How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SLORA/PRO", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SLORA/PRO", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SLORA/PRO", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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⚡ SLORA/PRO

Architected & Maintained by Muhammad Taqi


👨‍💻 Creator Profile

  • Architect: Muhammad Taqi
  • Model ID: SLORA/PRO
  • Architecture Base: NOTHING (THIS IS BASE FREE ALL CODE IS WRITTEN BY MUHAMMAD TAQI)
  • Key Feature: Super-fast execution & instant response generation

Developed with ❤️ by Muhammad Taqi Syntax of use model.
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