初始化项目，由ModelHub XC社区提供模型

Model: Ramikan-BR/Qwen2-0.5B-v5
Source: Original Platform

README.md

@@ -0,0 +1,68 @@
+---
+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 the fifth refinement using accumulated Lora, the AI managed to give me a Python code to train an AI offline. We are making progress with this model “Qwen2-0.5B,” which is so small and yet so powerful that it seems it will far surpass the TinyLlama 1.1B I was training for generating Python code. The detail is that TinyLlama is twice the size of this model, which has only 0.5B!
+
+Setting `pad_token_id` to `eos_token_id`:151643 for open-end generation.
+Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+I need train a AI offline on my computer, give me a code.
+
+### Input:
+
+
+### Output:
+The task is to write a Python program that trains an AI model on a specific dataset. The program should take the dataset as input and use the training data to train the model. The model should then be tested on a separate dataset to evaluate its performance. The program should output the accuracy of the model on the test dataset.
+
+To accomplish this, you will need to follow these steps:
+1. Import the necessary libraries for data manipulation and training.
+2. Load the dataset into memory.
+3. Split the dataset into training and testing sets.
+4. Train the model on the training set.
+5. Evaluate the model's performance on the testing set.
+6. Calculate the accuracy of the model on the testing set.
+
+Here is a sample code that demonstrates these steps:
+```python
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn import svm
+
+# Load the dataset
+data = pd.read_csv('your_dataset.csv')
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)
+
+# Train the model
+model = svm.SVC(kernel='linear', C=1.0)
+model.fit(X_train, y_train)
+
+# Evaluate the model's performance on the testing set
+accuracy = model.score(X_test, y_test)
+print('Accuracy of the model on the testing set:', accuracy)
+´´´ 
+In this code, we first load the dataset from the CSV file. Then, we split the dataset into training and testing sets. We train the model using the `train_test_split` function from scikit-learn, with a 20% split for the training set and 80% split for the testing set. We then evaluate the model's performance on the testing set and print the accuracy.<|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)