metadata

library_name: transformers
license: gemma
language:
  - tr
base_model:
  - google/gemma-2-9b-it
pipeline_tag: text-generation

Gemma-2-9b-it-tr

Gemma-2-9b-it-tr is a finetuned version of google/gemma-2-9b-it on a carefully curated and manually filtered dataset of 55k question answering and conversational samples in Turkish.

Training Details

Base model: google/gemma-2-9b-it
Training data: A filtered version of metedb/turkish_llm_datasets and a small private dataset of 8k conversational samples on various topics.
Training setup: We performed supervised fine tuning with LoRA with rank=128 and lora_alpha=64. Training took 4 days on a single RTX 6000 Ada.

Compared to the base model, we find Gemma-2-9b-tr has superior conversational and reasoning skills.

Usage

You can load and use neuralwork/gemma-2-9b-it-tras follows.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
   "neuralwork/gemma-2-9b-it-tr",
   torch_dtype=torch.bfloat16,
   device_map="auto",
   trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained("neuralwork/gemma-2-9b-it-tr")

messages = [
   {"role": "user", "content": "Python'da bir öğenin bir listede geçip geçmediğini nasıl kontrol edebilirim?"},
]

prompt = tokenizer.apply_chat_template(
   messages,
   tokenize=False,
   add_generation_prompt=True
)

outputs = model.generate(
   tokenizer(prompt, return_tensors="pt").input_ids.to(model.device),
   max_new_tokens=1024,
   do_sample=True,
   temperature=0.7,
   top_p=0.9
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)[len(prompt):]
print(response)