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--- |
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license: apache-2.0 |
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language: |
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- tr |
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--- |
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<img src="https://huggingface.co./Orbina/Orbita-v0.1/resolve/main/orbita.png" |
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alt="Orbita LLM" width="500"/> |
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# Orbita-v0.1 |
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This model is an extended version of a Qwen-based Large Language Model (LLM) for Turkish. It was trained on a cleaned Turkish dataset carefully annotated to carry out turkish instructions in an accurate and organized manner. This model was fully finetuned extensively on 8 H100 GPU's for 2 days using a carefully annotated Turkish dataset. |
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## Model Details |
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- **Base Model**: Qwen 14B based LLM |
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- **Training Dataset**: Annotated Turkish Dataset |
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- **Training Method**: Full Finetuning |
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## Usage Examples |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained( |
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"Qwen/Qwen1.5-14B-Chat", |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-14B-Chat") |
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prompt = "türkiyenin inflasyonu nasıl çözebiliriz?" |
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messages = [ |
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{"role": "system", "content": "Sen Orbina ai tarafından üretelen bir yapay zekasındır, soruları uygun bir şekilde cevap veriyorsun"}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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