---
license: apache-2.0
datasets:
- yahma/alpaca-cleaned
metrics:
- accuracy
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
---
## Usage
Support for this model will be added in the upcoming transformers release. In the meantime, please install the library from source:
~~~
pip install transformers
~~~
We can now run inference on this model:
~~~
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
model_path = "YaoLuzjut/Llama-3.1-6.3B-It-Alpaca"
tokenizer = AutoTokenizer.from_pretrained(model_path)

device = 'cuda'
dtype = torch.bfloat16
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)

# Prepare the input text
prompt = 'Complete the paragraph: our solar system is'
inputs = tokenizer.encode(prompt, return_tensors='pt').to(model.device)

# Generate the output
outputs = model.generate(inputs, max_length=20)

# Decode and print the output
output_text = tokenizer.decode(outputs[0])
print(output_text)
~~~

## Evaluation Results
Zero-shot performance. Evaluated using select datasets from the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/main) with additions:
| PIQA     | HellaSwag | OpenbookQA |  ARC-e | ARC-c | MMLU | CMMLU | WinoGrande |
| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
|   0.7383±0.0103    |    0.5323±0.0050    |    0.3080±0.0207    |    0.7260±0.0092    |    0.4684±0.0146    |    0.6567±0.0038    |    0.5515±0.0045    |    0.6646±0.0133    |