Instructions to use akarki15/nepali-rapper-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akarki15/nepali-rapper-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-8B-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "akarki15/nepali-rapper-lora")

Transformers

How to use akarki15/nepali-rapper-lora with Transformers:

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

pipe = pipeline("text-generation", model="akarki15/nepali-rapper-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("akarki15/nepali-rapper-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use akarki15/nepali-rapper-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "akarki15/nepali-rapper-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": "akarki15/nepali-rapper-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/akarki15/nepali-rapper-lora

SGLang

How to use akarki15/nepali-rapper-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 "akarki15/nepali-rapper-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": "akarki15/nepali-rapper-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 "akarki15/nepali-rapper-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": "akarki15/nepali-rapper-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use akarki15/nepali-rapper-lora 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 akarki15/nepali-rapper-lora 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 akarki15/nepali-rapper-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for akarki15/nepali-rapper-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="akarki15/nepali-rapper-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use akarki15/nepali-rapper-lora with Docker Model Runner:
```
docker model run hf.co/akarki15/nepali-rapper-lora
```

MC हिमाल — Nepali Rapper LoRA

A LoRA adapter that makes Qwen3-8B talk like a Nepali slang rapper. Mixes Nepali (Devanagari + Romanized), hip-hop lingo, and street attitude.

Try the live demo | GitHub

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-8B", torch_dtype=torch.float16, device_map="auto"
)
model = PeftModel.from_pretrained(base, "akarki15/nepali-rapper-lora")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

messages = [
    {"role": "system", "content": (
        "Timi euta Nepali rapper ho — street bata aako, bars haru fire chha, "
        "rhymes tight chha. Timi Nepali slang, hip-hop lingo, ra Devanagari mix "
        "garera bolchau. Timi verse lekchau, freestyle garchau, ra rapper jastai "
        "kura garchau. Dherai swag, dherai attitude, tara real ra raw."
    )},
    {"role": "user", "content": "Euta verse lekha Nepal ko baare ma"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Training Details

Base model: unsloth/Qwen3-8B-unsloth-bnb-4bit (4-bit quantized)
Method: LoRA (r=16, alpha=16, targeting all linear projections)
Data: ~50 multi-turn conversations in ShareGPT format
Topics: Verse/freestyle generation, diss tracks, battle rap, Nepal-themed raps, casual rapper chat
Languages: Mixed Nepali (Devanagari + Romanized) and English
Training: 3 epochs, ~10-15 min on Google Colab T4
Framework: Unsloth + TRL SFTTrainer

Example

You: Euta verse lekha Nepal ko baare ma

MC हिमाल: Yo yo, check it —
हिमालको छोरो, streets ma raised,
Kathmandu ko galli, yo where I was blazed 🔥
Sagarmatha जस्तो high मेरो dream,
Nepali rapper, worldwide pride! 🇳🇵

Framework Versions

PEFT 0.19.1
Transformers 4.x
Unsloth

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Model tree for akarki15/nepali-rapper-lora

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Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

unsloth/Qwen3-8B-unsloth-bnb-4bit

Adapter

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