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---
license: apache-2.0
language:
- es
pipeline_tag: text-generation
library_name: transformers
inference: false
---
# Llama-2-13B-ft-instruct-es
[Llama 2 (13B)](https://huggingface.co/meta-llama/Llama-2-13b) fine-tuned on [Clibrain](https://huggingface.co/clibrain)'s Spanish instructions dataset.
## Model Details
Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pre-trained model.
## Example of Usage
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_id = "clibrain/Llama-2-13b-ft-instruct-es"
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 = {
"Instrucci贸n": instruction,
"Entrada": input_data,
"Contexto": context,
}
system_prompt = "A continuaci贸n hay una instrucci贸n que describe una tarea, junto con una entrada que proporciona m谩s 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 Espa帽a."
print(generate(instruction))
```
## Example of Usage with `pipelines`
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "clibrain/Llama-2-13b-ft-instruct-es"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200, device=0)
prompt = """
A continuaci贸n hay una instrucci贸n que describe una tarea. Escriba una respuesta que complete adecuadamente la solicitud.
### Instrucci贸n:
Dame una lista de 5 lugares a visitar en Espa帽a.
### Respuesta:
"""
result = pipe(prompt)
print(result[0]['generated_text'])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_clibrain__Llama-2-13b-ft-instruct-es)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 46.27 |
| ARC (25-shot) | 59.39 |
| HellaSwag (10-shot) | 81.51 |
| MMLU (5-shot) | 54.31 |
| TruthfulQA (0-shot) | 37.81 |
| Winogrande (5-shot) | 75.77 |
| GSM8K (5-shot) | 8.57 |
| DROP (3-shot) | 6.55 |
|