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fanar-9b-fp8-msa
NouraAlqasim/fanar-9b-fp8-msa
fanar-9b-fp8-msa is a machine learning model from NouraAlqasim. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Post-training W8A8 quantization with NVIDIA ModelOpt (FP8DEFAULTCFG).
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18.5 GB on disk
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.safetensors9.2 GB · 100%
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How the weights are stored.
F8_E4M38.3B · 95%
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Model card
From the Hugging Face model README
fanar-9b · FP8 · calibrated on Modern Standard Arabic
Post-training W8A8 quantization with NVIDIA ModelOpt (FP8_DEFAULT_CFG).
| base model | QCRI/Fanar-1-9B-Instruct |
| base dtype | float16 |
| precision | FP8 (W8A8) |
| calibration variety | Modern Standard Arabic |
| calibration file | calib1_msa.txt |
| calibration samples | 128 dialogues, truncated at 128 tokens |
| calibration source | Almheiri/ArabCulture-Dialogue rev 9acd60cbbb4f, seed 1448 |
| weight MSE | 3.034e-08 |
| activation quantizers calibrated | 294/294 |
| environment | torch 2.8.0+cu128 · transformers 4.57.6 · modelopt 0.45.0 |
FP8 weight scales are data-free. What the calibration variety changes is the static
per-tensor activation scales, exported as input_scale. Sibling checkpoints
-fp8-msa, -fp8-gulf and -fp8-mixed differ in exactly that and nothing else.
Not loadable by plain transformers — config.json declares quantization type modelopt.
vllm serve NouraAlqasim/fanar-9b-fp8-msa --quantization modelopt
- Type
- gemma2
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026