add config

config/llama3-lima.yml

@@ -0,0 +1,83 @@
+#Mistral-7b
+base_model: NousResearch/Meta-Llama-3-8B
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path:  64bits/lima_vicuna_format
+    #for type,conversation arguments read axolotl readme and pick what is suited for your project, I wanted a chatbot and put sharegpt and chatml
+    type: sharegpt
+    conversation: chatml
+chat_template: chatml
+dataset_prepared_path: lima/data #Path to json dataset file in huggingface
+val_set_size: 0.05
+output_dir: ./llama3-lima-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+
+wandb_project: llama3-lima
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 3
+optimizer: paged_adamw_32bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 2
+xformers_attention:
+flash_attention: true
+
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_table_max_new_tokens:
+saves_per_epoch: 1
+debug: true
+deepspeed:
+weight_decay: 0.05
+fsdp:
+fsdp_config:
+special_tokens:
+  eos_token: "<|im_end|>"
+  pad_token: "<|end_of_text|>"
+tokens:
+  - "<|im_start|>"
+  - "<|im_end|>"