القائمة
Load large LLMs with 4-bit NF4 quantization and optional double quantization via BitsAndBytes to reduce GPU memory by 4x while preserving inference quality
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | llm-4bit-nf4-double-quantization |
| description | Load large LLMs with 4-bit NF4 quantization and optional double quantization via BitsAndBytes to reduce GPU memory by 4x while preserving inference quality |
NormalFloat4 (NF4) quantization maps weights to a 4-bit data type optimized for normally-distributed neural network weights. Double quantization further compresses the quantization constants themselves. Together they reduce a 7B model from ~14GB (fp16) to ~4GB, fitting on a single consumer GPU. Quality loss is minimal for inference tasks like scoring, generation, and classification.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1")
BitsAndBytesConfig with NF4 quant type and bfloat16 compute dtypedouble_quant=True for additional memory savings (~0.4GB on 7B)device_map="auto" for automatic GPU placement