'Not for all audiences' tag due to dataset only; this one's still fairly censored. But its style feels a bit more on point than v1 without losing too much intelligence.
axolotl version: 0.4.1
base_model: Lambent/arsenic-v1-qwen2.5-14B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
save_safetensors: true
load_in_8bit: false
load_in_4bit: true
strict: false
rl: dpo
# total_num_tokens:
datasets:
- path: Lambent/ai-deconditioning-synthesized-dpo
split: train
type: chatml.prompt_pairs
- path: jondurbin/gutenberg-dpo-v0.1
split: train
type: chatml.prompt_pairs
- path: nbeerbower/gutenberg2-dpo
split: train
type: chatml.prompt_pairs
- path: unalignment/toxic-dpo-v0.2
split: train
type: chatml.prompt_pairs
- path: vicgalle/configurable-system-prompt-multitask
split: train
type: chatml.prompt_pairs
dataset_prepared_path: prepared-dpo
output_dir: ./dpoq
val_set_size: 0.01
seed: 1
sequence_len: 2048
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false
adapter: qlora
lora_model_dir:
lora_r: 256
lora_alpha: 256
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
peft_use_dora: true
wandb_project: arsenic-qwen2.5-qlora-dpo
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00001
#cosine_min_lr_ratio: 0.1
#cosine_constant_lr_ratio: 0.95
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
warmup_steps: 16
evals_per_epoch: 8
saves_per_epoch: 8
save_total_limit: 2
debug:
deepspeed:
weight_decay: 0.001
fsdp:
fsdp_config:
dpoq
This model is a fine-tuned version of Lambent/arsenic-v1-qwen2.5-14B on the None dataset.
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: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 16
- training_steps: 124
Training results
Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0