--- 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 library_name: transformers inference: false --- **LINCE-ZERO** (Llm for Instructions from Natural Corpus en Español) is a SOTA Spanish instruction-tuned LLM 🔥 Developed by [Clibrain](https://www.clibrain.com/), it is a causal decoder-only model with 7B parameters. LINCE-ZERO is based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and has been fine-tuned using an 80k examples proprietary dataset inspired in famous instruction datasets such as Alpaca and Dolly. The model is released under the Apache 2.0 license. If you want to test the robust 40B parameters version called **LINCE**, you can request access at [lince@clibrain.com](mailto:lince@clibrain.com). Be one of the first to discover the possibilities of LINCE!
lince logo

# Table of Contents - [Model Details](#model-details) - [Model Description](#model-description) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use](#downstream-use) - [Out-of-Scope Use](#out-of-scope-use) - [Bias, Risks, and Limitations](#bias-risks-and-limitations) - [Recommendations](#recommendations) - [Training Details](#training-details) - [Training Data](#training-data) - [Evaluation](#evaluation) - [Results](#results) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Model Architecture and Objective](#model-architecture-and-objective) - [Compute Infrastructure](#compute-infrastructure) - [Hardware](#hardware) - [Software](#software) - [How to Get Started with the Model](#how-to-get-started-with-the-model) - [Citation](#citation) - [Contact](#contact) # 🐯 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](https://www.clibrain.com/), it is a causal decoder-only model with 7B parameters. LINCE-ZERO is based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and has been fine-tuned using an 80k examples proprietary dataset. - **Developed by:** [Clibrain](https://www.clibrain.com/) - **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. ## 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. ## 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](https://huggingface.co/tiiuae/falcon-7b)** and has been fine-tuned using an 80k examples proprietary dataset inspired in famous instruction datasets such as Alpaca and Dolly. # ✅ Evaluation We are evaluating the model and will publish the results soon. ### Results Paper coming soon! Meanwhile, check the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_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 using a GPU A100 with 40 GB during 8h. ### Software We used the following libraries: - transformers - accelerate - peft - bitsandbytes - einops # 🌳 Environmental Impact 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:** 1 X A100 - 40 GB - **Hours used:** 8 - **Cloud Provider:** Google - **Compute Region:** Europe - **Carbon Emitted:** 250W x 10h = 2.5 kWh x 0.57 kg eq. CO2/kWh = 1.42 kg eq. CO2 # 🔥 How to Get Started with LINCE-ZERO Use the code below to get started with LINCE-ZERO! ```py import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer model_id = "clibrain/lince-zero" model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).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.replace("### Respuesta:\n", "") 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)) ``` # 📝 Citation There is a paper coming soon! Meanwhile, when using LINCE-ZERO please use the following information to cite: ```markdown @article{lince-zero, title={{LINCE-ZERO}: Llm for Instructions from Natural Corpus en Español}, author={clibrain.com}, year={2023} } ``` # 📧 Contact [lince@clibrain.com](mailto:lince@clibrain.com)