Gemma-2-9B-It-SPPO-Iter3_Q8

Gemma-2-9B-It-SPPO-Iter3-Q8

Model Overview

Gemma-2-9B-It-SPPO-Iter3 quantized to FP8 weights using dynamic activation scheme, ready for inference with vLLM >= 0.5.0.

Usage and Creation

Produced using AutoFP8.

from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig

pretrained_model_dir = "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3"
quantized_model_dir = "/quantized/Gemma-2-9B-It-SPPO-Iter3_Q8"

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir)

quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="dynamic")

model = AutoFP8ForCausalLM.from_pretrained(
    pretrained_model_dir, quantize_config=quantize_config
)

model.save_quantized(quantized_model_dir)

How to run FP8 quantized models

vLLM has full support for FP8 models quantized with this package. Install vLLM with: pip install vllm>=0.5.0

Then simply pass the quantized checkpoint directly to vLLM's entrypoints! It will detect the checkpoint format using the quantization_config in the config.json.

from vllm import LLM
model = LLM("tranhoangnguyen03/Gemma-2-9B-It-SPPO-Iter3_Q8")

outputs = model.generate("Once upon a time,")
print(outputs[0].outputs[0].text)

Benchmark Results

||| TBA |||