---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- 'quantization '
- LLM
- Dolly
---
Import this model using:

<pre>
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "AhmedBou/databricks-dolly-v2-3b_on_NCSS"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
</pre>

Inference using:

<pre>
batch = tokenizer("“Multiple Regression for Appraisal” -->: ", return_tensors='pt')

with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=50)

print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
</pre>

Output:

<pre>
“Multiple Regression for Appraisal” -->: Multiple Regression for Appraisal (MRA) -->: Multiple Regression for Appraisal (MRA) (with Covariates) -->: Multiple Regression for Appraisal (MRA) (with Covariates
</pre>