Model Card for oswestry-mistral-finetuned

This is a fine-tuned version of the Mistral-7B-Instruct-v0.2 model specialized in scoring functional disability interviews using the Oswestry Disability Index (ODI) scale in Spanish. The model is designed to transform clinical-style interview transcripts into structured scores, demonstrating improved performance compared to the base model.

Model Details

Model Description

Developed by: [Alejandro M.L]
Model type: Causal Decoder-Only Transformer (LLM)
Language(s): Spanish (with clinical vocabulary)
License: apache-2.0
Fine-tuned from model: mistral-7b-instruct-v0.2

Model Sources

Repository: Github
Paper [Transformación de Informes Médicos en escalas funcionales]:

Uses

Direct Use

The model takes as input a clinical interview transcript in Spanish (following a structured instruction format) and returns a text output containing the scores of each item in the Oswestry scale.

Downstream Use

Can be integrated into tools that support:

Preliminary functional assessment in telemedicine
Research pipelines for NLP in healthcare
Spanish-language LLM benchmarking on medical tasks

Out-of-Scope Use

Not suitable for general-purpose chat applications
Should not be used for real clinical decisions without expert supervision
Not intended for languages other than Spanish

Bias, Risks, and Limitations

The model was fine-tuned on synthetic data, which may limit generalizability.
Outputs might include hallucinations if the input format is not followed.
It reflects the biases of the base model and the prompt structure used.

Recommendations

Use only for research and non-critical applications.
Always validate outputs against clinical judgment.
Further training with real anonymized clinical data is highly recommended.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("DrAleML/Oswestry-Instruct")
tokenizer = AutoTokenizer.from_pretrained("DrAleML/Oswestry-Instruct")

prompt = "Entrevista:\nPaciente refiere dolor lumbar que aumenta al estar de pie...\n\nResponde con puntuación Oswestry:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))