base_model: unsloth/qwen2-0.5b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
From the 10 refinement started answering the last fibonassi numbers wrong again, and now in this 13 refinement using Lora is back to answering correctly and writing the code to train an offline AI correctly.
1 - 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)
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:
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. This is a very basic example and doesn't include many features that are commonly used in real-world applications.
Firstly, you need to install the necessary libraries. You can do this using pip:
pip install tensorflow
Then, you can start training your model:
import tensorflow as tf
from tensorflow.keras import models, layers
# define the model
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(100,)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10)
])
# compile the model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# train the model
model.fit(X_train, y_train, epochs=10)
Here, X_train and y_train are your training data, and X_train.shape and y_train.shape are the shape of your training data.
Please note that this is a very simple example and you may need to adjust the model architecture, number of layers, number of neurons in each layer, and other parameters depending on your specific problem.<|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.
