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---
language:
- es
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
inference: false
model-index:
- name: Llama-2-ft-instruct-es
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 22.7
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=clibrain/Llama-2-ft-instruct-es
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 25.04
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=clibrain/Llama-2-ft-instruct-es
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 23.12
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=clibrain/Llama-2-ft-instruct-es
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 0.0
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=clibrain/Llama-2-ft-instruct-es
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.57
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=clibrain/Llama-2-ft-instruct-es
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=clibrain/Llama-2-ft-instruct-es
name: Open LLM Leaderboard
---
# Llama-2-ft-instruct-es
# 鈿狅笍 Please go to [clibrain/Llama-2-7b-ft-instruct-es](https://huggingface.co/clibrain/Llama-2-7b-ft-instruct-es) for the fixed and updated version.
[Llama 2 (7B)](https://huggingface.co/meta-llama/Llama-2-7b) fine-tuned on [Clibrain](https://huggingface.co/clibrain)'s Spanish instructions dataset.
## Model Details
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model. Links to other models can be found in the index at the bottom.
## Example of Usage
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, GenerationConfig
model_id = "clibrain/Llama-2-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))
```
# [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-ft-instruct-es)
| Metric |Value|
|---------------------------------|----:|
|Avg. |20.07|
|AI2 Reasoning Challenge (25-Shot)|22.70|
|HellaSwag (10-Shot) |25.04|
|MMLU (5-Shot) |23.12|
|TruthfulQA (0-shot) | 0.00|
|Winogrande (5-shot) |49.57|
|GSM8k (5-shot) | 0.00|
|