---
license: apache-2.0
base_model: kaist-ai/mpa-Mistral-7b-v0.2-hf-sft-66k
tags:
- axolotl
- dpo
- trl
- dpo
- generated_from_trainer
model-index:
- name: mpa-Mistral-7b-v0.2-hf-66k-dpo-5e-7
  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: kaist-ai/mpa-Mistral-7b-v0.2-hf-sft-66k
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

rl: dpo
datasets:
  - path: kaist-ai/mpa-train-dpo-66k
    type: chatml.argilla
    # conversation: mistral
      
dataset_prepared_path:
hub_model_id: kaist-ai/mpa-Mistral-7b-v0.2-hf-66k-dpo-5e-7
hub_strategy: checkpoint
# val_set_size: 0
output_dir: /mnt/nas/seongyun/axolotl/outputs/mpa_66k_dpo-5e-7

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: mpa
wandb_entity: seongyun
wandb_watch:
wandb_name: mpa_mistral-7b-v0.2-hf-66k-dpo-5e-7
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0000005

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: 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:

```

</details><br>

# mpa-Mistral-7b-v0.2-hf-66k-dpo-5e-7

This model is a fine-tuned version of [kaist-ai/mpa-Mistral-7b-v0.2-hf-sft-66k](https://huggingface.co/kaist-ai/mpa-Mistral-7b-v0.2-hf-sft-66k) 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-07
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 8143

### Training results



### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0