--- base_model: unsloth/Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en datasets: - CreitinGameplays/mango-v2 --- # Uploaded finetuned model - **Developed by:** CreitinGameplays - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.1-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) Trained using the following parameters: ```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 = 16, lora_dropout = 0, bias = "none", use_gradient_checkpointing = "unsloth", random_state = 3407, use_rslora = False, loftq_config = None, ) training_args = TrainingArguments( per_device_train_batch_size = 12, gradient_accumulation_steps = 2, warmup_steps = 100, num_train_epochs = 2, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 10, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, output_dir = OUTPUT_DIR, report_to = "none", save_strategy = "steps", save_steps = 50, save_total_limit = 3, load_best_model_at_end = False, ) 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, args = training_args, ) trainer = train_on_responses_only( trainer, instruction_part = "<|start_header_id|>user<|end_header_id|>\n\n", # llama response_part = "<|start_header_id|>assistant<|end_header_id|>\n\n", ) ```