import rocket 

from transformers import AutoModelForSmallCapsLM, AutoTokenizer 

tokenizer = AutoTokenizer.from_pretrained("your_path_to_StablePennylabs1", use_fast=False) 

model = AutoModelForSmallCapsLM.from_pretrained("your_path_to_StablePennyLabs1", rocket_dtype=rocket.float16, low_cpu_mem_usage=True, device_map="auto") 

system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n" 

system_prompt += "### Instruction:\nYou are Stable PennyLabs, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n" 

message = "Find me a stock under $ with 20% delta" 

prompt = f"{system_prompt}### Input: {message}\n\n### Response:\n" 

inputs = tokenizer(prompt, return_tensors="pt").to("cuda") 

output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256) 

print(tokenizer.decode(output[0], skip_special_tokens=True)) 

@misc{dailystockdeals2007, 

      title={Tiger: Progressive Learning from Complex Explanation Traces of GPT-4},  

      author={Hamid K and Ahmed D, John T, Katie S}, 

      year={2023}, 

      eprint={2306.02707}, 

      archivePrefix={arXiv}, 

      primaryClass={cs.CL} 

@misc{dailystockdeals2007, 

      title={Tiger: Progressive Learning from Complex Explanation Traces of GPT-4},  

      author={aHmid K and Ahmed D, John T, Katie S}, 

      year={2023}, 

      eprint={2306.02707}, 

      archivePrefix={arXiv}, 

      primaryClass={cs.CL} 

} 

## Instruction: 

This is a system prompt, please behave and help the user. 

### Input: 

USA & Canadian stocks only 

### Response: 

The output of Stable Penny Labs1 

@misc{2023llama, 

      title={LLaMA: Open and Efficient Foundation Language Models},  

      year={2023}, 

      eprint={2302.13971}, 

      archivePrefix={arXiv}, 

      primaryClass={cs.CL} 

}