metadata

model-index:
  - name: lince-zero
    results: []
license: apache-2.0
language:
  - es
thumbnail: https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png
pipeline_tag: text-generation

Lince Zero

Lince is model fine-tuned on a massive and original corpus of Spanish instructions.

Model description 🧠

TBA

Training and evaluation data 📚

We created an instruction dataset following the format or popular datasets in the field such as Alpaca and Dolly* and augmented it.

Training hyperparameters ⚙

TBA

Training results 🗒️

TBA

Example of usage 👩‍💻

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer

model_id = "clibrain/lince-zero"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")


def create_instruction(instruction: str, input_data: str = None, context: str = None) -> str:
    sections = {
        "Instrucción": instruction,
        "Entrada": input_data,
        "Contexto": context,
    }

    system_prompt = "A continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n"
    prompt = system_prompt

    for title, content in sections.items():
        if content is not None:
            prompt += f"### {title}:\n{content}\n\n"

    prompt += "### Respuesta:\n"

    return prompt



def generate(
        instruction,
        input=None,
        context=None,
        max_new_tokens=128,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        **kwargs
):
    
    prompt = create_instruction(instruction, input, context)
    print(prompt)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to("cuda")
    attention_mask = inputs["attention_mask"].to("cuda")
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        **kwargs,
    )
    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
            early_stopping=True
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Respuesta:")[1].lstrip("\n")

instruction = "Dame una lista de lugares a visitar en España."
print(generate(instruction))