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README.md

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-base_model: Qwen/Qwen2.5-3B-Instruct
-library_name: peft
----
-
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-## Uses
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-### Framework versions
-
-- PEFT 0.14.0
+### README
+
+#### Project Overview
+Yo! You're looking at a sick project where we've finetuned the Qwen 2.5 3B model using LoRA with a dirty language corpus. Yeah, you heard it right, we're taking this language model to a whole new level of sass!
+
+#### What's LoRA?
+LoRA, or Low-Rank Adaptation, is like a magic trick for large language models. Instead of finetuning the entire massive model, which is as expensive as buying a spaceship, LoRA only tweaks a small part of it. It's like fixing a small engine in a big plane.
+
+The core formula of LoRA is:
+
+$\Delta W = BA$
+
+Here, $W$ is the original weight matrix of the model. $\Delta W$ is the low-rank update to $W$. $B$ and $A$ are two low-rank matrices. By training these two small matrices, we can achieve a similar effect as finetuning the whole $W$. It's efficient, it's fast, and it's like a cheat code for model finetuning!
+
+#### Code Explanation
+Let's break down the provided code:
+
+1. **Model and Tokenizer Loading**:
+   ```python
+   import torch
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   from peft import PeftModel
+
+   # Check for GPU availability
+   device = "cuda" if torch.cuda.is_available() else "cpu"
+
+   # Model name
+   model_name = "Qwen/Qwen2.5-3B-Instruct"
+
+   # Load the tokenizer
+   tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+   # Load the base model
+   base_model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
+   # Load the LoRA model
+   lora_model = PeftModel.from_pretrained(base_model, "./qwen25_3b_instruct_lora_vulgarity_finetuned")
+   ```
+   This part loads the Qwen 2.5 3B model and its tokenizer. Then it applies the LoRA adaptation to the base model using the finetuned LoRA weights.
+
+2. **Inference Example**:
+   ```python
+   input_text = "Hello"
+   input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(lora_model.device)
+   output = lora_model.generate(input_ids, max_new_tokens=50, do_sample=True, top_p=0.95, temperature=0.35)
+   output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+   print(output_text)
+   ```
+   This is a simple inference example. It takes an input text, converts it to input IDs, generates an output using the finetuned model, and then decodes the output to text.
+
+3. **Gradio Interface**:
+   ```python
+   import gradio as gr
+
+   def chatbot(input_text, history):
+       # Chatbot logic here
+       ...
+
+   iface = gr.Interface(
+       fn=chatbot,
+       inputs=[gr.Textbox(label="输入你的问题"), gr.State()],
+       outputs=[gr.Chatbot(label="聊天历史"), gr.State()],
+       title="Qwen2.5-finetune-骂人专家",
+       description="Qwen2.5-finetune-骂人专家"
+   )
+
+   iface.launch(share=True, inbrowser=False, debug=True)
+   ```
+   This creates a Gradio interface for the chatbot. Users can input text, and the chatbot will respond based on the finetuned model.
+
+#### How to Run
+1. Make sure you have all the necessary libraries installed. You can install them using `pip`:
+   ```bash
+   pip install torch transformers peft gradio
+   ```
+2. Place your finetuned LoRA weights in the `./qwen25_3b_instruct_lora_vulgarity_finetuned` directory.
+3. Run the Python script. It will start the Gradio server, and you can access the chatbot through the provided link.
+
+#### Warning
+This project uses a dirty language corpus for finetuning. Please use it responsibly and don't let it loose in a polite society!
+
+That's it, folks! You're now ready to unleash the power of this finetuned Qwen 2.5 model. Have fun!