README.md

---
library_name: transformers
datasets:
- sofya-ai/soap_multilanguage_filtered
- sofya-ai/asclepius_multilanguage_filtered
language:
- pt
- es
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---

# Model Card for Sofya-LLaMA-3.1-8B

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## Model Details

### Model Description

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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:** Sofya AI
- **Funded by [optional]:** Sofya AI
- **Shared by [optional]:** Sofya AI
- **Model type:** Large Language Model (LLM) for clinical workflows.
- **Language(s) (NLP):** Portuguese, English, Spanish.
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** `meta-llama/llama-3.1-8b`

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses
This model is designed for processing and generating structured clinical notes, particularly in multilingual formats (Portuguese, English, Spanish).

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### Downstream Use [optional]

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- Fine-tuning for specific clinical tasks.
- Use in health-related applications requiring structured input-output for SOAP (Subjective, Objective, Assessment, Plan) workflows.

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### 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.

[More Information Needed]

## **Training Details**

### **Training Data**
- **Datasets Used:**
  - **SOAP Multilanguage Filtered** (`sofya-ai/soap_multilanguage_filtered`): Structured clinical notes in SOAP format.
  - **Asclepius Multilanguage Filtered** (`sofya-ai/asclepius_multilanguage_filtered`): Annotations for clinical workflows.
  - **Data Characteristics:**
    - SOAP: Full dataset used.
    - Asclepius: Randomly selected **1250 samples** for balanced training.
  - **Languages:** Portuguese, English, Spanish.

---

### **Training Procedure**

#### **Preprocessing [optional]**
- Tokenized inputs to a maximum sequence length of **4096** tokens.
- Interleaved datasets using Hugging Face's `interleave_datasets`.

#### **Training Hyperparameters**
| **Argument**                 | **Value**                |
|-------------------------------|--------------------------|
| `output_dir`                  | `./results`             |
| `max_seq_length`              | `4096`                  |
| `learning_rate`               | `3e-5`                  |
| `per_device_train_batch_size` | `2`                     |
| `per_device_eval_batch_size`  | `2`                     |
| `num_train_epochs`            | `2`                     |
| `weight_decay`                | `0.01`                  |
| `optim`                       | `paged_adamw_32bit`     |
| `warmup_steps`                | `250`                   |
| `lr_scheduler_type`           | `cosine`                |
| `eval_strategy`               | `steps`                 |
| `save_strategy`               | `steps`                 |
| `save_steps`                  | `1000`                  |
| `eval_steps`                  | `500`                   |
| `save_total_limit`            | `3`                     |
| `logging_dir`                 | `./logs`                |
| `logging_steps`               | `10`                    |
| `bf16`                        | `True`                  |
| `max_grad_norm`               | `1.0`                   |

---

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

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[More Information Needed]

## 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]
- **Compute Region:** [More Information Needed]
- **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]

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**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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