Nexta-39-23 / README.md
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from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
load_in_4bit=True,
device_map="auto"
)
# Load NEXTa adapters
model = PeftModel.from_pretrained(base_model, "NEXTa-SA/Nexta-39-23")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
# Example prompt structure
prompt = """
Task: Create social media post
Language: English
Brand: [Brand name]
Audience: [Target audience]
Objective: [Campaign objective]
Tone: [Desired tone]
Additional context: [Any specific requirements]
Generate a social media post that:
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=300,
temperature=0.7,
top_p=0.9,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)