--- language: - en - hi license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-2b-bnb-4bit datasets: - yahma/alpaca-cleaned - ravithejads/samvaad-hi-filtered - HydraIndicLM/hindi_alpaca_dolly_67k --- # TinyLlama-1.1B-Hinglish-LORA-v1.0 model - **Developed by:** [Kiran Kunapuli](https://www.linkedin.com/in/kirankunapuli/) - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b-bnb-4bit - - **Model config:** ```python model = FastLanguageModel.get_peft_model( model, r = 16, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 32, lora_dropout = 0, bias = "none", use_gradient_checkpointing = True, random_state = 42, use_rslora = True, loftq_config = None, ) ``` - **Training parameters:** ```python trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = True, args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 5, max_steps = 120, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 42, output_dir = "outputs", report_to = "wandb", ), ) ``` - **Training details:** ``` ==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 14,343 | Num Epochs = 1 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4 \ / Total batch size = 8 | Total steps = 120 "-____-" Number of trainable parameters = 19,611,648 GPU = Tesla T4. Max memory = 14.748 GB. 2118.7553 seconds used for training. 35.31 minutes used for training. Peak reserved memory = 9.172 GB. Peak reserved memory for training = 6.758 GB. Peak reserved memory % of max memory = 62.191 %. Peak reserved memory for training % of max memory = 45.823 %. ``` This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth)