Text Classification
TensorRT
ONNX
OpenVINO
English
Russian
feature-extraction
vocabulary-filtering
denoising
multilingual
quantized
int8
Instructions to use faxenoff/code-daemon-denoise-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use faxenoff/code-daemon-denoise-v1 with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
compiled artefacts from win_x64 (sm_120)
Browse files- README.md +10 -3
- code-daemon-denoise-v1-s_ov2026.2_cpu_int8_b64_s40.bin β code-daemon-denoise-v1-s_ov2026.3_cpu_int8_b64_s40.bin +0 -0
- code-daemon-denoise-v1-s_ov2026.2_cpu_int8_b64_s40.xml β code-daemon-denoise-v1-s_ov2026.3_cpu_int8_b64_s40.xml +0 -0
- code-daemon-denoise-v1-s_ov2026.2_igpu_lnl_int8_b64_s40.bin β code-daemon-denoise-v1-s_ov2026.3_igpu_lnl_int8_b64_s40.bin +0 -0
- code-daemon-denoise-v1-s_ov2026.2_igpu_lnl_int8_b64_s40.xml β code-daemon-denoise-v1-s_ov2026.3_igpu_lnl_int8_b64_s40.xml +0 -0
- code-daemon-denoise-v1-s_ov2026.2_npu_int4_b16_s40.bin β code-daemon-denoise-v1-s_ov2026.3_npu_int4_b16_s40.bin +0 -0
- code-daemon-denoise-v1-s_ov2026.2_npu_int4_b16_s40.xml β code-daemon-denoise-v1-s_ov2026.3_npu_int4_b16_s40.xml +0 -0
- manifest.json +2 -2
README.md
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@@ -113,9 +113,16 @@ Measured on one laptop: Intel Core Ultra 9 275HX / NVIDIA RTX 5060 Laptop, batch
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| lane | per batch | throughput | per word |
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| **TensorRT FP16, RTX 5060 Laptop** | **3.60 ms** | **17 790 words/s** | 0.056 ms |
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| **OpenVINO INT8,
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| ONNX Runtime FP32, CPU | 188 ms | 341 words/s | 2.93 ms |
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The INT8 CPU lane is the intended default β 800 words/sec is enough to filter a repository's whole
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harvested vocabulary in seconds without touching a GPU, and it is 2.3Γ the unquantized ONNX path.
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The GPU lane exists for hosts that have spare VRAM anyway.
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@@ -170,9 +177,9 @@ drops really was ballast.
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## 7. What is in this repo
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- **OpenVINO INT8** β `code-daemon-denoise-v1-s_ov2026.
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default lane (CPU) and an Intel iGPU build.
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- **OpenVINO INT4, NPU** β `code-daemon-denoise-v1-s_ov2026.
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INT4 at batch 16 for Intel NPUs.
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- **TensorRT FP16** β `code-daemon-denoise-v1-s_{win_x64,linux_x64}_trt11.0_sm_120.engine`.
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- **TVM Vulkan** β `code-daemon-denoise-v1_{win_x64,linux_x64}_tvm0.25_vulkan.{dll,so}` β GPU fallback
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| lane | per batch | throughput | per word |
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|---|--:|--:|--:|
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| **TensorRT FP16, RTX 5060 Laptop** | **3.60 ms** | **17 790 words/s** | 0.056 ms |
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| **OpenVINO INT8, iGPU** (OV 2026.3) | **56.1 ms** | **1 140 words/s** | 0.88 ms |
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| **OpenVINO INT8, CPU** (OV 2026.3) | **75-83 ms** | **770-850 words/s** | 1.18-1.30 ms |
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| OpenVINO INT4, NPU (OV 2026.3) | 57.8 ms | 277 words/s | 3.61 ms |
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| ONNX Runtime FP32, CPU | 188 ms | 341 words/s | 2.93 ms |
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The NPU number is per batch 16, not 64 β it is a low-batch part, so its per-word cost is the
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highest of the three even though its per-batch latency looks similar to the CPU's. Running all
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three Intel devices at once yields ~87 % of the sum of their solo rates (they share one memory
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controller), so a host with no discrete GPU can still denoise ~2 000 words/s.
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The INT8 CPU lane is the intended default β 800 words/sec is enough to filter a repository's whole
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harvested vocabulary in seconds without touching a GPU, and it is 2.3Γ the unquantized ONNX path.
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The GPU lane exists for hosts that have spare VRAM anyway.
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## 7. What is in this repo
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- **OpenVINO INT8** β `code-daemon-denoise-v1-s_ov2026.3_{cpu,igpu_lnl}_int8_b64_s40.{xml,bin}` β the
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default lane (CPU) and an Intel iGPU build.
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- **OpenVINO INT4, NPU** β `code-daemon-denoise-v1-s_ov2026.3_npu_int4_b16_s40.{xml,bin}` β weight-only
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INT4 at batch 16 for Intel NPUs.
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- **TensorRT FP16** β `code-daemon-denoise-v1-s_{win_x64,linux_x64}_trt11.0_sm_120.engine`.
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- **TVM Vulkan** β `code-daemon-denoise-v1_{win_x64,linux_x64}_tvm0.25_vulkan.{dll,so}` β GPU fallback
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code-daemon-denoise-v1-s_ov2026.2_cpu_int8_b64_s40.bin β code-daemon-denoise-v1-s_ov2026.3_cpu_int8_b64_s40.bin
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code-daemon-denoise-v1-s_ov2026.2_cpu_int8_b64_s40.xml β code-daemon-denoise-v1-s_ov2026.3_cpu_int8_b64_s40.xml
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code-daemon-denoise-v1-s_ov2026.2_igpu_lnl_int8_b64_s40.xml β code-daemon-denoise-v1-s_ov2026.3_igpu_lnl_int8_b64_s40.xml
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code-daemon-denoise-v1-s_ov2026.2_npu_int4_b16_s40.bin β code-daemon-denoise-v1-s_ov2026.3_npu_int4_b16_s40.bin
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code-daemon-denoise-v1-s_ov2026.2_npu_int4_b16_s40.xml β code-daemon-denoise-v1-s_ov2026.3_npu_int4_b16_s40.xml
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manifest.json
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@@ -10,10 +10,10 @@
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],
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"runtime_tags": {
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"trt": "trt11.0",
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"ov": "ov2026.
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"tvm": "tvm0.25",
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"mlx_dir": "model_gpu_mlx0.22"
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},
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"compiled_at": "2026-08-
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"compiled_by": "models/_compile"
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}
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],
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"runtime_tags": {
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"trt": "trt11.0",
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"ov": "ov2026.3",
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"tvm": "tvm0.25",
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"mlx_dir": "model_gpu_mlx0.22"
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},
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"compiled_at": "2026-08-05T08:54:59Z",
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"compiled_by": "models/_compile"
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}
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