transformers tensorboard safetensors llama text-generation generated_from_trainer base_model:Drewskidang/up2chatml license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: Drewskidang/up2chatml
tags:
- generated_from_trainer
model-index:
- name: dpo-out
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: Drewskidang/up2chatml
model_type:  LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false
rl: dpo
datasets:
  - path: Drewskidang/DPO_RAG
    split: train
    type: chatml.intel
  - path: Drewskidang/DPOIP
    split: train
    type: chatml.intel
  - path: jondurbin/contextual-dpo-v0.1
    split: train
    type: chatml.prompt_pairs
  - path: unalignment/toxic-dpo-v0.2
    split: train
    type: chatml.prompt_pairs
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./dpo-out


sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false

#adapter: qlora
lora_model_dir:

#lora_r: 32
#lora_alpha: 16
#lora_dropout: 0.05
#lora_target_modules:
#lora_target_linear: true
#lora_fan_in_fan_out:

wandb_project: dpo up
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 3
micro_batch_size: 6
eval_batch_size: 1
num_epochs: 1
#adamw_bnb_8bit
optimizer: adamw_bnb_8bit
lr_scheduler: linear
learning_rate: .0000002
cosine_min_lr_ratio: 0.1
weight_decay: 0.0
# adamw hyperparams
#adam_beta1: 0.9
#adam_beta2: 0.999
# Gradient clipping max norm
max_grad_norm: 0.3
#noisy_embedding_alpha: 5

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

early_stopping_patience:
resume_from_checkpoint: 
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
# fsdp:
  # - full_shard
  # - auto_wrap
# fsdp_config:
  # fsdp_offload_params: false
  # fsdp_state_dict_type: FULL_STATE_DICT
  # fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
warmup_steps: 100
eval_steps: 0
save_steps: 50
eval_sample_packing: false
save_total_limit: 50
debug:
deepspeed:
tokens: # these are delimiters
  - "<|im_start|>"
  - "<|im_end|>"
```

</details><br>

# dpo-out

This model is a fine-tuned version of [Drewskidang/up2chatml](https://huggingface.co/Drewskidang/up2chatml) 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: 2e-07
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 3
- total_train_batch_size: 144
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 88

### Training results



### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0
