metadata

language:
  - tr
library_name: transformers
datasets:
  - merve/turkish_instructions
pipeline_tag: text-generation

Model Card for Model ID

This model is a fine-tuned version of YTU's Cosmos GPT2 Language Model. You can check the code from here:Fine Tuning Cosmos by LoRA and QLoRA

Training Details

The model was fine-tuned using LoRA and QLoRA techniques. Training parameters are defined below.

LoRA configs:

r=16
lora_alpha=32
target_modules=c_proj,c_fc, gate_proj, c_proj, c_attn
lora_dropout=0.05
bias="lora_only"
fan_in_fan_out=True
max_seq_length=512
use_rslora=True

Train Parameters:

train_epochs=5
optim="paged_lion_8bit"
learning_rate=2e-4
warmup_ratio=0.03
max_grad_norm=0.3
lr_scheduler_type="linear"

Training Data

For training, I used Merve's Turkish Instructions Dataset, which you can check here: Merve's Turkish Instructions Dataset

Instruction template:

def format_instruction(sample):
    return f"""Sen cevap vermeyi seven yardımcı bir dil modelisin.
        ### Input:
        {sample["talimat"]}
        
        ### Context:
        {sample[" giriş"]}

        ### Response:
        {sample[" çıktı"]}
    """

Generate Output:

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "ardaorcun/finetuned_cosmos2603"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, 
                                             device_map='auto', 
                                             load_in_8bit=True)

sampling_params = dict(do_sample=True, temperature=0.3, top_k=50, top_p=0.9)

pipe = pipeline("text-generation", 
                model=model, 
                tokenizer=tokenizer,
                device_map="auto",
                max_new_tokens=512, 
                return_full_text=True,
                repetition_penalty=1.1
               )

DEFAULT_SYSTEM_PROMPT = "Sen cevap vermeyi seven yardımcı bir dil modelisin.\n"

def format_instruction(sample):
    return f"""{DEFAULT_SYSTEM_PROMPT}
### Input:
{sample["talimat"]}

### Context:
{sample["giriş"]}

### Response:
{sample["çıktı"]}"""

Create Answer:

prompt = "your_prompt"
girdi = "your_entry"
instruction = f"""Sen cevap vermeyi seven yardımcı bir dil modelisin.\n### Input:\n{prompt}\n\n### Context:\n{girdi}\n\n### Response:"""
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length = 2048)
result = pipe(instruction)
print(result[0]['generated_text'][len(instruction):])