--- base_model: teknium/OpenHermes-2.5-Mistral-7B license: apache-2.0 datasets: - Intel/orca_dpo_pairs --- # Model Card for decruz07/kellemar-Orca-DPO-7B This model was created using OpenHermes-2.5 as the base, and finetuned with 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** | AGIEval | GPT4All | TruthfulQA | Bigbench | Average | |---|---|---|---|---| | 43.35 | 73.43 | 54.02 | 42.24 |53.26 | ## 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