Instructions to use dtp-fine-tuning/multi-turn_chatbot_diploy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use dtp-fine-tuning/multi-turn_chatbot_diploy with PEFT:
```
Task type is invalid.
```

How to use dtp-fine-tuning/multi-turn_chatbot_diploy with Transformers:

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

pipe = pipeline("text-generation", model="dtp-fine-tuning/multi-turn_chatbot_diploy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dtp-fine-tuning/multi-turn_chatbot_diploy")
model = AutoModelForCausalLM.from_pretrained("dtp-fine-tuning/multi-turn_chatbot_diploy")
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

vLLM

How to use dtp-fine-tuning/multi-turn_chatbot_diploy with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dtp-fine-tuning/multi-turn_chatbot_diploy

SGLang

How to use dtp-fine-tuning/multi-turn_chatbot_diploy 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 "dtp-fine-tuning/multi-turn_chatbot_diploy" \
    --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": "dtp-fine-tuning/multi-turn_chatbot_diploy",
		"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 "dtp-fine-tuning/multi-turn_chatbot_diploy" \
        --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": "dtp-fine-tuning/multi-turn_chatbot_diploy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use dtp-fine-tuning/multi-turn_chatbot_diploy 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 dtp-fine-tuning/multi-turn_chatbot_diploy 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 dtp-fine-tuning/multi-turn_chatbot_diploy to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dtp-fine-tuning/multi-turn_chatbot_diploy to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="dtp-fine-tuning/multi-turn_chatbot_diploy",
    max_seq_length=2048,
)

Docker Model Runner
How to use dtp-fine-tuning/multi-turn_chatbot_diploy with Docker Model Runner:
```
docker model run hf.co/dtp-fine-tuning/multi-turn_chatbot_diploy
```

Model Card for SFT-Bakti-8B-Base-MultiTurn-Chatbot

Model Details

Model Description

This model is a fine-tuned version of [aitfindonesia/Bakti-8B-Base] designed specifically for multi-turn conversational capabilities in the Indonesian language. It was trained using the Unsloth library for faster and memory-efficient training, utilizing LoRA (Low-Rank Adaptation).

The model is optimized to handle context retention across multiple turns of conversation, making it suitable for interview simulations, customer support, and general-purpose Indonesian assistants.

Developed by: DTP Fine Tuning Team
Model type: Causal Language Model (Fine-tuned Qwen2/3 architecture)
Language(s) (NLP): Indonesian
License: Apache 2.0
Finetuned from model: aitfindonesia/Bakti-8B-Base

Uses

Direct Use

The model is designed for:

Multi-turn chat interactions in Indonesian.
Question Answering (QA) requiring context from previous turns.
Roleplay interactions (e.g., interview scenarios).

Out-of-Scope Use

The model should not be used for generating factually accurate data without RAG (Retrieval Augmented Generation) as hallucinations are possible.
Not intended for code generation tasks.

Training Details

Training Data

Dataset: dtp-fine-tuning/dtp-multiturn-interview-valid-15k

Split: Train (90%) / Test (10%)
Format: Multi-turn conversation format.
Max Length: 2048 tokens

Training Procedure

The model was fine-tuned using Unsloth on a single NVIDIA A100 (80GB) GPU. It utilizes 4-bit quantization (NF4) to reduce memory usage while maintaining performance via QLoRA.

Training Hyperparameters

Training regime: QLoRA (4-bit quantization with FP16 precision)
Optimizer: AdamW 8-bit
Learning Rate: $2 \times 10^{-5}$
Scheduler: Linear with 5% warmup
Batch Size: 8 per device (Gradient Accumulation: 4)
Epochs: 2
LoRA Config:
- Rank ($r$): 16
- Alpha ($\alpha$): 32
- Dropout: 0.05
- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Hardware

GPU: NVIDIA A100 80GB PCIe
VRAM Usage: Peak allocation approx. 19GB (23% utilization) due to 4-bit loading.

Evaluation

Results

The model demonstrates strong convergence on the multi-turn dataset.

Final Train Loss: $\approx 0.42$
Final Eval Loss: $\approx 0.41$

Note: The model outperforms the standard Qwen3-8B baseline on this specific Indonesian dataset, achieving lower loss values faster.

Environmental Impact

Hardware Type: NVIDIA A100 80GB
Compute Region: asia-east1
Carbon Emitted: 0.31

Framework Versions

Unsloth
PEFT
Transformers
TRL

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for dtp-fine-tuning/multi-turn_chatbot_diploy

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Adapter

aitf-komdigi/KomdigiUB-8B-Base

Adapter

(11)

this model