---
license: gemma
base_model: google/gemma-2b-it
tags:
- generated_from_trainer
model-index:
- name: pm_models/gemma-2b-it_lr1e-5_ultrafeedback
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: google/gemma-2b-it
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# tokenizer_config: google/gemma-2b-it

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /scratch/jiarui14/dpo-ood/LLM-finetune/WX-Reward-Modeling/pair-pm/ultrafeedback-single
    conversation: gemma
    type: sharegpt.load_ultrachat
    split: "train"
    train_on_split: "train"

warmup_steps: 40
val_set_size: 0.0
output_dir: ./pm_models/gemma-2b-it_lr1e-5_ultrafeedback
#wandb_project: preference-models
#wandb_entity: domain-generalization
wandb_watch:
wandb_name: "gemma-2b-it_lr1e-5_ultrafeedback"
#_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/gemma-2b-it/ultrafeedback


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: false

#warmup_ratio: 0.1
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 1e-5

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: 1
xformers_attention:
flash_attention: true


eval_steps:
eval_table_size:
eval_table_max_new_tokens:
save_steps: 50
save_strategy: "steps"
save_total_limit: 2
debug:


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

fsdp:
fsdp_config:
special_tokens:


```

</details><br>

# pm_models/gemma-2b-it_lr1e-5_ultrafeedback

This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) 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: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- 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 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1