Instructions to use HarshikaDewani/sarcastic-bakery-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HarshikaDewani/sarcastic-bakery-lora with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HarshikaDewani/sarcastic-bakery-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HarshikaDewani/sarcastic-bakery-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HarshikaDewani/sarcastic-bakery-lora

SGLang

How to use HarshikaDewani/sarcastic-bakery-lora 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 "HarshikaDewani/sarcastic-bakery-lora" \
    --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": "HarshikaDewani/sarcastic-bakery-lora",
		"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 "HarshikaDewani/sarcastic-bakery-lora" \
        --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": "HarshikaDewani/sarcastic-bakery-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HarshikaDewani/sarcastic-bakery-lora with Docker Model Runner:
```
docker model run hf.co/HarshikaDewani/sarcastic-bakery-lora
```

Sarcastic Bakery Chatbot — LoRA Adapter

This repository contains LoRA adapter weights for a fine-tuned large language model that produces sarcastic yet polite responses, while strictly adhering to a bakery-only assistant role.

The model was fine-tuned as part of an academic project to demonstrate parameter-efficient fine-tuning (PEFT) of LLMs.

Model Details

Model Description

Developed by: Harshika Dewani
Model type: LoRA adapter for causal language model
Language(s): English
Base model: unsloth/llama-3-8b-bnb-4bit
Fine-tuning method: LoRA (Low-Rank Adaptation)
Quantization: 4-bit (QLoRA)
License: Same as base model (LLaMA 3 license)

This model modifies the conversational style and tone of the base model to introduce sarcasm while preserving politeness and role adherence.

Intended Uses

Direct Use

This model is intended to be loaded as a LoRA adapter on top of the base LLaMA-3 model to generate sarcastic bakery-themed responses.

Out-of-Scope Use

Medical, legal, financial, or technical advice
Non-bakery domain conversations
Malicious or harmful content generation

Training Details

Training Data

Custom instruction–response dataset
Domain: Bakery customer support
Focus:
- Sarcastic tone
- Polite refusals
- Strict role adherence
Dataset size: ~100–300 curated examples
Format: Instruction → Response pairs

Training Procedure

Framework: Unsloth + TRL
Trainer: SFTTrainer
Fine-tuning strategy: Supervised Fine-Tuning (SFT) with LoRA
Only LoRA adapter weights were trained; base model weights were frozen.

Training Hyperparameters

Batch size (per device): 2
Gradient accumulation steps: 4
Learning rate: 2e-4
Max training steps: 100
Precision: FP16 / BF16 (hardware dependent)
GPU: Free Google Colab GPU

Evaluation

Evaluation Approach

The model was evaluated qualitatively using:

Before vs after comparison with the base model
Manual inspection of:
- Sarcasm presence
- Politeness
- Role adherence

Example behavior change:

Prompt:

Do you sell pizza?

Base model: Neutral refusal
Fine-tuned model: Sarcastic, bakery-focused refusal

How to Use the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = "unsloth/llama-3-8b-bnb-4bit"
adapter = "your-username/sarcastic-bakery-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    load_in_4bit=True,
    device_map="auto"
)

model.load_adapter(adapter)

prompt = "Do you sell pizza?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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