Instructions to use Threedotz/llama3.1-8b-qlora-alpaca-indonesian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Threedotz/llama3.1-8b-qlora-alpaca-indonesian with Transformers:

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

pipe = pipeline("text-generation", model="Threedotz/llama3.1-8b-qlora-alpaca-indonesian")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Threedotz/llama3.1-8b-qlora-alpaca-indonesian")
model = AutoModelForCausalLM.from_pretrained("Threedotz/llama3.1-8b-qlora-alpaca-indonesian")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Threedotz/llama3.1-8b-qlora-alpaca-indonesian with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Threedotz/llama3.1-8b-qlora-alpaca-indonesian"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Threedotz/llama3.1-8b-qlora-alpaca-indonesian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Threedotz/llama3.1-8b-qlora-alpaca-indonesian

SGLang

How to use Threedotz/llama3.1-8b-qlora-alpaca-indonesian 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 "Threedotz/llama3.1-8b-qlora-alpaca-indonesian" \
    --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": "Threedotz/llama3.1-8b-qlora-alpaca-indonesian",
		"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 "Threedotz/llama3.1-8b-qlora-alpaca-indonesian" \
        --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": "Threedotz/llama3.1-8b-qlora-alpaca-indonesian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Threedotz/llama3.1-8b-qlora-alpaca-indonesian 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 Threedotz/llama3.1-8b-qlora-alpaca-indonesian 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 Threedotz/llama3.1-8b-qlora-alpaca-indonesian to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Threedotz/llama3.1-8b-qlora-alpaca-indonesian to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Threedotz/llama3.1-8b-qlora-alpaca-indonesian",
    max_seq_length=2048,
)

Docker Model Runner
How to use Threedotz/llama3.1-8b-qlora-alpaca-indonesian with Docker Model Runner:
```
docker model run hf.co/Threedotz/llama3.1-8b-qlora-alpaca-indonesian
```

Model Card for threedotz/llama3.1-8b-qlora-alpaca-indonesian

A fine-tuned Llama 3.1 8B model trained using QLoRA on Indonesian Alpaca instruction dataset.

Model Details

Model Description

This is a fine-tuned version of unsloth/Llama-3.1-8B using QLoRA (Quantized Low-Rank Adaptation) via Unsloth. The model is trained on Ichsan2895/alpaca-gpt4-indonesian dataset containing 49,969 Indonesian instruction-response pairs.

The model uses Llama 3.1 chat template with the system prompt: "Kamu adalah asisten AI yang membantu menjawab pertanyaan pengguna berdasarkan konteks yang diberikan."

Developed by: Threedotz
Model type: Language Model (Causal LM)
Language(s) (NLP): Indonesian (id)
License: llama3.1 license
Finetuned from model: unsloth/Llama-3.1-8B

Model Sources

Repository: https://huggingface.co/threedotz/llama3.1-8b-qlora-alpaca-indonesian
Base Model: https://huggingface.co/unsloth/Llama-3.1-8B
Training Dataset: https://huggingface.co/datasets/Ichsan2895/alpaca-gpt4-indonesian

Uses

Direct Use

Indonesian language tasks:

Question answering in Bahasa Indonesia
Instruction following
Text generation in Indonesian

Out-of-Scope Use

Non-Indonesian language tasks
Medical, legal, or financial advice without human oversight

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

# Load model
model_name = "threedotz/llama3.1-8b-qlora-alpaca-indonesian"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# Prompt
prompt = "Apa itu machine learning?"

# Tokenize & generate
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt=True)

_ = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], 
                   max_new_tokens=256, streamer=text_streamer)

Training Details

Training Data

Dataset: Ichsan2895/alpaca-gpt4-indonesian
Size: 49,969 instruction-response pairs
Language: Indonesian (Bahasa Indonesia)
Format: Alpaca instruction format (input → output)
License: CC-BY-SA-4.0

Training Procedure

Preprocessing

Dataset formatted using Llama 3.1 chat template with system prompt.

Training Hyperparameters

Hyperparameter	Value
Training regime	4-bit QLoRA (bf16/fp16)
Max steps	800
Per device batch size	1
Gradient accumulation steps	4
Total batch size	4
Learning rate	2e-4
LR scheduler	linear
Warmup steps	5
Max sequence length	512
Optimizer	paged_adamw_8bit
Gradient checkpointing	unsloth

QLoRA Configuration

Parameter	Value
LoRA rank (r)	16
LoRA alpha	16
LoRA dropout	0
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Load in 4-bit	True

Model Statistics

Metric	Value
Total parameters	8,072,204,288
Trainable parameters	41,943,040
Trainable %	0.52%

Technical Specifications

Model Architecture

Architecture: Llama 3.1 (Decoder-only Transformer)
Parameters: 8 billion
Quantization: 4-bit (QLoRA)
Max sequence length: 2048

Compute Infrastructure

GPU: Tesla T4 (Kaggle)
Training time: ~1h 17min 11s
Framework: Unsloth + TRL + Transformers

Citation

BibTeX:

@misc{threedotz2024llama31indonesian,
  author = {Threedotz},
  title = {Llama 3.1 8B QLoRA Fine-tuned on Alpaca Indonesian},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/threedotz/llama3.1-8b-qlora-alpaca-indonesian}
}

Model Card Contact

For questions, please contact Threedotz on HuggingFace.

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Safetensors

Model size

8B params

Tensor type

F16

F32