bertin-gpt

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bertin-gpt-clara-med

This model is a fine-tuned version of bertin-project/bertin-gpt-j-6B-alpaca on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6110

Usage

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:

# Generate responses
def generate(input):
    prompt = f"""A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escribe una respuesta que complete adecuadamente lo que se pide.

### Instrucción:
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

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.0+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3