metadata
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/partial-layer_fine-tuning_Llama-3.1-8B-Instruct"
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 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 |