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---
license: openrail
base_model: bertin-project/bertin-gpt-j-6B-alpaca
tags:
- generated_from_trainer
model-index:
- name: bertin-gpt-clara-med
results: []
datasets:
- CLARA-MeD/CLARA-MeD
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertin-gpt-clara-med
This model is a fine-tuned version of [bertin-project/bertin-gpt-j-6B-alpaca](https://huggingface.co/bertin-project/bertin-gpt-j-6B-alpaca) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6110
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
from peft import PeftConfig, PeftModel
import torch
from accelerate import init_empty_weights, load_checkpoint_and_dispatch, infer_auto_device_map
repo_name = "CLARA-MeD/bertin-gpt"
config = PeftConfig.from_pretrained(repo_name)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,torch_dtype=torch.float16,
device_map="auto")
model = PeftModel.from_pretrained(model, repo_name)
```
For generation, we can use the model's `.generate()` method. Remember that the prompt needs a **Spanish** template:
```python
# Generate responses
def generate(input):
prompt = f"""A continuaci贸n hay una instrucci贸n que describe una tarea, junto con una entrada que proporciona m谩s contexto. Escribe una respuesta que complete adecuadamente lo que se pide.
### Instrucci贸n:
Simplifica la siguiente frase
### Entrada:
{input}
### Respuesta:"""
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
generation_config=GenerationConfig(temperature=0.2, top_p=0.75, num_beams=4),
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=256
)
for seq in generation_output.sequences:
output = tokenizer.decode(seq, skip_special_tokens=True)
print(output.split("### Respuesta:")[-1].strip())
generate("Acromegalia")
# La acromegalia es un trastorno causado por un exceso de hormona del crecimiento en el cuerpo.
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5564 | 0.38 | 50 | 0.7804 |
| 0.3879 | 0.75 | 100 | 0.6551 |
| 0.3609 | 1.13 | 150 | 0.6327 |
| 0.3615 | 1.5 | 200 | 0.6179 |
| 0.3371 | 1.88 | 250 | 0.6135 |
| 0.3242 | 2.25 | 300 | 0.6110 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3 |