Best of the Rest
Collection
If you only try one of my models, make it one of these.
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3 items
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axolotl version: 0.4.1
base_model: jeiku/Dante_9B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: FourOhFour/RP_Phase
type: sharegpt
conversation: chatml
chat_template: chatml
val_set_size: 0.0025
output_dir: ./outputs/out
adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: false
liger_swiglu: true
liger_fused_linear_cross_entropy: false
wandb_project: chatml9B
wandb_entity:
wandb_watch:
wandb_name: chatml9B
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000008
weight_decay: 0.05
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
special_tokens:
pad_token: <pad>
This model is a fine-tuned version of jeiku/Dante_9B on the None dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.7474 | 0.0135 | 1 | 1.7996 |
1.6968 | 0.2570 | 19 | 0.9551 |
1.6583 | 0.5139 | 38 | 0.8805 |
1.5418 | 0.7709 | 57 | 0.7926 |
1.3997 | 1.0271 | 76 | 0.7500 |
1.3921 | 1.2847 | 95 | 0.7168 |
1.4141 | 1.5424 | 114 | 0.7155 |
1.4139 | 1.8 | 133 | 0.7075 |