maximilianshwarzmullers/tm-hukuk

Model Card for Model ID

maximilianshwarzmullers/tm-hukuk

Model Description

This is Turkmen LLM Finetuned in Turkmen law data

• Developed by: [Annamyrat Saparow]
• Funded date: [30.04.2025]
• Shared date: [07.05.2025]
• Model type: [Instruction model]
• Language(s) (NLP): [Turkmen, English]
• License: [MIT]
• Finetuned from model [optional]: [llama 3.1 8b instruct ]

Model Sources [optional]

• Repository: [maximilianshwarzmullers/tm-hukuk]

Training Data

Training data is maximilianshwarzmullers/hukukchy for law It is my own dataset

Training Procedure

QLora

Preprocessing [optional]

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

Instruction:

{}

Input:

{}

Response:

{}"""

EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN def formatting_prompts_func(examples): instructions = examples["instruction"] inputs = examples["question"] outputs = examples["answer"] texts = [] for instruction, input, output in zip(instructions, inputs, outputs): # Must add EOS_TOKEN, otherwise your generation will go on forever! text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN texts.append(text) return { "text" : texts, } pass

from datasets import load_dataset dataset = load_dataset("maximilianshwarzmullers/hukukchy", split = "train") dataset = dataset.map(formatting_prompts_func, batched = True,)

Training Hyperparameters

from trl import SFTTrainer from transformers import TrainingArguments from unsloth import is_bfloat16_supported

trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = False, # Can make training 5x faster for short sequences. args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 5, # num_train_epochs = 1, # Set this for 1 full training run. max_steps = 750, learning_rate = 3e-5, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", report_to = "none", # Use this for WandB etc ), )