Model Card for decruz07/kellemar-DPO-7B-v1.01
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
| AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|
| 43.17 | 73.25 | 55.87 | 42.2 | 53.62 |
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
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
