Update README.md

README.md

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+# 🧠 LoL_Build-Llama3B
+
+A fine-tuned version of the LLaMA 3.2B model using QLoRA on a custom League of Legends build suggestion dataset. This model generates champion-specific item build recommendations based on gameplay roles and current meta.
+
 ---
-base_model: unsloth/llama-3.2-3b-bnb-4bit
-library_name: transformers
-model_name: LoL_Build-Llama3B
-tags:
-- generated_from_trainer
-- unsloth
-- trl
-- sft
-licence: license
+
+## 📚 Dataset
+
+- **Source**: Custom JSONL dataset with `prompt` and `completion` fields.
+- **Train/Val Split**: 2 files – `train.jsonl` and `val.jsonl`
+- **Schema Example**:
+  ```json
+  {
+    "prompt": "Suggest a build for Ahri mid lane.",
+    "completion": "Luden's Tempest, Sorcerer's Shoes, Shadowflame..."
+  }
+  ```
+
+---
+
+## 🏋️‍♂️ Training Configuration
+
+| Hyperparameter              | Value        |
+|----------------------------|--------------|
+| Base Model                 | unsloth/Llama-3.2-3B-bnb-4bit |
+| Batch Size                 | 16           |
+| Gradient Accumulation      | 1            |
+| Epochs                     | 1            |
+| Max Steps                  | 10000        |
+| Learning Rate              | 2e-4         |
+| Weight Decay               | 0.01         |
+| Max Sequence Length        | 512          |
+| Precision                  | BF16 (fallback to FP16) |
+| Optimizer                  | AdamW (8bit) |
+| LoRA Rank                  | 16           |
+| LoRA Alpha                 | 32           |
+| LoRA Dropout               | 0.05         |
+
 ---
 
-# Model Card for LoL_Build-Llama3B
+### 📊 Evaluation
 
-This model is a fine-tuned version of [unsloth/llama-3.2-3b-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-3b-bnb-4bit).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+Trained on a single NVIDIA RTX 3060 GPU.
 
-## Quick start
+| Metric                     | Value              |
+|---------------------------|--------------------|
+| **Final Eval Loss**       | 0.1472             |
+| **Steps Completed**       | 2386               |
+| **Total Epochs Trained**  | 1.0                |
+| **Training Batch Size**   | 32 (effective)     |
+| **Final Learning Rate**   | 1.68e-7            |
+| **Final Grad Norm**       | 1.64               |
+| **Total FLOPs**           | 6.67e+17           |
+| **Eval Runtime**          | 1611.14 seconds    |
+| **Eval Samples/sec**      | 5.27               |
+| **Eval Steps/sec**        | 0.659              |
+
+---
+
+## ⚙️ Usage
 
 ```python
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="HatimF/LoL_Build-Llama3B", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+tokenizer = AutoTokenizer.from_pretrained("HatimF/LoL_Build-Llama3B")
+model = AutoModelForCausalLM.from_pretrained("HatimF/LoL_Build-Llama3B")
 
-## Training procedure
+prompt = "Suggest a build for Ahri in mid lane."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
- 
+---
 
+## 🧠 Intended Use
 
-This model was trained with SFT.
+- **Primary**: Champion item build recommendation for League of Legends.
+- **Limitations**:
+  - May hallucinate outdated items or suggest invalid builds.
+  - Not trained on patch-specific data.
 
-### Framework versions
+---
 
-- TRL: 0.15.2
-- Transformers: 4.51.3
-- Pytorch: 2.6.0
-- Datasets: 3.5.0
-- Tokenizers: 0.21.1
+## 📦 Repository Files
 
-## Citations
+| File                      | Description                     |
+|---------------------------|---------------------------------|
+| `adapter_model.safetensors` | LoRA adapter weights          |
+| `adapter_config.json`     | Configuration for LoRA          |
+| `generation_config.json`  | Decoding hyperparameters        |
+| `training_args.bin`       | TrainingArguments instance (Unsloth) |
+| `trainer_state.json`      | Logged evaluation metrics       |
+| `tokenizer.json`          | Tokenizer vocabulary            |
+| `special_tokens_map.json` | Special tokens                  |
+| `tokenizer_config.json`   | Tokenizer settings              |
 
+---
 
+## 📄 Citation
 
-Cite TRL as:
-    
 ```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
+@misc{hatimf2025lolbuildllama3b,
+  title={LoL_Build-Llama3B},
+  author={HatimF},
+  year={2025},
+  url={https://huggingface.co/HatimF/LoL_Build-Llama3B}
 }
-```
+```