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axolotl version: 0.4.0
mlflow_tracking_uri: http://127.0.0.1:2340
mlflow_experiment_name: Default
base_model: intervitens/internlm2-limarp-chat-20b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: ResplendentAI/Alpaca_NSFW_Shuffled
type: alpaca
- path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
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
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
outputs/qlora-out
This model is a fine-tuned version of intervitens/internlm2-limarp-chat-20b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9896
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 56
- total_eval_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4668 | 0.0476 | 1 | 1.4615 |
| 1.3541 | 0.2857 | 6 | 1.4253 |
| 1.2057 | 0.5714 | 12 | 1.2120 |
| 1.0818 | 0.8571 | 18 | 1.1259 |
| 1.0835 | 1.1429 | 24 | 1.0750 |
| 1.0503 | 1.4286 | 30 | 1.0451 |
| 1.0031 | 1.7143 | 36 | 1.0288 |
| 0.9728 | 2.0 | 42 | 1.0137 |
| 0.8879 | 2.2857 | 48 | 1.0082 |
| 0.8981 | 2.5714 | 54 | 0.9956 |
| 0.8613 | 2.8571 | 60 | 0.9926 |
| 0.8608 | 3.1429 | 66 | 0.9903 |
| 0.7841 | 3.4286 | 72 | 0.9903 |
| 0.9237 | 3.7143 | 78 | 0.9899 |
| 0.868 | 4.0 | 84 | 0.9896 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1