Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 language:
-- tr
+- en
 tags:
 - instruction-tuning
 - text-generation
@@ -62,63 +62,6 @@ Erynn excels at a variety of text generation tasks:
  
 - **💻 Basic Code Examples**: Generate simple code snippets for common tasks
 
-## 📥 Using Erynn
-
-The model works best with simple prompt formats. Here's how to use it:
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Model paths
-MODEL_PATH = "erynn/erynn-model"
-
-def load_model():
-   """Load the Erynn model and tokenizer."""
-   # Load model with efficient memory usage
-   model = AutoModelForCausalLM.from_pretrained(
-       MODEL_PATH,
-       device_map="auto",
-       torch_dtype=torch.float16,
-       low_cpu_mem_usage=True
-   )
-   
-   # Load tokenizer
-   tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-   tokenizer.pad_token = tokenizer.eos_token
-   return model, tokenizer
-
-def get_response(model, tokenizer, instruction, context=None):
-   """
-   Generate a response for the given instruction and optional context.
-   Example: get_response(model, tokenizer, "Write an ad for a phone")
-   """
-   # Build simple prompt
-   prompt = f"Instruction: {instruction}\n"
-   if context and context.strip():
-       prompt += f"Context: {context}\n"
-   prompt += "Response: "
-   
-   # Tokenize input
-   inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-   
-   # Generate response
-   with torch.no_grad():
-       output = model.generate(
-           input_ids=inputs["input_ids"],
-           attention_mask=inputs["attention_mask"],
-           max_new_tokens=100,
-           temperature=0.7,
-           top_p=0.9,
-           repetition_penalty=1.2,
-           do_sample=True,
-           pad_token_id=tokenizer.eos_token_id
-       )
-   
-   # Extract response
-   response = tokenizer.decode(output[0], skip_special_tokens=True)
-   response_start = response.find("Response: ") + len("Response: ")
-   return response[response_start:].strip()
 
 ## 📊 Performance Examples
 
@@ -146,9 +89,9 @@ Response:
 
 Thanks to advanced quantization techniques, Erynn runs efficiently on standard hardware:
 
-- **GPU**: NVIDIA GPU with 4GB+ VRAM (tested on RTX 3050 Ti 4GB)
-- **CPU**: Any modern multi-core processor (Intel i7 12700H or equivalent recommended)
-- **RAM**: 8GB+ system RAM recommended
+- **GPU**: NVIDIA GPU RTX 3050 Tİ 4GB VRAM
+- **CPU**: Intel i7 12700H
+- **RAM**: 16GB
 
 ## 🛠️ Limitations