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Roshang09112007/adaption-air-bench-healthcare-es

sourceHugging Facellama3.3updated 3mo agoView on Hugging Face
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Model Card

AIR-Bench Healthcare ES LoRA Adapter

Model Details

Model Description

This repository contains a LoRA adapter fine-tuned on the AIR-Bench Healthcare Spanish (ES) benchmark using Meta Llama 3.3 70B Instruct as the base model. The adapter is intended for research on healthcare-oriented instruction following, medical question answering, and clinical reasoning tasks in Spanish.

  • —Developed by: Roshan G
  • —Funded by: Independent research
  • —Shared by: Roshan G
  • —Model type: PEFT LoRA Adapter
  • —Language(s): Spanish (es)
  • —License: Subject to the license of the base model (Meta Llama 3.3)
  • —Finetuned from model: meta-llama/Llama-3.3-70B-Instruct

Model Sources

  • —Repository: https://huggingface.co/Roshang09112007/adaption-air-bench-healthcare-es
  • —Base Model: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct

Uses

Direct Use

This model is intended for:

  • —Medical question answering
  • —Healthcare conversational systems
  • —Clinical reasoning research
  • —Instruction-following evaluation
  • —Spanish medical NLP research

Downstream Use

Potential downstream applications include:

  • —Medical chatbots
  • —Clinical decision support research
  • —Healthcare benchmark evaluation
  • —Educational and academic projects
  • —Domain adaptation studies

Out-of-Scope Use

This model is not intended for:

  • —Real-world clinical diagnosis
  • —Medical treatment recommendations
  • —Autonomous healthcare decision making
  • —Emergency medical advice
  • —Any safety-critical healthcare application

Bias, Risks, and Limitations

This model inherits limitations from both:

  • —The underlying Llama 3.3 70B Instruct model
  • —The AIR-Bench Healthcare training data

Potential risks include:

  • —Generation of incorrect medical information
  • —Hallucinated medical facts
  • —Biases present in healthcare datasets
  • —Outdated clinical knowledge
  • —Unsafe recommendations if used without expert oversight

Outputs should always be verified by qualified healthcare professionals.

Recommendations

Users should:

  • —Treat outputs as research artifacts only.
  • —Verify all medical information independently.
  • —Avoid deploying this model in production healthcare systems.
  • —Evaluate performance thoroughly before downstream usage.

How to Get Started with the Model

Install dependencies:

bash
pip install transformers peft accelerate torch

Load the adapter:

python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

BASE_MODEL = "meta-llama/Llama-3.3-70B-Instruct"
ADAPTER = "Roshang09112007/adaption-air-bench-healthcare-es"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER
)

prompt = "¿Cuáles son los síntomas de la diabetes tipo 2?"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=200
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned using the AIR-Bench Healthcare Spanish (ES) dataset consisting of healthcare-related instruction-response pairs.

Training Procedure

The model was trained using Parameter-Efficient Fine-Tuning (PEFT) with the LoRA method.

Training Hyperparameters

  • —Training regime: Supervised fine-tuning (SFT)
  • —Fine-tuning method: LoRA
  • —Framework: PEFT
  • —Precision: Mixed precision
  • —Base model: Llama 3.3 70B Instruct

Evaluation

Testing Data

Evaluation was performed using healthcare-related Spanish benchmark examples.

Factors

Evaluation considered:

  • —Instruction following
  • —Medical reasoning
  • —Response relevance
  • —Spanish language quality

Metrics

Potential evaluation metrics include:

  • —Accuracy
  • —Exact Match (EM)
  • —BLEU
  • —ROUGE
  • —Human evaluation

Results

Evaluation results are currently under investigation and may vary depending on task and prompting strategy.

Summary

This repository provides a LoRA adapter specialized for healthcare-related Spanish instruction-following tasks using Meta Llama 3.3 70B Instruct as the backbone model.

Environmental Impact

Carbon emissions were not formally measured.

  • —Hardware Type: GPU Accelerator
  • —Hours Used: Not reported
  • —Cloud Provider: Not reported
  • —Compute Region: Not reported
  • —Carbon Emitted: Unknown

Technical Specifications

Model Architecture and Objective

  • —Architecture: Transformer Decoder
  • —Base Model: Meta Llama 3.3 70B Instruct
  • —Adaptation Method: LoRA (PEFT)
  • —Objective: Instruction fine-tuning for healthcare tasks

Compute Infrastructure

Fine-tuning performed using GPU-based hardware acceleration.

Hardware

  • —GPU: Not specified

Software

  • —Python 3.10
  • —Transformers
  • —PEFT 0.15.1
  • —PyTorch
  • —Accelerate

Citation

BibTeX

bibtex
@misc{roshan2026airbenchhealthcare,
  title={AIR-Bench Healthcare ES LoRA Adapter},
  author={Roshan G},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Roshang09112007/adaption-air-bench-healthcare-es}}
}

APA

Roshan G. (2026). AIR-Bench Healthcare ES LoRA Adapter. Hugging Face.

More Information

This model is released for research and educational purposes only.

Model Card Authors

Roshan G

Model Card Contact

Hugging Face: https://huggingface.co/Roshang09112007

Framework Versions

  • —PEFT: 0.15.1
  • —Transformers: Compatible
  • —PyTorch: Compatible