simplify the example configs to be more minimal and less daunting (#2486) [skip ci]

* simplify the example configs to be more minimal and less daunting

* drop empty s2_attention from example yamls

examples/stablelm-2/1.6b/fft.yml

@@ -6,9 +6,6 @@ tokenizer_type: AutoTokenizer
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: false
 strict: false
 
 datasets:
@@ -28,7 +25,6 @@ lora_r:
 lora_alpha:
 lora_dropout:
 lora_target_linear:
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -43,18 +39,12 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
@@ -63,11 +53,8 @@ flash_attn_fuse_mlp: true
 
 warmup_steps: 100
 evals_per_epoch: 4
-eval_table_size:
 saves_per_epoch: 1
-debug:
+
 deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
 weight_decay: 0.1
-fsdp:
-fsdp_config:
 special_tokens:

examples/stablelm-2/1.6b/lora.yml

@@ -28,7 +28,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 lora_target_linear: true
-lora_fan_in_fan_out:
 
 wandb_project:
 wandb_entity:
@@ -43,18 +42,12 @@ optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
 
-train_on_inputs: false
-group_by_length: false
 bf16: auto
-fp16:
 tf32: false
 
 gradient_checkpointing: true
-early_stopping_patience:
 resume_from_checkpoint:
-local_rank:
 logging_steps: 1
-xformers_attention:
 flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
@@ -62,9 +55,5 @@ flash_attn_rms_norm: true
 warmup_steps: 10
 evals_per_epoch: 4
 saves_per_epoch: 1
-debug:
-deepspeed:
 weight_decay: 0.0
-fsdp:
-fsdp_config:
 special_tokens: