Instructions to use RichardLu/Llama3_AE_laptop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardLu/Llama3_AE_laptop with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RichardLu/Llama3_AE_laptop")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RichardLu/Llama3_AE_laptop", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RichardLu/Llama3_AE_laptop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardLu/Llama3_AE_laptop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardLu/Llama3_AE_laptop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RichardLu/Llama3_AE_laptop

SGLang

How to use RichardLu/Llama3_AE_laptop with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RichardLu/Llama3_AE_laptop" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardLu/Llama3_AE_laptop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RichardLu/Llama3_AE_laptop" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardLu/Llama3_AE_laptop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use RichardLu/Llama3_AE_laptop with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardLu/Llama3_AE_laptop to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardLu/Llama3_AE_laptop to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardLu/Llama3_AE_laptop to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RichardLu/Llama3_AE_laptop",
    max_seq_length=2048,
)

Docker Model Runner
How to use RichardLu/Llama3_AE_laptop with Docker Model Runner:
```
docker model run hf.co/RichardLu/Llama3_AE_laptop
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Aspect Extraction Model for Laptop Reviews using Llama 3.1 8b

This repository contains a fine-tuned version of unsloth/meta-llama-3.1-8b-instruct-bnb-4bit, trained specifically for Aspect Extraction tasks using the SemEval 2014 Laptop Dataset. The model employs the InstructABSA instruction prompt format combined with the Alpaca prompting structure, optimizing its performance on real-world laptop review analysis.

Model Overview

Base Model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
Fine-tuning Dataset: SemEval 2014 Laptop Dataset
Task: Aspect Extraction
Prompt Format: InstructABSA within Alpaca prompt format

Performance Metrics

Dataset	F1 Score
Train	91.42%
Test	91.89%

Use Cases

This model is well-suited for:

Research purposes: Explore novel methodologies or validate existing theories in ABSA.
Real-world applications: Deriving actionable insights from laptop reviews for businesses, marketers, and product developers.

Inference Speed

Approximate inference time: ~1 second per review (tested on NVIDIA GPUs with 4-bit quantization).

Installation

Install the required dependencies using pip:

import os
if "COLAB_" not in "".join(os.environ.keys()):
    !pip install unsloth
else:
    # Do this only in Colab notebooks! Otherwise, use pip install unsloth
    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton
    !pip install --no-deps cut_cross_entropy unsloth_zoo
    !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer
    !pip install --no-deps unsloth

!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git

Example Usage

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    "RichardLu/Llama3_AE_laptop",
    load_in_4bit=True,
    max_seq_length=2048,
)

FastLanguageModel.for_inference(model)

# Define the instruction for aspect extraction
instructabsa_instruction = """Definition: The output will be the aspects (both implicit and explicit) which have an associated opinion that are extracted from the input text. In cases where there are no aspects the output should be noaspectterm.
Positive example 1-
input: I charge it at night and skip taking the cord with me because of the good battery life.
output: battery life
Positive example 2-
input: I even got my teenage son one, because of the features that it offers, like, iChat, Photobooth, garage band and more!.
output: features, iChat, Photobooth, garage band
Negative example 1-
input: Speaking of the browser, it too has problems.
output: browser
Negative example 2-
input: The keyboard is too slick.
output: keyboard
Neutral example 1-
input: I took it back for an Asus and same thing- blue screen which required me to remove the battery to reset.
output: battery
Neutral example 2-
input: Nightly my computer defrags itself and runs a virus scan.
output: virus scan
Now complete the following example:"""
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""

prompt = alpaca_prompt.format(instructabsa_instruction, "The laptop has great battery life, but the screen resolution is disappointing.", "")

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output_ids = model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(output_text.split("### Response:")[-1].strip())

License

This model is intended for research and educational purposes. Please ensure proper citation if utilized in academic or industry research.

Citation

If you utilize this model in your research, please cite it appropriately and reference this repository.

@misc{yourcitation2024,
  author = {Lu Phone Maw},
  title = {Aspect Extraction Model for Laptop Reviews using Llama 3.1 8b},
  year = {2025},
  publisher = {Lu Phone Maw},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/RichardLu/Llama3_AE_laptop}}
}

For any questions or feedback, please contact the repository maintainer.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for RichardLu/Llama3_AE_laptop

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2750)

this model