judgelrm/judge-pairpm

See axolotl config

axolotl version: 0.4.1

base_model: /shared/ssd/models/Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: nuojohnchen/judgelm-train-100k-pair-pm
    type: sharegpt
    conversation: chatml
    split: "train"
    train_on_split: "train"

warmup_steps: 40
val_set_size: 0.0
output_dir: ./pm_models/qwen7b-it_bs128_lr1e-5
#wandb_project: preference-models
#wandb_entity: domain-generalization
wandb_watch:
wandb_name: "qwen-7b-it_bs128_lr1e-5"
#_response_only
wandb_log_model:

train_on_inputs: false

save_safetensors: true
#noisy_embedding_alpha: 10.0 # default for sharegpt type
dataset_prepared_path: ~/data/preference-models/last_run_prepared


dataset_processes: 48
#torch_compile: true
sequence_len: 3072
sample_packing: true
pad_to_sequence_len: true

trust_remote_code: True
adapter:
lora_model_dir:





gradient_checkpointing: True

#warmup_ratio: 0.1
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 1
#max_steps: 10
optimizer: adamw_torch_fused
#optimizer: paged_adamw_32bit
#lr_scheduler: constant_with_warmup
lr_scheduler: cosine
learning_rate: 5.0e-6

weight_decay: 0.0
max_grad_norm: 1.0


group_by_length: false
bf16: auto
fp16: false
tf32: true

early_stopping_patience:
local_rank:
logging_steps: 2
xformers_attention:
flash_attention: true


eval_steps:
eval_table_size:
eval_table_max_new_tokens:
#save_steps: 100
save_strategy: "epoch"
save_total_limit: 1
#save_safetensors: false
debug:


ddp: #true
deepspeed: #deepspeed/zero1.json # multi-gpu only

fsdp:
fsdp_config:
special_tokens:

pm_models/qwen7b-it_bs128_lr1e-5

This model was trained from scratch 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: 5e-06
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 40
• num_epochs: 1

Training results

Framework versions

• Transformers 4.43.3
• Pytorch 2.6.0+cu124
• Datasets 2.19.1
• Tokenizers 0.19.1