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--- |
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base_model: unsloth/mistral-small-24b-instruct-2501-bnb-4bit |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- base_model:adapter:unsloth/mistral-small-24b-instruct-2501-bnb-4bit |
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- lora |
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- sft |
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- transformers |
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- trl |
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- unsloth |
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- psychiatry |
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- spanish |
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- EHR |
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- mental |
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- illness |
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- clinical |
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- symptoms |
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- phenotypes |
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license: apache-2.0 |
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language: |
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- es |
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--- |
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# Model Card for Model ID |
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<!-- Extract psychiatric symptoms from Spanish-language clinical text and EHR notes --> |
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## Model Details |
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### Model Description |
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<!-- 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). |
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--> |
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- **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 |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** Spanish |
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- **License:** Apache 2.0 |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [[More Information Needed]](https://github.com/clarafrydman/LLMs_for_psychiatric_phenotyping/tree/master/) |
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- **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. |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- 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. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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### Framework versions |
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- PEFT 0.16.0 |
