Instructions to use Tammy7777777/kpop-llama2-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tammy7777777/kpop-llama2-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Tammy7777777/kpop-llama2-finetuned")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tammy7777777/kpop-llama2-finetuned", dtype="auto")

PEFT
How to use Tammy7777777/kpop-llama2-finetuned with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tammy7777777/kpop-llama2-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tammy7777777/kpop-llama2-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tammy7777777/kpop-llama2-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Tammy7777777/kpop-llama2-finetuned

SGLang

How to use Tammy7777777/kpop-llama2-finetuned 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 "Tammy7777777/kpop-llama2-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tammy7777777/kpop-llama2-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Tammy7777777/kpop-llama2-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tammy7777777/kpop-llama2-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Tammy7777777/kpop-llama2-finetuned with Docker Model Runner:
```
docker model run hf.co/Tammy7777777/kpop-llama2-finetuned
```

🎤 LLaMA2 K-Pop Q&A Model (Fine-tuned)

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf using PEFT (LoRA) on a custom 20-example K-Pop Q&A dataset.

✨ Overview

Task: Text generation (K-Pop Question Answering)
Dataset: Manually written 20 K-Pop themed examples
Training Setup: LoRA-based fine-tuning on Google Colab Free Tier (15 GB GPU)
Use Case: Lightweight model for educational/demo use related to K-Pop fan Q&A

📚 Dataset Description

Each example is structured as: Example:

Question: Who is the leader of BTS?
Context: BTS is a popular South Korean boy band formed in 2013.
Answer: RM is the leader of BTS.

🧪 Training Details

Parameter	Value
Base model	LLaMA2 7B HF
Finetuning method	PEFT (LoRA)
Epochs	1
Batch Size	1
Max Length	512 tokens
Optimizer	AdamW
Learning Rate	2e-4
GPU Used	Free Google Colab
Layers Updated	Only 1 (rest frozen)
Quantization	4-bit (bitsandbytes)

🧾 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model = AutoModelForCausalLM.from_pretrained("Tammy7777777/kpop-llama2-finetuned", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Tammy7777777/kpop-llama2-finetuned")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = "Question: Who is the leader of BTS?\nContext: BTS is a South Korean boy band formed in 2013.\nAnswer:"
print(pipe(prompt, max_new_tokens=64)[0]['generated_text'])

📈 Evaluation

Base model output: Often generic or unrelated

Fine-tuned model: More aligned, provides specific K-Pop answers

Method: Manual comparison of predictions before & after fine-tuning

📦 Model Architecture

Based on meta-llama/Llama-2-7b-hf

Only one transformer layer fine-tuned using LoRA

Efficient for few-shot adaptation

⚠️ Limitations

Trained on a very small (20-example) dataset

May hallucinate or overfit on known examples

Not suitable for production use or factually sensitive topics

👤Author

Tamanna Sheikh (@Tammy7777777)

📝 License

This model is released under the MIT license for research and educational use.

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