Instructions to use perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF 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 perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF: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 perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF: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 perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M

Use Docker

docker model run hf.co/perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF with Ollama:
```
ollama run hf.co/perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF with Docker Model Runner:
```
docker model run hf.co/perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M
```

Lemonade

How to use perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

RCRC Hanifa GGUF (Gemma 3 1B)

Quantized GGUF of perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa for llama.cpp and Ollama. Original FP16 weights remain at the source repo.

This is the chatbot for RCRC's Hanifa Standard knowledge base, fine-tuned with RAG-style instruction data across MSA, Najdi, Hijazi, and English.

Files

File	Quant	Size (approx)	Notes
`rcrc-hanifa-gemma3-1b-F16.gguf`	F16	~2.0 GB	Reference precision
`rcrc-hanifa-gemma3-1b-Q8_0.gguf`	Q8_0	~1.0 GB	Near-lossless
`rcrc-hanifa-gemma3-1b-Q5_K_M.gguf`	Q5_K_M	~720 MB	Balanced
`rcrc-hanifa-gemma3-1b-Q4_K_M.gguf`	Q4_K_M	~620 MB	Recommended for laptops

Quick start — llama.cpp

hf download perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF rcrc-hanifa-gemma3-1b-Q4_K_M.gguf --local-dir .
./llama-cli -m rcrc-hanifa-gemma3-1b-Q4_K_M.gguf -cnv

Quick start — Ollama

hf download perfectPresentation/rcrc-chat-v3-gemma-1b-rag-hanifa-GGUF rcrc-hanifa-gemma3-1b-Q4_K_M.gguf Modelfile --local-dir ./rcrc-hanifa
cd rcrc-hanifa
ollama create rcrc-hanifa -f Modelfile
ollama run rcrc-hanifa

Chat template (Gemma 3)

<start_of_turn>user
{user_message}<end_of_turn>
<start_of_turn>model
{response}<end_of_turn>

Downloads last month: 63

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

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