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nm-testing/
Qwen2-VL-7B-Instruct-FP8-dynamic-all

Name: Qwen2-VL-7B-Instruct-FP8-dynamic-all
Availability: InStock

safetensors

qwen2_vl

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https://github.com/vllm-project/llm-compressor/pull/185

python

from transformers import AutoProcessor

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.sparsification import create_sparse_auto_model_class

MODEL_ID = "Qwen/Qwen2-VL-7B-Instruct"

# Load model.
model_class = create_sparse_auto_model_class("Qwen2VLForConditionalGeneration")
model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)

# 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"]
)

# Apply quantization.
oneshot(model=model, recipe=recipe)

# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(processor.decode(output[0]))
print("==========================================")

# Save to disk in compressed-tensors format.
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
model.save_pretrained(SAVE_DIR)
processor.save_pretrained(SAVE_DIR)

nm-testing/Qwen2-VL-7B-Instruct-FP8-dynamic-all

nm-testing/
Qwen2-VL-7B-Instruct-FP8-dynamic-all