erythropygia
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README.md
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---
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license: cc-by-nc-4.0
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language:
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- tr
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pipeline_tag: question-answering
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---
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# Model Card for Model ID
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gemma-2b-tr fine-tuned with Turkish Instruction-Response pairs.
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## Restrictions
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Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms
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Please refer to the gemma use restrictions before start using the model.
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https://ai.google.dev/gemma/terms#3.2-use
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## Using model
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```Python
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import torch,re
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "erythropygia/Gemma2b-Turkish-Instruction"
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0})
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tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side="left")
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def get_completion(query: str, model, tokenizer) -> str:
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device = "cuda:0"
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prompt_template = """
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<start_of_turn>user
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Alt satırdaki soruya cevap ver:\n
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{query}
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<end_of_turn>\n<start_of_turn>model
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"""
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prompt = prompt_template.format(query=query)
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encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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model_inputs = encodeds.to(device)
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#max_new_tokens = 200, temperature = 0.9, repetition_penalty = 0.5, disabled
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#num_return_sequences=1, max_length = 256,
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generated_ids = model.generate(**model_inputs, max_new_tokens = 256, do_sample=True, pad_token_id=tokenizer.eos_token_id)
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# decoded = tokenizer.batch_decode(generated_ids)
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decoded = tokenizer.decode(generated_ids[0], skip_special_tokens=False)
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# Kapanmamış etiketleri silmek için düzenli ifade kullanma
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decoded = re.sub(r'<(end_of_turn|start_of_turn|eos|bos)>[^<]*$', '', decoded)
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decoded = re.sub(r'<(end_of_turn|start_of_turn|eos|bos)>', '', decoded)
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return decoded.strip()
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result = get_completion(query="int türünde üç parametre alan ve bunların toplamını döndüren bir işlev oluşturun.", model=model, tokenizer=tokenizer)
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print(result)
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```
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## Training Details
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### Training Data
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- Dataset size: ~75k instruction-response pair.
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### Training Procedure
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#### Training Hyperparameters
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- **Epochs:** 1
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- **Context length:** 1024
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- **LoRA Rank:** 32
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- **LoRA Alpha:** 64
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- **LoRA Dropout:** 0.05
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