Feature Extraction
Transformers
Safetensors
English
qwen2
quantized
4bit
bnb
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use manu02/VulnLLM-R-7B-bnb-4bit-nf4-dq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manu02/VulnLLM-R-7B-bnb-4bit-nf4-dq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="manu02/VulnLLM-R-7B-bnb-4bit-nf4-dq")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("manu02/VulnLLM-R-7B-bnb-4bit-nf4-dq") model = AutoModel.from_pretrained("manu02/VulnLLM-R-7B-bnb-4bit-nf4-dq") - Notebooks
- Google Colab
- Kaggle
VulnLLM-R-7B (Quantized)
Description
This model is a 4-bit quantized version of the original UCSB-SURFI/VulnLLM-R-7B model, optimized for reduced memory usage while maintaining performance.
Quantization Details
- Quantization Type: 4-bit
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
- bnb_4bit_quant_storage: uint8
- Original Footprint: 15231.23 MB (BFLOAT16)
- Quantized Footprint: 4353.31 MB (UINT8)
- Memory Reduction: 71.4%
Usage
from transformers import AutoModel, AutoTokenizer
model_name = "VulnLLM-R-7B-bnb-4bit-nf4"
model = AutoModel.from_pretrained(
"manu02/VulnLLM-R-7B-bnb-4bit-nf4",
)
tokenizer = AutoTokenizer.from_pretrained("manu02/VulnLLM-R-7B-bnb-4bit-nf4", use_fast=True)
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