Update README.md

README.md

@@ -1,3 +1,71 @@
----
-license: llama3.1
----
+---
+license: llama3.1
+language:
+- tr
+base_model: meta-llama/Meta-Llama-3.1-8B
+---
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6639e48c27ef2d37a71eb4aa/Ds_KOVYwhRQ1FQY8S4WqO.png"
+alt="CEREBRUM LLM" width="420"/>
+
+
+# CERE V2 -LLMA-3.1-8b-TR
+
+This model is an fine-tuned version of a Llama3.1 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner. 
+
+## Model Details
+
+- **Base Model**: LLMA 3.1 8B based LLM
+- **Tokenizer Extension**: Specifically extended for Turkish
+- **Training Dataset**: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets
+- **Training Method**: Initially with DORA, followed by fine-tuning with LORA
+
+## Benchmark Results
+
+- **Winogrande_tr**: 56.16
+- **TruthfulQA_tr_v0.2**: 47.46
+- **Mmlu_tr_v0.2**: 46.46
+- **HellaSwag_tr_v0.2**: 48.87
+- **GSM8k_tr_v0.2**: 25.43
+- **Arc_tr_v0.2**: 41.97
+
+
+## Usage Examples
+
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Cerebrum/cere-llama-3.1-8B-tr",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3.1-8B-tr")
+
+prompt = "Python'da ekrana 'Merhaba Dünya' nasıl yazılır?"
+messages = [
+    {"role": "system", "content": "Sen, Cerebrum Tech tarafından üretilen ve verilen talimatları takip ederek en iyi cevabı üretmeye çalışan yardımcı bir yapay zekasın."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    temperature=0.3,
+    top_k=50,
+    top_p=0.9,
+    max_new_tokens=512,
+    repetition_penalty=1,
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```