Update README.md

README.md

@@ -1,22 +1,184 @@
 ---
-base_model: unsloth/Qwen2.5-7B-bnb-4bit
+base_model: unsloth/codellama-34b-bnb-4bit
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
-- gguf
+- trl
 license: apache-2.0
 language:
 - en
 ---
 
-# Uploaded  model
+# Efficient Fine-Tuning of Large Language Models - Minecraft AI Assistant Tutorial  
 
-- **Developed by:** chriscelaya
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-7B-bnb-4bit
+This repository demonstrates how to fine-tune the **Codellama-34b** model to create "Andy," an AI assistant for Minecraft. Using the **Unsloth framework**, this tutorial showcases efficient fine-tuning with 4-bit quantization and LoRA for scalable training on limited hardware.
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## 🚀 Resources  
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+- **Source Code**: [GitHub Repository](https://github.com/while-basic/mindcraft)
+- **Colab Notebook**: [Colab Notebook](https://colab.research.google.com/drive/1Eq5dOjc6sePEt7ltt8zV_oBRqstednUT?usp=sharing)
+- **Blog Article**: [Walkthrough](https://chris-celaya-blog.vercel.app/articles/unsloth-training)
+- **Dataset**: [Andy-3.5](https://huggingface.co/datasets/Sweaterdog/Andy-3.5)
+- **Teaser**: [Video](https://www.youtube.com/watch?v=KUXY5OtaPZc)
+  
+## Overview  
+
+This **readme.md** provides step-by-step instructions to:  
+1. Install and set up the **Unsloth framework**.  
+2. Initialize the **Qwen 7B** model with **4-bit quantization**.  
+3. Implement **LoRA Adapters** for memory-efficient fine-tuning.  
+4. Prepare the **Andy-3.5 dataset** with Minecraft-specific knowledge.  
+5. Configure and execute training in a resource-efficient manner.  
+6. Evaluate and deploy the fine-tuned AI assistant.
+
+---
+
+### Key Features  
+
+- **Memory-Efficient Training**: Fine-tune large models on GPUs as low as T4 (Google Colab).  
+- **LoRA Integration**: Modify only key model layers for efficient domain-specific adaptation.  
+- **Minecraft-Optimized Dataset**: Format data using **ChatML templates** for seamless integration.  
+- **Accessible Hardware**: Utilize cost-effective setups with GPU quantization techniques.
+
+---
+
+## Prerequisites  
+
+- **Python Knowledge**: Familiarity with basic programming concepts.  
+- **GPU Access**: T4 (Colab Free Tier) is sufficient; higher-tier GPUs like V100/A100 recommended.  
+- **Optional**: [Hugging Face Account](https://huggingface.co/) for model sharing.
+
+---
+
+## Setup  
+
+Install the required packages:  
+```bash
+!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+!pip install --no-deps xformers trl peft accelerate bitsandbytes
+```
+
+---
+
+## Model Initialization  
+
+Load the **Codellama-34b** model with 4-bit quantization for reduced resource usage:  
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/codellama-34b-bnb-4bit",
+    max_seq_length=2048,
+    dtype=torch.bfloat16,
+    load_in_4bit=True,
+    trust_remote_code=True,
+)
+```
+
+---
+
+## Adding LoRA Adapters  
+
+Add LoRA to fine-tune specific layers efficiently:  
+```python
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=16,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "embed_tokens", "lm_head"],
+    lora_alpha=16,
+    lora_dropout=0,
+    use_gradient_checkpointing="unsloth",
+)
+```
+
+---
+
+## Dataset Preparation  
+
+Prepare the Minecraft dataset (**Andy-3.5**):  
+```python
+from datasets import load_dataset
+from unsloth.chat_templates import get_chat_template
+
+dataset = load_dataset("Sweaterdog/Andy-3.5", split="train")
+tokenizer = get_chat_template(tokenizer, chat_template="chatml")
+```
+
+---
+
+## Training Configuration  
+
+Set up the training parameters:  
+```python
+from trl import SFTTrainer
+from transformers import TrainingArguments
+
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    args=TrainingArguments(
+        per_device_train_batch_size=16,
+        max_steps=1000,
+        learning_rate=2e-5,
+        gradient_checkpointing=True,
+        output_dir="outputs",
+        fp16=True,
+    ),
+)
+```
+
+Clear unused memory before training:  
+```python
+import torch
+torch.cuda.empty_cache()
+```
+
+---
+
+## Train the Model  
+
+Initiate training:  
+```python
+trainer_stats = trainer.train()
+```
+
+---
+
+## Save and Share  
+
+Save your fine-tuned model locally or upload to Hugging Face:  
+```python
+model.save_pretrained("andy_minecraft_assistant")
+```
+
+---
+
+## Optimization Tips  
+
+- Expand the dataset for broader Minecraft scenarios.  
+- Adjust training steps for better accuracy.  
+- Fine-tune inference parameters for more natural responses.
+
+---
+
+For more details on **Unsloth** or to contribute, visit [Unsloth GitHub](https://github.com/unslothai/unsloth).  
+
+Happy fine-tuning! 🎮
+
+## Citation
+
+@misc{celaya2025minecraft,
+  author = {Christopher B. Celaya},
+  title = {Efficient Fine-Tuning of Large Language Models - A Minecraft AI Assistant Tutorial},
+  year = {2025},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/kolbytn/mindcraft}},
+  note = {\url{https://chris-celaya-blog.vercel.app/articles/unsloth-training}}
+}