Instructions to use smshahbaj/Rifa-Nano-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smshahbaj/Rifa-Nano-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="smshahbaj/Rifa-Nano-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("smshahbaj/Rifa-Nano-0.5B")
model = AutoModelForCausalLM.from_pretrained("smshahbaj/Rifa-Nano-0.5B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use smshahbaj/Rifa-Nano-0.5B 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 smshahbaj/Rifa-Nano-0.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

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 smshahbaj/Rifa-Nano-0.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

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 smshahbaj/Rifa-Nano-0.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

Use Docker

docker model run hf.co/smshahbaj/Rifa-Nano-0.5B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use smshahbaj/Rifa-Nano-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smshahbaj/Rifa-Nano-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smshahbaj/Rifa-Nano-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/smshahbaj/Rifa-Nano-0.5B:Q4_K_M

SGLang

How to use smshahbaj/Rifa-Nano-0.5B 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 "smshahbaj/Rifa-Nano-0.5B" \
    --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": "smshahbaj/Rifa-Nano-0.5B",
		"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 "smshahbaj/Rifa-Nano-0.5B" \
        --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": "smshahbaj/Rifa-Nano-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use smshahbaj/Rifa-Nano-0.5B with Ollama:
```
ollama run hf.co/smshahbaj/Rifa-Nano-0.5B:Q4_K_M
```
Unsloth Desktop

How to use smshahbaj/Rifa-Nano-0.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "smshahbaj/Rifa-Nano-0.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use smshahbaj/Rifa-Nano-0.5B with Docker Model Runner:
```
docker model run hf.co/smshahbaj/Rifa-Nano-0.5B:Q4_K_M
```

Lemonade

How to use smshahbaj/Rifa-Nano-0.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull smshahbaj/Rifa-Nano-0.5B:Q4_K_M

Run and chat with the model

lemonade run user.Rifa-Nano-0.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use smshahbaj/Rifa-Nano-0.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

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 smshahbaj/Rifa-Nano-0.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use smshahbaj/Rifa-Nano-0.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf smshahbaj/Rifa-Nano-0.5B:Q4_K_M

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 "smshahbaj/Rifa-Nano-0.5B:Q4_K_M" \
  --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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RIFA Nano (0.5B)

The smallest model in the RIFA series
Fine-tuned by SM Shahbaj

About

RIFA Nano is a carefully fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct.
It is designed to be a lightweight, helpful assistant with strong Bangla support and reduced hallucination on unanswerable questions.

Property	Value
Name	RIFA Nano
Parameters	0.5B
Developed by	SM Shahbaj
Series	RIFA (Nano → Flash → Code → Edge → Pro)

What's new in v2

Improved skill mix (Bangla + English + coding + light math + general knowledge)
Dedicated anti-hallucination training ("I don't know" examples)

Downloads

File	Link
Model files (safetensors)	Browse files
`Rifa-Nano-0.5B.F16.gguf`	Download
`Rifa-Nano-0.5B.Q3_K_M.gguf`	Download
`Rifa-Nano-0.5B.Q4_K_M.gguf`	Download
`Rifa-Nano-0.5B.Q5_K_M.gguf`	Download
`Rifa-Nano-0.5B.Q6_K.gguf`	Download
`Rifa-Nano-0.5B.Q8_0.gguf`	Download

Recommended quant: Q5_K_M (best balance of quality & size)

Quick Start

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "smshahbaj/Rifa-Nano-0.5B"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")

messages = [{"role": "user", "content": "তুমি কে?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0], skip_special_tokens=True))

GGUF (LM Studio / Ollama / llama.cpp)

Download any .gguf file from the table above and load it directly.

Limitations

This is a 0.5B model. It will still make mistakes on complex reasoning, very recent events, and obscure facts.
The anti-hallucination training reduces confident wrong answers on clearly unanswerable questions, but it does not add knowledge beyond the model's size.
Always double-check important information (code, numbers, facts).