generate-data and some eval

examples/classify-recipes/training-config.yaml

@@ -0,0 +1,73 @@
+# This file is used by the training script in train.ipynb. You can read more about
+# the format and see more examples at https://github.com/OpenAccess-AI-Collective/axolotl.
+# One of the parameters you might want to play around with is `num_epochs`: if you have a
+# smaller dataset size, making that large can have good results.
+
+base_model: meta-llama/Llama-2-7b-hf
+base_model_config: meta-llama/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: ./data/train.jsonl
+    type: alpaca_instruct.load_no_prompt
+dataset_prepared_path: ./data/last_run_prepared
+val_set_size: 0.05
+output_dir: ./models/run1
+
+sequence_len: 4096
+sample_packing: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+# This will report stats from your training run to https://wandb.ai/. If you don't want to create a wandb account you can comment this section out.
+wandb_project: classify-recipes
+wandb_entity:
+wandb_watch:
+wandb_run_id: run1
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 5
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 20
+save_steps: 60
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"