README.md

metadata

model-index:
  - name: lince-zero
    results: []
license: apache-2.0
language:
  - es
thumbnail: https://huggingface.co/clibrain/lince-zero/resolve/main/LINCE-CLIBRAIN-HD.jpg
pipeline_tag: text-generation
datasets:
  - tatsu-lab/alpaca
  - databricks/databricks-dolly-15k
library_name: transformers
inference: false

Model Card for LINCE-ZERO

LINCE-ZERO (Llm for Instructions from Natural Corpus en Español) is a SOTA Spanish instruction-tuned LLM 🔥

Developed by Clibrain, it is a causal decoder-only model with 7B parameters. LINCE-ZERO is based on Falcon-7B and has been fine-tuned using a combination of the Alpaca and Dolly datasets, both translated into Spanish and augmented to 80k examples.

The model is released under the Apache 2.0 license.

Table of Contents

• Model Details
  • Model Description
• Uses
  • Direct Use
  • Downstream Use
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Environmental Impact
• Technical Specifications
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Citation
• Contact
• How to Get Started with the Model

🐯 Model Details

Model Description

LINCE-ZERO (Llm for Instructions from Natural Corpus en Español) is a state-of-the-art Spanish instruction-tuned large language model. Developed by Clibrain, it is a causal decoder-only model with 7B parameters. LINCE-ZERO is based on Falcon-7B and has been fine-tuned using an 80k examples augmented combination of the Alpaca and Dolly datasets, both translated into Spanish.

• Developed by: Clibrain
• Model type: Language model, instruction model, causal decoder-only
• Language(s) (NLP): es
• License: apache-2.0
• Parent Model: https://huggingface.co/tiiuae/falcon-7b

Model Sources

• Paper: Coming soon! ✨
• Demo: Coming soon! ✨

💡 Uses

Direct Use

LINCE-ZERO's fine-tuning on an instructions dataset enables it to follow natural language instructions in Spanish. The direct use cases include virtual assistants and content generation.

Please note that running inference with LINCE-ZERO efficiently requires a minimum of XGB of memory.

Downstream Use

LINCE-ZERO is an instruct model, it’s primarily intended for direct use and may not be ideal for further fine-tuning. It serves as a general model suitable for a wide range of applications. However, for specific use cases within certain domains, fine-tuning with domain-specific data may improve LINCE-ZERO's performance.

Out-of-Scope Use

LINCE-ZERO should not be used for production purposes without conducting a thorough assessment of risks and mitigation strategies.

⚠️ Bias, Risks, and Limitations

LINCE-ZERO has limitations associated with both the underlying language model and the instruction tuning data. It is crucial to acknowledge that predictions generated by the model may inadvertently exhibit common deficiencies of language models, including hallucination, toxicity, and perpetuate harmful stereotypes across protected classes, identity characteristics, and sensitive, social, and occupational groups.

Since the model has been fine-tuned on translated versions of the Alpaca and Dolly datasets, it has potentially inherited certain limitations and biases:

• Alpaca: The Alpaca dataset is generated by a language model (text-davinci-003) and inevitably contains some errors or biases inherent in that model. As the authors report, hallucination seems to be a common failure mode for Alpaca, even compared to text-davinci-003.
• Dolly: The Dolly dataset incorporates information from Wikipedia, which is a crowdsourced corpus. Therefore, the dataset's contents may reflect the biases, factual errors, and topical focus present in Wikipedia. Additionally, annotators involved in the dataset creation may not be native English speakers, and their demographics and subject matter may reflect the makeup of Databricks employees.

Recommendations

Please, when utilizing LINCE-ZERO, exercise caution and critically assess the output to mitigate the potential impact of biased or inaccurate information.

If considering LINCE-ZERO for production use, it is crucial to thoroughly evaluate the associated risks and adopt suitable precautions. Conduct a comprehensive assessment to address any potential biases and ensure compliance with legal and ethical standards.

📚 Training Details

Training Data

LINCE-ZERO is based on Falcon-7B and has been fine-tuned using an augmented combination of the Alpaca and Dolly datasets, both translated into Spanish.

Alpaca is a 24.2 MB dataset of 52,002 instructions and demonstrations in English. It was generated by OpenAI's text-davinci-003 engine using the data generation pipeline from the Self-Instruct framework with some modifications. For further details, refer to Alpaca's Data Card.

Dolly is a 13.1 MB dataset of 15,011 instruction-following records in American English. It was generated by thousands of Databricks employees, who were requested to provide reference texts copied from Wikipedia for specific categories. To learn more, consult Dolly’s Data Card.

After combining both translations, the dataset was augmented to a total of 80k examples.

Training Procedure

For detailed information about the model architecture and compute infrastructure, please refer to the Technical Specifications section.

Preprocessing

To prepare the training data, both the Alpaca and Dolly datasets, originally in English, were translated into Spanish using …

The data was tokenized using LINCE-ZERO’s tokenizer, which is based on the Falcon-7B/40B tokenizer.

Training Hyperparameters

More information needed

Speeds, Sizes, Times

More information needed (throughput, start/end time, checkpoint size if relevant, etc.)

✅ Evaluation

Testing Data, Factors & Metrics

Testing Data

The model has been tested on a X% of the augmented combination of Alpaca (24.2 MB) and Dolly (13.1 MB) translated into Spanish.

Metrics

Since LINCE-ZERO is an instruction model, the metrics used to evaluate it are:

• X:

Results

Paper coming soon! Meanwhile, check the OpenLLM Leaderboard.

⚙️ Technical Specifications

Model Architecture and Objective

LINCE-ZERO is a causal decoder-only model trained on a causal language modeling task. Its objective is to predict the next token in a sequence based on the context provided.

The architecture of LINCE-ZERO is based on Falcon-7B, which itself is adapted from the GPT-3 paper (Brown et al., 2020) with the following modifications:

• Positional embeddings: rotary (Su et al., 2021);
• Attention: multiquery (Shazeer et al., 2019) and FlashAttention (Dao et al., 2022);
• Decoder-block: parallel attention/MLP with a single-layer norm.

Compute Infrastructure

Hardware

LINCE-ZERO was trained on AWS SageMaker, on ... GPUs in ... instances.

Software

More information needed

🌳 Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• 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

📝 Citation

There is a paper coming soon! Meanwhile, when using LINCE-ZERO please use the following information to cite:

@article{lince-zero,
  title={{LINCE-ZERO}: Llm for Instructions from Natural Corpus en Español},
  author={},
  year={2023}
}

📧 Contact

contacto@clibrain.com

🔥 How to Get Started with LINCE-ZERO

Use the code below to get started with LINCE-ZERO!

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer

model_id = "clibrain/lince-zero"

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

def create_instruction(instruction, input_data=None, context=None):
    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))