120824_Beta_finetuned_model_prometheus-8x7b-v2.0_Feedback-Collection

Model Card for Model ID

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

This model is a finetuned version of Prometheus2-8x7b-hf using a Dataset Feedback Collection with questions, answers, and evaluations related to general subjects.

Model Details

It used a QLORA version for the finetuning process.

Model Description

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

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Guilherme Tavares dos Santos
• Model type: Transformer
• Language(s) (NLP): Python, Pytorch
• License: Apache License 2.0 Open-sourced for research and commercial use (https://github.com/prometheus/prometheus/blob/main/LICENSE)
• Finetuned from the model: QLORA

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Use destinated to improve automatic healthcare evaluations. LLM as a Judge.

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The dataset used is not specialized in healthcare questions, answers, and evaluation with rubric scores. The main risk is getting evaluations not so customized to our proposal.

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. More specific healthcare data is needed for further recommendations.

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

The prometheus-eval/Feedback-Collection dataset contains 9,996 rows. (90%) of those rows was randomized and used for training.

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

It wasn't a full finetuning, but only of the following linear layers of the transformer model: Percentage of trained parameters Trainable: 121112576 Total: 46823905280 Percentage: 0.2587% Linear Layers: ['w3', 'o_proj', 'q_proj', 'gate', 'v_proj', 'w1', 'w2', 'k_proj']

Training Hyperparameters

• Training regime: [More Information Needed]
• <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

For LORA step it was used the following hyperparameters: r=8, lora_alpha=32, target_modules=modules, lora_dropout=0.05

The hiperparameters related to the training process:

    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    warmup_steps=0,
    max_steps=100,
    learning_rate=2e-4,
    logging_steps=20,
    output_dir="outputs",
    optim="paged_adamw_8bit",
    save_strategy="epoch"
    

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

It was used for the assessment of the training process Loss Function (built-in Cross-entropy for Transformers). It's the cross-entropy between the predicted probability matrix between the predicted probability matrix ∥sentence length∥×∥vocab∥ (right before taking the argmax to find the token to output), and the ∥sentence length∥ -length vector of token IDs as the true label. It is a method of evaluating how well your algorithm models your dataset. In this case, it's a method to assess how well a transformer predicts the evaluations for each pair question and generated answer. There is a decrease in the Loss function indicating a learning process. Ideally, adding more data and adjusting hyperparameters can close this function to zero.

Results

The metrics related to the Finetuning process:

global_step=100 train_runtime: 1584 s outputs train/loss: 0.4637 https://wandb.ai/eeg-neuko/huggingface/runs/bfnozz0c?nw=nwuserguicondor1512

Hardware

It used a GPU A100 with VRAM of 40 GB from Google Colab Pro+.