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Model: Ramikan-BR/Qwen2-0.5B-v9 Source: Original Platform
2026-04-13 06:32:04 +08:00
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+---
+base_model: unsloth/qwen2-0.5b-bnb-4bit
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+- sft
+---
+## For the first time after 9 refinements with LORA accumulations, the model answered the Fibonassi sequence without errors on both questions and wrote code to train an offline AI. Really this little model Qwen2-0.5B is very good! And the way it responds to the code and gives information is perfect, impressive!
+
+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
+
+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 very 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 very good for this case.
+
+### Output:
+Sure, I'd be happy to help you with that! Here's a simple example of how you could use TensorFlow, a popular deep learning library for Python, to train an AI model on your computer. 
+
+Firstly, you'll need to install TensorFlow on your computer. You can do this using pip:
+
+```
+pip install tensorflow
+```
+
+Once you have installed TensorFlow, you can start training your model. Here's a simple example:
+
+```python
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+# define the model
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
+    tf.keras.layers.Dense(64, activation='relu'),
+    tf.keras.layers.Dense(10, activation='softmax')
+])
+
+# compile the model
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# train the model
+model.fit(x_train, y_train, epochs=10)
+```
+
+In this example, we are using the Sequential model, which is a simple way to build a model. The model consists of two layers: one dense layer with 64 units and ReLU activation, and another dense layer with 64 units and also ReLU activation. The output layer has 10 units and softmax activation. 
+
+The model is then compiled with the Adam optimizer and categorical cross-entropy loss function. The model is then trained for 10 epochs.
+
+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 problem. 
+
+Also, remember that you need to install the necessary libraries first, which are TensorFlow and Keras. You can do this using pip:
+
+```
+pip install tensorflow
+pip install keras
+```
+
+Once you have installed these libraries, you can run your code. 
+
+I hope this helps!<|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)