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Ithanil/Llama-3_3-Nemotron-Super-49B-v1_5-FP8-Dynamic
Llama-3_3-Nemotron-Super-49B-v1_5-FP8-Dynamic is a machine learning model from Ithanil. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
FP8 quantization of https://huggingface.co/nvidia/Llama-33-Nemotron-Super-49B-v15
Downloads · 30 days
13
2% of all-time downloads
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.safetensors52 GB · 100%
How the weights are stored.
F8_E4M347.8B · 96%
From the Hugging Face model README
FP8 quantization of https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5
Created with llmcompressor using the following code:
import sys
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
MODEL_ID = sys.argv[1]
SAVE_DIR = sys.argv[2]
# Load the model
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto", local_files_only=True, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, local_files_only=True, trust_remote_code=True)
# Configure the simple PTQ quantization
recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
# Apply the quantization algorithm.
oneshot(model=model, recipe=recipe, trust_remote_code_model=True)
# Save the model
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)