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decruz07
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kellemar-DPO-Orca-Distilled-7B-SLERP

Text Generation
Transformers
Safetensors
mistral
text-generation-inference
Inference Endpoints
Model card Files Community
2
kellemar-DPO-Orca-Distilled-7B-SLERP
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---
license: cc-by-nc-4.0
base_model: mlabonne/Marcoro14-7B-slerp
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
---

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

<!-- Provide a quick summary of what the model is/does. -->

This model was created using mlabonne/Marcoro14-7B-slerp 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

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** @decruz
- **Funded by [optional]:** my full-time job
- **Finetuned from model [optional]:** mlabonne/Marcoro14-7B-slerp

## Benchmarks
Top 5 in OpenLLM Benchmarks as of 2024/01/17

**OpenLLM**
|Model| Average  |  ARC | HellaSwag  |  MMLU |  TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
|**kellemar-DPO-Orca-Distilled-7B-SLERP**| 73.71  | 70.48  | 87.56  | 65.33  |64.97   | 81.93 | 72.02 |

**Nous**
Model| AGIEval  |  GPT4All | TruthfulQA  |  Bigbench |  Average |
|---|---|---|---|---|---|
|**kellemar-DPO-Orca-Distilled-7B-SLERP**| 45.27  | 76.42  | 65.48  | 47.21  |58.6   |
|Marcoro14-7B-slerp| 44.66  | 76.24  | 64.15  | 45.64 |57.67 |
|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