1, null);` ``` This is because currently implementation of the `slice` method manually iterates through each element and calculate indices which is a big time consuming if the tensor shape is large. For cases like `slice(null, -1, null)`, where the slicing operation is contiguous along certain dimensions, which can be optimized by bulk copy by using `TypeArray.subarray()` and `TypeArray.set()`. * nit * Add a few more tensor slice unit tests --------- Co-authored-by: Joshua Lochner <[email protected]> --------- Co-authored-by: Yulong Wang <[email protected]> Co-authored-by: Wanming Lin <[email protected]>
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New model: https://huggingface.co/onnx-community/AFM-4.5B-ONNX