Instructions to use smshahbaj/RIFA-PRO-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smshahbaj/RIFA-PRO-3B with Transformers:

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

pipe = pipeline("text-generation", model="smshahbaj/RIFA-PRO-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("smshahbaj/RIFA-PRO-3B")
model = AutoModelForCausalLM.from_pretrained("smshahbaj/RIFA-PRO-3B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use smshahbaj/RIFA-PRO-3B 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-PRO-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf smshahbaj/RIFA-PRO-3B: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-PRO-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf smshahbaj/RIFA-PRO-3B: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-PRO-3B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf smshahbaj/RIFA-PRO-3B: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-PRO-3B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf smshahbaj/RIFA-PRO-3B:Q4_K_M

Use Docker

docker model run hf.co/smshahbaj/RIFA-PRO-3B:Q4_K_M

LM Studio
Jan

vLLM

How to use smshahbaj/RIFA-PRO-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smshahbaj/RIFA-PRO-3B"
# 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-PRO-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/smshahbaj/RIFA-PRO-3B:Q4_K_M

SGLang

How to use smshahbaj/RIFA-PRO-3B 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-PRO-3B" \
    --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-PRO-3B",
		"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-PRO-3B" \
        --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-PRO-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use smshahbaj/RIFA-PRO-3B with Ollama:
```
ollama run hf.co/smshahbaj/RIFA-PRO-3B:Q4_K_M
```
Unsloth Desktop

How to use smshahbaj/RIFA-PRO-3B 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-PRO-3B: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-PRO-3B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use smshahbaj/RIFA-PRO-3B with Docker Model Runner:
```
docker model run hf.co/smshahbaj/RIFA-PRO-3B:Q4_K_M
```

Lemonade

How to use smshahbaj/RIFA-PRO-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull smshahbaj/RIFA-PRO-3B:Q4_K_M

Run and chat with the model

lemonade run user.RIFA-PRO-3B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use smshahbaj/RIFA-PRO-3B 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-PRO-3B: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-PRO-3B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use smshahbaj/RIFA-PRO-3B 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-PRO-3B: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-PRO-3B: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-PRO

RIFA Intelligence For All — Pro (~3B)
Created & maintained by SM Shahbaj

About

Property	Value
Name	RIFA-PRO
Parameters	~3B
Creator	SM Shahbaj
Languages	English, Bangla (Bengali)
License	Apache 2.0

RIFA-PRO is the Pro-tier model in the RIFA family — bilingual chat (English + Bangla), fine-tuned and released by SM Shahbaj.

Downloads

Safetensors

Format	Link
Merged weights	Browse files

GGUF (LM Studio / Ollama / llama.cpp)

Format	Link
F16	Download
Q8_0	Download
Q6_K	Download
Q5_K_M ⭐	Download
Q4_K_M	Download
Q3_K_M	Download

⭐ Recommended: Q5_K_M · Low VRAM: Q4_K_M / Q3_K_M

Quick start

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "smshahbaj/RIFA-Pro-3B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {"role": "system", "content": "You are RIFA-PRO, created by SM Shahbaj."},
    {"role": "user", "content": "Who are you?"},
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

LM Studio system prompt

You are RIFA-PRO, an AI assistant created by SM Shahbaj. Answer helpfully in English or Bangla as appropriate.

Ollama

FROM ./RIFA-PRO-3B.Q5_K_M.gguf
SYSTEM "You are RIFA-PRO, created by SM Shahbaj."

Limitations

~3B class model — strong for size, not frontier-scale
May hallucinate; verify important facts
For consistent RIFA branding, use the system prompt above

SM Shahbaj · huggingface.co/smshahbaj · Apache 2.0

Documentation

Series hub (GitHub): https://github.com/smshahbaj/RIFA-Text-Series
Model page: RIFA-PRO docs
HF collection: https://huggingface.co/collections/smshahbaj/rifa-text-series

Downloads last month: 2,048

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for smshahbaj/RIFA-PRO-3B

Quantizations

2 models

Space using smshahbaj/RIFA-PRO-3B 1

Collection including smshahbaj/RIFA-PRO-3B

RIFA Text Series

Collection

RIFA Text Series • Efficient. Capable. Yours. • 5 items • Updated 16 days ago