--- library_name: peft base_model: meta-llama/Llama-2-7b-hf language: - en - tr tags: - llama-2 - turkish - dolly datasets: - atasoglu/databricks-dolly-15k-tr --- # Model Card for Model ID malhajar/Llama-2-7b-chat-dolly-tr is a finetuned version of Llama-2-7b-hf using SFT Training. This model can answer information in turkish language as it is finetuned on a turkish dataset specifically [`databricks-dolly-15k-tr`]( https://huggingface.co/datasets/atasoglu/databricks-dolly-15k-tr) ![llama](./llama.png) ### Model Description - **Developed by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/) - **Language(s) (NLP):** Turkish - **Finetuned from model:** [`meta-llama/Llama-2-7b-hf`](https://huggingface.co/meta-llama/Llama-2-7b-hf) ### Prompt Template ``` [INST] [/INST] ``` ## How to Get Started with the Model Use the code sample provided in the original post to interact with the model. ```python from transformers import AutoTokenizer,AutoModelForCausalLM model_id = "malhajar/Llama-2-7b-chat-dolly-tr" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", torch_dtype=torch.float16, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_id) question: "Türkiyenin en büyük şehir nedir?" # For generating a response prompt = ''' [INST] {question} [/INST] ''' input_ids = tokenizer(prompt, return_tensors="pt").input_ids output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,repetition_penalty=1.3 top_p=0.95) response = tokenizer.decode(output[0]) print(response) ``` ## Example Generation ``` [INST] Türkiyenin en büyük şehir nedir? [/INST] İstanbul, dünyanın en kalabalık ikinci ve Turuncu kütle'de yer almaktadır. Pek çok insandaki birçok ünlüsün bulundusuyla biliniyor. ```