Llama 3-8B Turkish Model

This repo contains the experimental-educational fine-tuned model of Meta's new Llama 3.2-1B that can be used for different purposes.

Trained with NVIDIA RTX 3070 Ti, took around 6 hours.

Example Usages

You can use it from Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned")
model = AutoModelForCausalLM.from_pretrained("myzens/llama3-8b-tr-finetuned")

alpaca_prompt = """
Instruction:
{}

Input:
{}

Response:
{}"""

inputs = tokenizer([
    alpaca_prompt.format(
        "",
        "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.",
        "",
)], return_tensors = "pt").to("cuda")


outputs = model.generate(**inputs, max_new_tokens=192)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Transformers Pipeline:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned")
model = AutoModelForCausalLM.from_pretrained("myzens/llama3-8b-tr-finetuned")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

alpaca_prompt = """
Instruction:
{}

Input:
{}

Response:
{}"""

input = alpaca_prompt.format(
        "",
        "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.",
        "",
)

pipe(input)

Output:

Instruction:


Input:
Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.

Response:
1. Anıtkabir - Mustafa Kemal Atatürk'ün mezarı
2. Gençlik ve Spor Sarayı - spor etkinliklerinin yapıldığı yer
3. Kızılay Meydanı - Ankara'nın merkezinde bulunan bir meydan

Important Notes

  • We recommend you to use an Alpaca Prompt Template or another template, otherwise you can see generations with no meanings or repeating the same sentence constantly.
  • Use the model with a CUDA supported GPU.

Fine-tuned by emre570.

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