Uploaded model

• Developed by: UsernameJustAnother
• License: apache-2.0
• Finetuned from model : unsloth/Mistral-Nemo-Instruct-2407

Experimental RP Finetune with secret sauce dataset, rsLoRA, r = 64, on an Colab A100 instance. 30GB vRAM used, 2 epochs ~ 3hrs of training.

==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
   \\   /|    Num examples = 8,160 | Num Epochs = 2
O^O/ \_/ \    Batch size per device = 2 | Gradient Accumulation steps = 4
\        /    Total batch size = 8 | Total steps = 2,040
 "-____-"     Number of trainable parameters = 228,065,280


        r = 64, 
        target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                          "gate_proj", "up_proj", "down_proj",],
        lora_alpha = 64, 
        lora_dropout = 0, # Supports any, but = 0 is optimized
        bias = "none",    # Supports any, but = "none" is optimized
        use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
        random_state = 3407,
        use_rslora = True,  # lora_alpha --> 8
        loftq_config = None,

        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 5,
        num_train_epochs = 2, 
        learning_rate = 2e-5, # down from 2e-4, could go down to (5e-5 then 1e-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,

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.