---
base_model: unsloth/mistral-small-24b-instruct-2501-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/mistral-small-24b-instruct-2501-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
- psychiatry
- spanish
- EHR
- mental
- illness
- clinical
- symptoms
- phenotypes
license: apache-2.0
language:
- es
---

# Model Card for Model ID

<!-- Extract psychiatric symptoms from Spanish-language clinical text and EHR notes -->



## Model Details

### Model Description

<!-- The accurate detection of clinical phenotypes from electronic health records (EHRs) is pivotal for advancing large-scale genetic and longitudinal studies in psychiatry. Free-text clinical notes are an essential source of symptom-level information, particularly in psychiatry. However, the automated extraction of symptoms from clinical text remains challenging. To generate a fine-tuned LLM for this purpose that can be shared with the scientific and medical community, we created a fully synthetic dataset free of patient information but based on original annotations. We fine-tuned a top-performing LLM on this data, creating "Mistral-small-psych", an LLM that can detect psychiatric phenotypes from Spanish text with performance comparable to that of LLMs trained on real EHR data (macro-F1=0.79 on 109 phenotypes). 

 -->



- **Developed by:** Clara Frydman-Gani, Alejandro Arias, Maria Perez Vallejo, John Daniel Londoño Martínez, Johanna Valencia-Echeverry, Mauricio Castaño, Alex A T Bui, Nelson B Freimer, Carlos Lopez-Jaramillo, Loes M Olde Loohuis
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** Spanish
- **License:** Apache 2.0
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [[More Information Needed]](https://github.com/clarafrydman/LLMs_for_psychiatric_phenotyping/tree/master/)
- **Paper [optional]:** Frydman-Gani C, Arias A, Vallejo MP, Londoño Martínez JD, Valencia-Echeverry J, Castaño M, Bui AAT, Freimer NB, Lopez-Jaramillo C, Olde Loohuis LM. Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records. medRxiv [Preprint]. 2025 Aug 12:2025.08.07.25333172. doi: 10.1101/2025.08.07.25333172. PMID: 40832382; PMCID: PMC12363723.
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### 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 -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- Frydman-Gani C, Arias A, Vallejo MP, Londoño Martínez JD, Valencia-Echeverry J, Castaño M, Bui AAT, Freimer NB, Lopez-Jaramillo C, Olde Loohuis LM. Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records. medRxiv [Preprint]. 2025 Aug 12:2025.08.07.25333172. doi: 10.1101/2025.08.07.25333172. PMID: 40832382; PMCID: PMC12363723. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.16.0