tmfi-us/Progenitor-V5-LLaMa-70B-FP8-Dynamic
04
basemodel: Tarek07/Progenitor-V5-LLaMa-70B quantizedby: tmfi-us license: llama3.3 ---
About
FP8 w8a8 dynamic quants of Tarek07/Progenitor-V5-LLaMa-70B.
Used following Python script with llmcompressor to generate:
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
MODEL_ID = 'Tarek07/Progenitor-V5-LLaMa-70B'
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# Configure the simple PTQ quantization
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
# Apply the quantization algorithm.
oneshot(model=model, recipe=recipe)
# Save the model.
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)Quantization recipe can be found in `recipe.yaml`
