license: gemma
language:
- en
pipeline_tag: text-generation
base_model:
- google/gemma-2-9b-it
tags:
- chat
library_name: transformers
Model Overview
- Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
- Release Date: 1/28/2025
Quantized version of google/gemma-2-9b-it to FP8 data type, ready for inference with SGLang >= 0.3 or vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.
Deployment
Use with SGLang
On SGLang, gemma 2's context length has been extended to 8192 thanks to their sliding window attention support.
python -m sglang.launch_server --model-path JamAndTeaStudios/gemma-2-9b-it-FP8-Dynamic \
--port 30000 --host 0.0.0.0
Creation
This model was created with llm-compressor by running the code snippet below.
Model Creation Code
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
MODEL_ID = "google/gemma-2-27b-it"
# 1) Load model.
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# 2) Configure the quantization algorithm and scheme.
# In this case, we:
# * quantize the weights to fp8 with per channel via ptq
# * quantize the activations to fp8 with dynamic per token
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)
# 3) Apply quantization and save in compressed-tensors format.
OUTPUT_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
oneshot(
model=model,
recipe=recipe,
tokenizer=tokenizer,
output_dir=OUTPUT_DIR,
)
# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
print("==========================================")
Evaluation
TBA
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