YaoLuzjut/Llama-3.1-6.3B-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