Qwen2-0.5B-v15

1 - Question :

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)

1 - Answer :

['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, 196418, 317811']

2 - Question :

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 = 128)

2 - Answer :

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, 1

3 - Question :

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( "I need train a AI offline on my computer, give me a code good for this case.", # 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)

3 - Answer :

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:

I need train a AI offline on my computer, give me a code good for this case.

Output:

Sure, here's a simple example of how you can train an AI model on a computer using Python and TensorFlow:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Define the model
model = tf.keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(100,)),
    layers.Dense(64, activation='relu'),
    layers.Dense(1)
])

# Compile the model
model.compile(optimizer='adam',
              loss='mean_squared_error',
              metrics=['mean_absolute_error'])

# Train the model
model.fit(X_train, y_train, epochs=100, batch_size=32)

# Evaluate the model
model.evaluate(X_test, y_test)

In this example, we are using the Keras library to create a sequential model. The model consists of two dense layers with ReLU activation. The first layer has 64 units and the second layer has 64 units. The output layer has 1 unit. The mean squared error is used as the loss function, and the mean absolute error is used as the metric for evaluation. The adam optimizer is used for training, and the mean_squared_error metric is used for evaluation.

Please note that this is a very simple example and you may need to adjust the model architecture, number of layers, number of units, and other parameters depending on your specific use case.<|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.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>