Upload v0.4.1 layered Whisper encoder artifacts

Multi-ELF encoder variant, published separately from the monolithic encoder model for comparison.

.gitattributes

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README.md

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+---
+library_name: llima
+license: apache-2.0
+pipeline_tag: automatic-speech-recognition
+base_model: openai/whisper-small
+base_model_relation: quantized
+language:
+- en
+- zh
+- de
+- es
+- ru
+- ko
+- fr
+- ja
+- pt
+- tr
+- pl
+- ca
+- nl
+- ar
+- sv
+- it
+- id
+- hi
+- fi
+- vi
+- he
+- uk
+- el
+- ms
+- cs
+- ro
+- da
+- hu
+- ta
+- "no"
+- th
+- ur
+- hr
+- bg
+- lt
+- la
+- mi
+- ml
+- cy
+- sk
+- te
+- fa
+- lv
+- bn
+- sr
+- az
+- sl
+- kn
+- et
+- mk
+- br
+- eu
+- is
+- hy
+- ne
+- mn
+- bs
+- kk
+- sq
+- sw
+- gl
+- mr
+- pa
+- si
+- km
+- sn
+- yo
+- so
+- af
+- oc
+- ka
+- be
+- tg
+- sd
+- gu
+- am
+- yi
+- lo
+- uz
+- fo
+- ht
+- ps
+- tk
+- nn
+- mt
+- sa
+- lb
+- my
+- bo
+- tl
+- mg
+- as
+- tt
+- haw
+- ln
+- ha
+- ba
+- jw
+- su
+tags:
+- whisper
+- audio
+- automatic-speech-recognition
+- speech-translation
+- embedded
+- sima
+- modalix
+- a16w8
+---
+
+# Whisper Small A16W8: Optimized for SiMa.ai Modalix
+
+## Overview
+
+This repository contains a precompiled and quantized version of
+[OpenAI Whisper Small](https://huggingface.co/openai/whisper-small), optimized
+for execution on the **SiMa.ai Modalix** platform.
+
+| Property | Value |
+|---|---|
+| Model architecture | Whisper encoder-decoder Transformer |
+| Parameters | 244 million |
+| Quantization | A16W8: 16-bit activations, 8-bit weights |
+| Target | SiMa.ai Modalix |
+| Tasks | Multilingual transcription and speech translation to English |
+| Languages | 99 |
+| Maximum audio window | 30 seconds |
+| Maximum decoder positions | 448 |
+| Compatible Neat release | 0.4.0 |
+| Source model | [openai/whisper-small](https://huggingface.co/openai/whisper-small) |
+
+> This repository is not a standard Transformers checkpoint. The `elf_files/`
+> and `devkit/` directories contain compiled artifacts consumed by the SiMa.ai
+> Neat runtime on Modalix.
+
+## Prerequisites
+
+To use this model, you need:
+
+1. A **SiMa.ai Modalix device**.
+2. The **SiMa.ai Neat Runtime** installed or updated on Modalix.
+3. The **Hugging Face CLI**, optionally, for downloading the model on a host
+   before copying it to Modalix.
+
+Follow the [SiMa.ai Neat getting started guide](https://developer.sima.ai/software/getting-started/)
+to install or update the Neat Runtime.
+
+## Installation and Deployment
+
+### Download the Model
+
+On Modalix, download the precompiled model with the LLiMa model manager:
+
+```bash
+llima pull whisper-small-a16w8
+```
+
+The model is installed under:
+
+```text
+/media/nvme/llima/models/whisper-small-a16w8
+```
+
+Alternatively, download it on a host and copy it to Modalix:
+
+```bash
+hf download simaai/whisper-small-a16w8 \
+  --local-dir whisper-small-a16w8
+
+scp -r whisper-small-a16w8 \
+  sima@<modalix-ip>:/media/nvme/llima/models/
+```
+
+Replace `<modalix-ip>` with the IP address or hostname of the Modalix device.
+
+## Usage
+
+This model is intended for applications built with the SiMa.ai Neat GenAI
+APIs. Use `ASRModel` for direct, in-process transcription or `GenAIServer` when
+clients access the model over HTTP.
+
+### Direct Application Integration
+
+The Neat `ASRModel` API provides the lowest-overhead path for speech
+transcription inside an application:
+
+```cpp
+#include "neat/genai.h"
+
+#include <iostream>
+
+int main() {
+    simaai::neat::genai::ASRModel model(
+        "/media/nvme/llima/models/whisper-small-a16w8");
+
+    simaai::neat::genai::GenerationRequest request;
+    request.audio_file = "speech.wav";
+    request.language = "en";
+
+    auto result = model.run(request);
+    std::cout << result.text << "\n";
+}
+```
+
+For the complete C++ and Python workflows, including audio tensors and
+streaming, see
+[GenAI Model](https://developer.sima.ai/software/develop-apps/development-workflow/genai-model).
+
+### Serve with the Neat GenAI Server
+
+Use `GenAIServer` when a browser, service, or remote client needs to access the
+model through an HTTP API. The packaged Neat tutorial can serve this ASR model
+by itself:
+
+```bash
+./lib/sima-neat/tutorials/tutorial_021_serve_genai_models \
+  --asr /media/nvme/llima/models/whisper-small-a16w8
+```
+
+Verify that the model was registered:
+
+```bash
+curl http://<modalix-ip>:9998/v1/models
+```
+
+Send an audio transcription request with the tutorial client:
+
+```bash
+python3 share/sima-neat/tutorials/021_serve_genai_models/request_audio_transcription.py \
+  --server-ip <modalix-ip> \
+  --model asr \
+  speech.wav
+```
+
+See
+[Serve GenAI Models](https://developer.sima.ai/software/tutorials/serve-genai-models)
+for the complete C++, Python, and HTTP workflow.
+
+## Artifact Layout
+
+```text
+devkit/                         Runtime configuration, tokenizer, and embeddings
+elf_files/                      Compiled Modalix accelerator programs
+compile.sh                      Compilation entry point
+gen_models--openai--whisper.py  Whisper compilation utility
+```
+
+The compilation scripts are included for provenance and reproducibility. They
+are not required when using the precompiled artifacts.
+
+## Evaluation
+
+No accuracy or performance results specific to this A16W8 compiled artifact
+are currently reported. Results on the
+[source model card](https://huggingface.co/openai/whisper-small) describe the
+unquantized upstream checkpoint and should not be treated as measurements of
+this build.
+
+## Limitations
+
+- These artifacts run on SiMa.ai Modalix and cannot be loaded with
+  `transformers.AutoModel`.
+- The runtime processes an audio window of up to 30 seconds. Segment longer
+  recordings before inference.
+- This build produces text without word- or segment-level timestamps.
+- Speaker diarization is not provided.
+- Quantization can cause minor differences from the full-precision source
+  model.
+- Whisper can hallucinate text during silence or noisy audio and performs
+  unevenly across languages, accents, and domains. Evaluate the model on
+  representative data before production deployment.
+
+## Resources
+
+- [SiMa.ai Neat](https://developer.sima.ai/software/getting-started/)
+- [GenAI Model API](https://developer.sima.ai/software/develop-apps/development-workflow/genai-model)
+- [Serve GenAI Models](https://developer.sima.ai/software/tutorials/serve-genai-models)
+- [GenAI with LLiMa](https://developer.sima.ai/software/genai-llima/)
+- [OpenAI Whisper Small](https://huggingface.co/openai/whisper-small)
+- [Whisper paper](https://arxiv.org/abs/2212.04356)
+
+## Citation
+
+```bibtex
+@misc{radford2022whisper,
+  doi       = {10.48550/ARXIV.2212.04356},
+  url       = {https://arxiv.org/abs/2212.04356},
+  author    = {Radford, Alec and Kim, Jong Wook and Xu, Tao and
+               Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  title     = {Robust Speech Recognition via Large-Scale Weak Supervision},
+  publisher = {arXiv},
+  year      = {2022}
+}
+```

compile.sh

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+#!/usr/bin/env bash
+
+set -euo pipefail
+
+# Download the source model using:
+#   hf download openai/whisper-small \
+#       --local-dir models--openai--whisper-small
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+MODEL_PATH="models--openai--whisper-small"
+OUTPUT_PATH="whisper-small-a16w8"
+NUM_PROCESSES="${NUM_PROCESSES:-1}"
+
+TMPDIR="$(mktemp -d /tmp/sima.XXXXXX)"
+export TMPDIR
+
+cleanup() {
+    rm -rf -- "${TMPDIR}"
+}
+trap cleanup EXIT
+
+python3 "${SCRIPT_DIR}/gen_models--openai--whisper.py" \
+    --model_path "${MODEL_PATH}" \
+    -o "${OUTPUT_PATH}" \
+    --num_processes "${NUM_PROCESSES}" \
+    --part all \
+    --enable_log_probe \
+    --resume \
+    "$@"

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+    "log_probe_enabled": true,
+    "model_name": "models--openai--whisper-small",
+    "decoder_use_future_token_mask": true
+}

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