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noeloco
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modeltest1-dpo

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dpo
trl
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modeltest1-dpo
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---
license: llama2
library_name: peft
tags:
- axolotl
- dpo
- trl
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: modeltest1-dpo
  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: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
is_llama_derived_model: true

hub_model_id: noeloco/modeltest1-dpo

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: noeloco/fizzbuzz-sft
    type: alpaca
    ds_type: json

hf_use_auth_token: true
push_dataset_to_hub: noeloco
val_set_size: 0.05
output_dir: ./lora-out
chat_template: chatml

rl: dpo
datasets:
    - path: noeloco/fizzbuzz-dpo
      split: train
      data_files: 
        - /tmp/fizzbuzz-ft/datasets/training-set-dpo.json  
        #type:
        # field_prompt: question
        # field_chosen: chosen
        # field_rejected: rejected
      ds_type: json
        #type: intel_apply_chatml
      type: chatml.intel

hf_use_auth_token: true
push_dataset_to_hub: noeloco
val_set_size: 0.05
output_dir: ./lora-out
chat_template: chatml


sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 8
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: runpod1
wandb_entity:
wandb_watch:
wandb_name: 
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
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
saves_per_epoch: 1
debug: true
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# modeltest1-dpo

This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 222

### Training results



### Framework versions

- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0