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