| --- |
| base_model: StandardThinking/StandardOne-8B-SH |
| base_model_relation: quantized |
| library_name: transformers |
| license: apache-2.0 |
| pipeline_tag: text-classification |
| tags: |
| - mistral3 |
| - decision-model |
| - schema-head |
| - fp8 |
| - compressed-tensors |
| --- |
| |
| # Standard One 8B SH — FP8 |
|
|
| **Version:** v4 (2026-10-13) |
|
|
| An FP8 (compressed-tensors, float8_e4m3 weights, dynamic per-token activations) quantization of the merged backbone |
| of [Standard One 8B SH](https://huggingface.co/StandardThinking/StandardOne-8B-SH) v4, with the same schema head. |
| The language-model linear projections (q/k/v/o, gate/up/down; 238 tensors) are quantized per output channel |
| (scale = absmax / 448); the vision tower, multi-modal projector, embeddings, norms and lm_head stay in BF16. This is |
| the same data-free recipe and `quantization_config` as |
| [StandardOne-8B-FP8](https://huggingface.co/StandardThinking/StandardOne-8B-FP8). The head (`head/`) is float32 and |
| identical to the BF16 release. |
|
|
| | | | |
| |---|---| |
| | Weights | ~10 GB (BF16 release: 17 GB) | |
| | Head | `head/schema_head.safetensors`, float32, taps: final layer and layer 25 | |
| | Prompt format | chat template with no default system message (see the [main card](https://huggingface.co/StandardThinking/StandardOne-8B-SH#prompt-format)) | |
|
|
| ## Validation |
|
|
| Measured 2026-10-13 on one AMD Instinct MI350X with our SGLang-based decision engine (ROCm build, with the fix in the notes), |
| against the official Decision Index 0.3 run of the BF16 release (StandardOne-8B-SH v4) on the same requests. The sample takes 150 |
| random requests from each of six benchmarks where the model is strong (Home appliance simulator, ContractNLI, HoVer, CLINC150, BANKING77, POP909-CL) and six where it is weak |
| (MuSR, WinoGrande, GSM8K, CRUXEval, ChessBench, MMLU-Pro); ContractNLI has 123 in total. Accuracy is the most probable answer against the benchmark's gold. |
|
|
| | Benchmarks | Questions | BF16 | FP8 (this repository) | Change (points) | Same answer as BF16 | |
| |---|---:|---:|---:|---:|---:| |
| | strong (6 benchmarks) | 4,209 | 93.09 % | 93.04 % | -0.05 | 99.45 % | |
| | weak (6 benchmarks) | 810 | 52.35 % | 52.72 % | +0.37 | 94.32 % | |
| | all | 5,019 | 86.51 % | 86.53 % | +0.02 | 98.63 % | |
|
|
| Answers change almost only where the model is unsure: on the strong benchmarks FP8 gives the same answer as BF16 for 99.5 % of questions, on the weak ones for 94.3 %, and accuracy stays within a fraction of a point in both. Per-benchmark numbers are in `release-manifest.json`. |
|
|
| ## Notes |
|
|
| - Engines that load compressed-tensors FP8 (SGLang, vLLM) read this checkpoint. On ROCm with the native per-token |
| activation-quantization fallback, SGLang 0.5.20 needs a small fix when the engine pads the token dimension |
| (the per-token scale buffer is padded but the fallback writes unpadded rows); without it the server fails at |
| warmup. The validation above used that fix. |
| - The PyTorch reference server in StandardOne-8B-SH is written for the BF16 checkpoint; use the BF16 release with it. |
|
|
| ## License |
|
|
| Apache-2.0, the same terms as [StandardOne-8B-SH](https://huggingface.co/StandardThinking/StandardOne-8B-SH), |
| built on [Standard One 8B](https://huggingface.co/StandardThinking/StandardOne-8B) and |
| [Ministral 3 8B](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512-BF16) (Apache-2.0). |
|
|
