Update README.md

README.md

@@ -2,5 +2,69 @@
 license: apache-2.0
 language:
 - tr
-pipeline_tag: text-generation
----
+---
+
+
+# Morfoz-LLM-8b-v1.0
+
+This model is an extended version of a Llama-3 8B Instruct-based Large Language Model (LLM) for Turkish. It was trained on a cleaned Turkish raw dataset. We utilized Turkish instruction sets created from various open-source for fine-tuning with the LORA method.
+## Model Details
+
+- **Base Model**: Meta Llama-3 8B Instruct
+- **Tokenizer Extension**: Specifically extended for Turkish
+- **Training Dataset**: Cleaned Turkish raw data with custom Turkish instruction sets
+- **Training Method**: Fine-tuning with LORA
+
+
+### LORA Fine-Tuning Configuration
+
+- `lora_alpha`: 16
+- `lora_dropout`: 0.05
+- `r`: 64
+- `target_modules`: "all-linear"
+
+## Usage Examples
+
+```python
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("Morfoz-Aigap/Morfoz-LLM-8b-v1.0")
+model = AutoModelForCausalLM.from_pretrained("Morfoz-Aigap/Morfoz-LLM-8b-v1.0", torch_dtype=torch.bfloat16, device_map={"": 0},low_cpu_mem_usage=True)
+
+messages = [
+    {"role": "user", "content": "Kırmızı başlıklı kız adında kısa bir çocuk hikayesi yazabilir misin?"}
+
+]
+
+top_k = 50
+top_p = 0.9
+temperature = 0.6
+def get_formatted_input(messages):
+
+    for item in messages:
+        if item['role'] == "user":
+            item['content'] = item['content']
+            break
+
+    conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:"
+    formatted_input = "\n\n" + conversation
+
+    return formatted_input
+
+formatted_input = get_formatted_input(messages)
+print(formatted_input)
+tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(input_ids=tokenized_prompt.input_ids, do_sample = True, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=256, eos_token_id=terminators, top_p=top_p, temperature=temperature)
+
+response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+
+