1 - # alpaca_prompt = Copied from above FastLanguageModel.for_inference(model) # Enable native 2x faster inference inputs = tokenizer( [ alpaca_prompt.format(

"Continue the fibonnaci sequence.", # instruction

"1, 1, 2, 3, 5, 8", # input

    "", # output - leave this blank for generation!
)

], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True) tokenizer.batch_decode(outputs)

['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### Input:\nContinue the fibonnaci sequence.\n\n### Output:\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196728, 318101']

2 - # alpaca_prompt = Copied from above FastLanguageModel.for_inference(model) # Enable native 2x faster inference inputs = tokenizer( [ alpaca_prompt.format(

"What is fibonacci sequence?", # instruction

    "", # input
    "", # output - leave this blank for generation!
)

], return_tensors = "pt").to("cuda")

from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 256)

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

Input:

What is fibonacci sequence?

Output:

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence goes like this: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196728, 328101, 544829, 973530, 1518361, 2492891, 4011452, 6504307, 9518768, 15023075

3 - if False: from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference

alpaca_prompt = You MUST copy from above!

inputs = tokenizer( [ alpaca_prompt.format(

"Crie uma IA. Ela será treinada para conversar por chat e escrever códigos em python conforme solicitada, após ser treinada para essas tarefas.", # instruction

    "", # input
    "", # output - leave this blank for generation!
)

], return_tensors = "pt").to("cuda")

from transformers import TextStreamer text_streamer = TextStreamer(tokenizer) _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 4096)

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

Input:

Crie uma IA. Ela será treinada para conversar por chat e escrever codigos em python conforme solicitada, após ser treinada para essas tarefas.

Output:

Here is a simple Python program that uses the OpenAI's ChatGPT API to simulate a chatbot:

import openai
from openai import ChatGPT

# Initialize the ChatGPT API
openai.api_key = "YOUR_API_KEY"

# Create a ChatGPT model
model = ChatGPT(model_name="gpt-3.5-turbo")

# Create a prompt
prompt = "Write a python program that takes a number as input and prints out the square of that number."

# Send the prompt to the ChatGPT model
response = model.create(input=prompt)

# Print the response
print(response)

This program will output a Python program that takes a number as input and prints out the square of that number.<|endoftext|>

Uploaded model

Developed by: Ramikan-BR
License: apache-2.0
Finetuned from model : unsloth/qwen2-0.5b-bnb-4bit

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.