Quantization
Collection
A collection of quantized models. All the models can be fine-tuned by adding a LoRA Adapter. • 82 items • Updated • 3
How to use shuyuej/SFR-Embedding-2_R-GPTQ with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="shuyuej/SFR-Embedding-2_R-GPTQ") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("shuyuej/SFR-Embedding-2_R-GPTQ")
model = AutoModel.from_pretrained("shuyuej/SFR-Embedding-2_R-GPTQ")Original Base Embedding Model: Salesforce/SFR-Embedding-2_R.
Link: https://huggingface.co/Salesforce/SFR-Embedding-2_R
"quantization_config": {
"bits": 4,
"checkpoint_format": "gptq",
"damp_percent": 0.01,
"desc_act": true,
"group_size": 128,
"model_file_base_name": null,
"model_name_or_path": null,
"quant_method": "gptq",
"static_groups": false,
"sym": true,
"true_sequential": true
},
Source Codes: https://github.com/vkola-lab/medpodgpt/tree/main/quantization.