---
base_model: unsloth/qwen2-0.5b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---

# 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:

```python
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](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

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