Update README.md

README.md

@@ -1,7 +1,91 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- Turkish
+- TR
+- ORPO
+datasets:
+- selimc/orpo-dpo-mix-TR-20k
+language:
+- tr
+base_model:
+- google/gemma-2-9b-it
 ---
 
-## Model Details
-Coming soon...
+# OrpoGemma-2-9B-TR
+
+OrpoGemma-2-9B-TR is a Turkish fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). It is trained using ORPO on a subset of 1500 rows from the dataset [selimc/orpo-dpo-mix-TR-20k](https://huggingface.co/datasets/selimc/orpo-dpo-mix-tr-20k).
+
+## Training Information
+
+- **Base Model**: [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
+- **Fine-Tuning Technique**: ORPO
+- **Training Data**: 1500 rows from [selimc/orpo-dpo-mix-TR-20k](https://huggingface.co/datasets/selimc/orpo-dpo-mix-tr-20k)
+- **Training Time**: 2.5 hours on NVIDIA H100
+
+  ### QLoRA Configurations:
+    - `lora_r`: 64
+    - `lora_alpha`: 32
+    - `lora_dropout`: 0.05
+  
+  ### ORPO Training Parameters
+    - `lr`: 2e-6
+    - `epochs`: 3
+    - `Per Device Train Batch Size`: 8
+    - `Gradient Accumulation Steps`: 4
+
+## 📈 Training Curves
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65281302cad797fc4abeffd7/bdhWq-TbvQ-h_aSQDf2pv.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65281302cad797fc4abeffd7/HUn3oZyiYA5dVf-fqPM7w.png)
+
+## Model Capabilities
+
+- Generates fluent and coherent text in Turkish.
+- Provides more informative and detailed responses to different types of instructions and question types.
+- May still produce incorrect or nonsensical outputs, user verification is recommended.
+
+## How to Use
+
+```python
+from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer
+import torch
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model_id = "selimc/OrpoGemma-2-9B-TR"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16 ,'quantization_config': bnb_config},
+    tokenizer=tokenizer,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "Gökyüzü neden mavi?"},
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+outputs = pipe(
+    prompt,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.3,
+    top_p=0.9
+)
+
+generated_text = outputs[0]['generated_text']
+response = generated_text[len(prompt):].strip()
+print(response)
+```