Sohailhosseini/Qwopus3.5-9B-v3-FP8

Qwopus3.5-9B-v3 - FP8

Jackrong/Qwopus3.5-9B-v3 quantized to FP8 (8-bit weights).

What this is

Near-lossless, no calibration data, and it halves every Linear weight. The safe default when you care about quality and have Ada/Hopper or newer.

Caveat. Needs compute capability >= 8.9 (Ada/Hopper+) to run fast.

Details

Source	Jackrong/Qwopus3.5-9B-v3
Scheme	FP8 (8-bit)
Format	compressed-tensors
Parameters	9.7B
Size on disk	11.9 GB
Compression	1.62x smaller than the 19.3 GB source
Left unquantized	lm_head, re:.*visual.*, re:.*vision_tower.*, re:.*vision_model.*, re:.*vision.*, re:.*multi_modal_projector.*, re:.*merger.*
Quantized on	L40S
Quantized by	Sohailhosseini

Usage

vllm serve Sohailhosseini/Qwopus3.5-9B-v3-FP8 \
  --max-model-len 32768

from vllm import LLM, SamplingParams

if __name__ == "__main__":
    llm = LLM("Sohailhosseini/Qwopus3.5-9B-v3-FP8", max_model_len=32768)
    out = llm.chat(
        [{"role": "user", "content": "What is quantization? Answer in one sentence."}],
        SamplingParams(temperature=0.6, max_tokens=512),
    )
    print(out[0].outputs[0].text)

Provenance

Produced with HF-quantized. recipe.yaml in this repo is the exact modifier stack that was applied, and the scheme, ignored layers and hardware are in the table above.

Licence is inherited from the source model. Quantization does not change what you are permitted to do with the weights.