Text Generation
Safetensors
GGUF
Nepali
English
qwen3
nepali
rapper
chatbot
lora
merged
conversational
Instructions to use akarki15/nepali-rapper-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use akarki15/nepali-rapper-merged with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="akarki15/nepali-rapper-merged", filename="nepali-rapper-q4km.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use akarki15/nepali-rapper-merged with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf akarki15/nepali-rapper-merged # Run inference directly in the terminal: llama cli -hf akarki15/nepali-rapper-merged
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf akarki15/nepali-rapper-merged # Run inference directly in the terminal: llama cli -hf akarki15/nepali-rapper-merged
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf akarki15/nepali-rapper-merged # Run inference directly in the terminal: ./llama-cli -hf akarki15/nepali-rapper-merged
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf akarki15/nepali-rapper-merged # Run inference directly in the terminal: ./build/bin/llama-cli -hf akarki15/nepali-rapper-merged
Use Docker
docker model run hf.co/akarki15/nepali-rapper-merged
- LM Studio
- Jan
- vLLM
How to use akarki15/nepali-rapper-merged with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "akarki15/nepali-rapper-merged" # 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-merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/akarki15/nepali-rapper-merged
- Ollama
How to use akarki15/nepali-rapper-merged with Ollama:
ollama run hf.co/akarki15/nepali-rapper-merged
- Unsloth Studio
How to use akarki15/nepali-rapper-merged 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-merged 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-merged 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-merged to start chatting
- Pi
How to use akarki15/nepali-rapper-merged with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf akarki15/nepali-rapper-merged
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "akarki15/nepali-rapper-merged" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use akarki15/nepali-rapper-merged with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf akarki15/nepali-rapper-merged
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default akarki15/nepali-rapper-merged
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use akarki15/nepali-rapper-merged with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf akarki15/nepali-rapper-merged
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "akarki15/nepali-rapper-merged" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use akarki15/nepali-rapper-merged with Docker Model Runner:
docker model run hf.co/akarki15/nepali-rapper-merged
- Lemonade
How to use akarki15/nepali-rapper-merged with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull akarki15/nepali-rapper-merged
Run and chat with the model
lemonade run user.nepali-rapper-merged-{{QUANT_TAG}}List all available models
lemonade list
MC हिमाल — Nepali Rapper (Merged)
Qwen3-8B with the nepali-rapper-lora adapter merged into the base weights. Ready to use without PEFT — just load and generate.
Try the live demo | GitHub | Merge script
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"akarki15/nepali-rapper-merged", dtype=torch.float16, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("akarki15/nepali-rapper-merged")
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))
How This Was Made
- Fine-tuned
Qwen/Qwen3-8B(4-bit) with LoRA (r=16, alpha=16) on ~50 Nepali rapper conversations - Uploaded LoRA adapter →
akarki15/nepali-rapper-lora - Merged LoRA weights into fp16 base model using
merge_and_push.py
Training Details
- Base model: Qwen/Qwen3-8B
- Method: LoRA (r=16, alpha=16, 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! 🇳🇵
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