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