Upload RCDS1.yaml

yaml file for RosettaCode DataSet
auto-created using Sonnet 3.5
untested as of July 17, 2024

RCDS1.yaml

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+base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: Guytron/RosettaCodeDataSet1
+    type: json  # Assuming the dataset is in JSON format
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./qlora-out-rosetta
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048  # Increased to accommodate potentially longer code samples
+sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project: rosetta-code-training
+wandb_entity:
+wandb_watch:
+wandb_name: rosetta-code-run-1
+wandb_log_model:
+
+mlflow_experiment_name: rosetta-code-experiment
+
+gradient_accumulation_steps: 4  # Increased to handle larger dataset
+micro_batch_size: 2  # Adjusted based on your GPU memory
+num_epochs: 3
+max_steps: -1  # Set to -1 to train on the entire dataset
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: true  # Changed to true for efficiency with varying length samples
+bf16: false
+fp16: true
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 10
+xformers_attention:
+flash_attention: false
+
+warmup_steps: 100  # Increased for a larger dataset
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.01  # Added some weight decay for regularization
+fsdp:
+fsdp_config:
+special_tokens: