Update README.md

README.md

@@ -47,11 +47,7 @@ This model was fine-tuned on a **Tesla T4 (Google Colab)** using **Unsloth**, a
 
 ---
 
-## Installation & Setup
-### 1. Install Dependencies
-```bash
-pip install unsloth transformers torch datasets
-```
+
 
 ### 2. Load the Model
 ```python
@@ -98,51 +94,7 @@ _ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=128,
                    use_cache=True, temperature=1.5, min_p=0.1)
 ```
 
-### **2. Fine-Tuning on a New Dataset**
-```python
-from datasets import load_dataset
-from unsloth.trainer import UnslothVisionDataCollator
-from trl import SFTTrainer, SFTConfig
-
-FastVisionModel.for_training(model)  # Enable training mode
-
-dataset = load_dataset("your_custom_dataset")
-data_collator = UnslothVisionDataCollator(model, tokenizer)
-
-trainer = SFTTrainer(
-    model=model,
-    tokenizer=tokenizer,
-    data_collator=data_collator,
-    train_dataset=dataset,
-    args=SFTConfig(
-        per_device_train_batch_size=2,
-        gradient_accumulation_steps=4,
-        warmup_steps=5,
-        max_steps=30,
-        learning_rate=2e-4,
-        optim="adamw_8bit",
-        output_dir="outputs"
-    ),
-)
-trainer.train()
-```
-
----
-
-## Deployment
-### **Save Locally**
-```python
-model.save_pretrained("Hnm_Llama3.2_(11B)-Vision_lora_model")
-tokenizer.save_pretrained("Hnm_Llama3.2_(11B)-Vision_lora_model")
-```
-
-### **Push to Hugging Face**
-```python
-model.push_to_hub("your_huggingface_username/Hnm_Llama3.2_(11B)-Vision_lora_model")
-tokenizer.push_to_hub("your_huggingface_username/Hnm_Llama3.2_(11B)-Vision_lora_model")
-```
 
----
 
 ## Notes
 - This model is optimized for vision-language tasks in the medical field but can be adapted for other applications.