---
license: apache-2.0
language:
- es
tags:
- falcon-fine-tune
- gguf
- llama.cpp
- lince-zero-quantized
model_name: LINCE-ZERO
base_model: clibrain/lince-zero
inference: false
model_creator: Clibrain
model_type: falcon
pipeline_tag: text-generation
prompt_template: >
  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### Instrucción: {prompt}\n\n### Respuesta:
quantized_by: alvarobartt
---

# Model Card for LINCE-ZERO-7B-GGUF

[LINCE-ZERO](https://huggingface.co/clibrain/lince-zero) is a fine-tuned LLM for instruction following of [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b). The team/org leading the fine-tune is [Clibrain](https://huggingface.co/clibrain), and the datasets used are both [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k) datasets, both translated into Spanish and augmented to 80k examples (as Clibrain claims in its [model card](https://huggingface.co/clibrain/lince-zero#model-card-for-lince-zero)).

This model contains the quantized variants using the GGUF format, introduced by the [llama.cpp](https://github.com/ggerganov/llama.cpp) team.

Some curious may ask, why don't you just use [TheBloke/lince-zero-GGUF](https://huggingface.co/TheBloke/lince-zero-GGUF)? Well, you can use those via `llama.cpp` to run inference over LINCE-ZERO on low resources, but in case you want to use it via [LM Studio](https://lmstudio.ai/) in MacOS you will encounter some issues, as it may only work with `q4_k_s`, `q4_k_m`, `q5_k_s`, and `q5_k_m` quantization formats, and those are not included in TheBloke's.

## Model Details

### Model Description

- **Model type:** Falcon
- **Fine-tuned from model:** [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
- **Created by**: [TIIUAE](https://huggingface.co/tiiuae)
- **Fine-tuned by:** [Clibrain](https://huggingface.co/clibrain)
- **Quantized by:** [alvarobartt](https://huggingface.co/alvarobartt)
- **Language(s) (NLP):** Spanish
- **License:** Apache 2.0 (disclaimer: there may be some licensing mismatch see https://huggingface.co/clibrain/lince-zero/discussions/5)

### Model Sources

- **Repository:** [LINCE-ZERO](https://huggingface.co/clibrain/lince-zero)

### Model Files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [lince-zero-7b-q4_k_s.gguf](https://huggingface.co/alvarobartt/lince-zero-7b-GGUF/blob/main/lince-zero-7b-q4_k_s.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [lince-zero-7b-q4_k_m.gguf](https://huggingface.co/alvarobartt/lince-zero-7b-GGUF/blob/main/lince-zero-7b-q4_k_m.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [lince-zero-7b-q5_k_s.gguf](https://huggingface.co/alvarobartt/lince-zero-7b-GGUF/blob/main/lince-zero-7b-q5_k_s.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [lince-zero-7b-q5_k_m.gguf](https://huggingface.co/alvarobartt/lince-zero-7b-GGUF/blob/main/lince-zero-7b-q5_k_m.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

## Uses

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

All the training details can be found at [Falcon 7B - Training Details](https://huggingface.co/tiiuae/falcon-7b#training-details), and the fine-tuning details at [LINCE-ZERO - Training Details](https://huggingface.co/clibrain/lince-zero#%F0%9F%93%9A-training-details).