RichardErkhov/

aloobun_-_Reyna-Mini-1.8B-v0.1-gguf

• Model creator: https://huggingface.co/aloobun/
• Original model: https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.1/

Original model description:

• teknium/OpenHermes-2.5 tags:
• chatml
• finetune
• gpt4
• synthetic data
• custom_code
• qwen2

• Finetuned Qwen/Qwen1.5-1.8B-Chat, with SFT on teknium's OpenHermes-2.5 dataset.
• This marks the inception of my Qwen1.5 LLM series, with this model laying the foundation for what lies ahead.
• Format: ChatML

  • <|im_start|>system
    {system}<|im_end|>
    <|im_start|>user
    {prompt}<|im_end|>
    <|im_start|>assistant

• Next step would be to do a DPO train on top.

Benchamrks:

Example:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
import torch

class MyStoppingCriteria(StoppingCriteria):
  def __init__(self, target_sequence, prompt):
    self.target_sequence = target_sequence
    self.prompt=prompt

  def __call__(self, input_ids, scores, **kwargs):
    generated_text = tokenizer.decode(input_ids[0])
    generated_text = generated_text.replace(self.prompt,'')
    if self.target_sequence in generated_text:
        return True 
    return False 

  def __len__(self):
    return 1

  def __iter__(self):
    yield self

modelpath="aloobun/Reyna-Mini-1.8B-v0.1"

model = AutoModelForCausalLM.from_pretrained(
    modelpath,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    trust_remote_code=True,       
)

tokenizer = AutoTokenizer.from_pretrained(
    modelpath,
    trust_remote_code=True,      
    use_fast=False,
)

prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nReflect on a real-world scenario where understanding probability theory could make a significant difference in decision-making.\n<|im_start|>assistant\n"

encoded_input = tokenizer(prompt, return_tensors='pt')
input_ids=encoded_input['input_ids'].cuda()
streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
op = model.generate(
    input_ids,
    streamer=streamer,
    pad_token_id=tokenizer.eos_token_id,
    do_sample=True,
    temperature=0.6,
    top_p=0.8,
    max_new_tokens=512,
    stopping_criteria=MyStoppingCriteria("<|im_end|>", prompt)
)

Output:

One real-world scenario where understanding probability theory can make a significant difference in decision-making is in the field of finance. Financial institutions, such as banks and investment firms, must make decisions about lending money to individuals or businesses, and how much risk they should take on. In this case, understanding probability theory would help financial analysts and investors make more informed decisions by providing them with information about the likelihood of different outcomes. For example, if an investor wants to invest in a particular stock, they might want to understand the probability that it will perform well over time, based on historical data and market trends. They might also be interested in understanding the probability of defaulting on a loan, which would help them evaluate whether it's worth taking on that risk. Probability theory provides valuable insights into how events are likely to occur and what factors contribute to those probabilities. By using statistical models and simulations, financial professionals can estimate the likelihood of different scenarios and make better-informed decisions about how to allocate their resources. This can lead to increased profits for financial institutions and improved customer satisfaction for individual investors.<|im_end|>