Create README.md
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README.md
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---
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base_model: mlabonne/Marcoro14-7B-slerp
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license: apache-2.0
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datasets:
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- argilla/distilabel-intel-orca-dpo-pairs
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---
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# Model Card for decruz07/kellemar-DPO-Orca-Distilled-7B
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<!-- Provide a quick summary of what the model is/does. -->
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This model was created using mlabonne/Marcoro14-7B-slerp as the base, and finetuned with argilla/distilabel-intel-orca-dpo-pairs
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## Model Details
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Finetuned with these specific parameters:
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Steps: 200
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Learning Rate: 5e5
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Beta: 0.1
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** @decruz
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- **Funded by [optional]:** my full-time job
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- **Finetuned from model [optional]:** teknium/OpenHermes-2.5-Mistral-7B
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## Benchmarks
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## Uses
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You can use this for basic inference. You could probably finetune with this if you want to.
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## How to Get Started with the Model
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You can create a space out of this, or use basic python code to call the model directly and make inferences to it.
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[More Information Needed]
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## Training Details
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The following was used:
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`training_args = TrainingArguments(
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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gradient_checkpointing=True,
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learning_rate=5e-5,
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lr_scheduler_type="cosine",
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max_steps=200,
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save_strategy="no",
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logging_steps=1,
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output_dir=new_model,
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optim="paged_adamw_32bit",
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warmup_steps=100,
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bf16=True,
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report_to="wandb",
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)
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# Create DPO trainer
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dpo_trainer = DPOTrainer(
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model,
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ref_model,
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args=training_args,
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train_dataset=dataset,
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tokenizer=tokenizer,
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peft_config=peft_config,
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beta=0.1,
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max_prompt_length=1024,
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max_length=1536,
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)`
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### Training Data
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This was trained with https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
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### Training Procedure
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Trained with Labonne's Google Colab Notebook on Finetuning Mistral 7B with DPO.
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## Model Card Authors [optional]
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@decruz
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## Model Card Contact
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@decruz on X/Twitter
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