README.md

---
license: cc-by-nc-4.0
datasets:
- bertin-project/alpaca-spanish
language:
- es
inference: false
---


# Model Card for Model ID

This model is the Llama-2-13b-hf fine-tuned with an adapter on the Spanish Alpaca dataset.

## Model Details

### Model Description

This is a Spanish chat model fine-tuned on a Spanish instruction dataset. 

The model expect a prompt containing the instruction, with an option to add an input (see examples below). 



- **Developed by:** 4i Intelligent Insights
- **Model type:** Chat model
- **Language(s) (NLP):** Spanish
- **License:** cc-by-nc-4.0 (inhereted from the alpaca-spanish dataset), 
- **Finetuned from model  :** Llama 2 13B  ([license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/))


## Uses

The model is intended to be used directly without the need of further fine-tuning.


## Bias, Risks, and Limitations

This model inherits the bias, risks, and limitations of its base model, Llama 2, and of the dataset used for fine-tuning. 
Note that the Spanish Alpaca dataset was obtained by translating the original Alpaca dataset. It contains translation errors that may have negatively impacted the fine-tuning of the model. 



## How to Get Started with the Model

Use the code below to get started with the model for inference. The adapter was directly merged into the original Llama 2 model. 


The following code sample uses 4-bit quantization, you may load the model without it if you have enough VRAM. We show results for hyperparameters that we found work well for this set of prompts.

```py
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments, GenerationConfig
import torch
model_name = "4i-ai/Llama-2-13b-alpaca-es"


#Tokenizer

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)

def create_and_prepare_model():
        compute_dtype = getattr(torch, "float16")
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=compute_dtype,
            bnb_4bit_use_double_quant=True,
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_name, quantization_config=bnb_config, device_map={"": 0}
        )
        return model
model = create_and_prepare_model()

def generate(instruction, input=None):
    #Format the prompt to look like the training data
    if input is not None:
        prompt = "### Instruction:\n"+instruction+"\n\n### Input:\n"+input+"\n\n### Response:\n"
    else :
        prompt = "### Instruction:\n"+instruction+"\n\n### Response:\n"


    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()

    generation_output = model.generate(
            input_ids=input_ids,
            repetition_penalty=1.5,
            generation_config=GenerationConfig(temperature=0.1, top_p=0.75, top_k=40, num_beams=20), #hyperparameters for generation
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=150, #maximum tokens generated, increase if you want longer asnwer (up to 2048 - the length of the prompt), generation "looks" slower for longer response

    )
    for seq in generation_output.sequences:
        output = tokenizer.decode(seq, skip_special_tokens=True)
        print(output.split("### Response:")[1].strip())
        
generate("Háblame de la superconductividad.")
print("-----------")
generate("Encuentra la capital de España.")
print("-----------")
generate("Encuentra la capital de Portugal.")
print("-----------")
generate("Organiza los números dados en orden ascendente.", "2, 3, 0, 8, 4, 10")
print("-----------")
generate("Compila una lista de 5 estados de EE. UU. ubicados en el Oeste.")
print("-----------")
generate("Compila una lista de 2 estados de EE. UU. ubicados en el Oeste.")
print("-----------")
generate("Compila una lista de 10 estados de EE. UU. ubicados en el Este.")
print("-----------")
generate("¿Cuál es el color de una fresa?")
print("-----------")
generate("¿Cuál es el color de la siguiente fruta?", "fresa")
print("-----------")

```

Expected output:

```
La superconductividad es un fenómeno físico en el que los materiales pueden conducir corrientes eléctricas a bajas temperaturas sin pérdida de energía debido a la resistencia. Los materiales superconductores son capaces de conducir corrientes eléctricas a temperaturas mucho más bajas que los materiales normales. Esto se debe a que los electrones en los materiales superconductores se comportan de manera cooperativa, lo que les permite conducir corrientes eléctricas sin pérdida de energía. Los materiales superconductores tienen muchas aplicaciones
-----------
La capital de España es Madrid.
-----------
La capital de Portugal es Lisboa.
-----------
0, 2, 3, 4, 8, 10
-----------
1. California
2. Oregón
3. Washington
4. Nevada
5. Arizona
-----------
California y Washington.
-----------
1. Maine
2. Nuevo Hampshire
3. Vermont
4. Massachusetts
5. Rhode Island
6. Connecticut
7. Nueva York
8. Nueva Jersey
9. Pensilvania
10. Delaware
-----------
El color de una fresa es rojo brillante.
-----------
El color de la fresa es rojo.
-----------
```





## Contact Us
[4i.ai](https://4i.ai/) provides natural language processing solutions with dialog, vision and voice capabilities to deliver real-life multimodal human-machine conversations. 
Please contact us at [email protected]