metadata

language:
  - en
  - hi
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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
License: apache-2.0
Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Model config:

  model = FastLanguageModel.get_peft_model(
  model,
  r = 64, 
  target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj",],
  lora_alpha = 128,
  lora_dropout = 0, 
  bias = "none",   
  use_gradient_checkpointing = True, 
  random_state = 42,
  use_rslora = True,  
  loftq_config = None, 
  )

Training parameters:

  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 = 12,
      gradient_accumulation_steps = 16,
      warmup_ratio = 0.1,
      num_train_epochs = 1,
      learning_rate = 2e-4,
      fp16 = not torch.cuda.is_bf16_supported(),
      bf16 = torch.cuda.is_bf16_supported(),
      logging_steps = 1,
      optim = "paged_adamw_32bit",
      weight_decay = 0.001,
      lr_scheduler_type = "cosine",
      seed = 42,
      output_dir = "outputs",
      report_to = "wandb",
    ),
  )

Training details:

==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
   \\   /|    Num examples = 15,464 | Num Epochs = 1
O^O/ \_/ \    Batch size per device = 12 | Gradient Accumulation steps = 16
\        /    Total batch size = 192 | Total steps = 80
 "-____-"     Number of trainable parameters = 50,462,720

GPU = NVIDIA GeForce RTX 3090. Max memory = 24.0 GB.
Total time taken for 1 epoch - 2h:35m:28s
9443.5288 seconds used for training.
157.39 minutes used for training.
Peak reserved memory = 17.641 GB.
Peak reserved memory for training = 15.344 GB.
Peak reserved memory % of max memory = 73.504 %.
Peak reserved memory for training % of max memory = 63.933 %.

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

[NOTE] TinyLlama's internal maximum sequence length is 2048. We use RoPE Scaling to extend it to 4096 with Unsloth!