Model Card for decruz07/kellemar-DPO-Orca-Distilled-7B

This model was created using OpenHermes-2.5 as the base, and finetuned with argilla/distilabel-intel-orca-dpo-pairs

Model Details

Finetuned with these specific parameters: Steps: 200 Learning Rate: 5e5 Beta: 0.1

Model Description

• Developed by: @decruz
• Funded by [optional]: my full-time job
• Finetuned from model [optional]: teknium/OpenHermes-2.5-Mistral-7B

Benchmarks

OpenLLM

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
68.32	65.78	85.04	63.24	55.54	78.69	61.64

Nous

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
kellemar-DPO-Orca-Distilled-7B	43.61	73.14	55.73	42.28	53.69
kellemar-Orca-DPO-7B	43.35	73.43	54.02	42.24	53.26
OpenHermes-2.5-Mistral-7B	43.07	73.12	53.04	40.96	52.38

Uses

You can use this for basic inference. You could probably finetune with this if you want to.

How to Get Started with the Model

You can create a space out of this, or use basic python code to call the model directly and make inferences to it.

[More Information Needed]

Training Details

The following was used: `training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=200, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", )

Create DPO trainer

dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, max_prompt_length=1024, max_length=1536, )`

Training Data

This was trained with https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs

Training Procedure

Trained with Labonne's Google Colab Notebook on Finetuning Mistral 7B with DPO.

Model Card Authors [optional]

@decruz

Model Card Contact

@decruz on X/Twitter