Instructions to use zabibutechnika/Fikra-Model-2-4B-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 zabibutechnika/Fikra-Model-2-4B-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 zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zabibutechnika/Fikra-Model-2-4B-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 zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use zabibutechnika/Fikra-Model-2-4B-GGUF with Ollama:
```
ollama run hf.co/zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use zabibutechnika/Fikra-Model-2-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zabibutechnika/Fikra-Model-2-4B-GGUF: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": "zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use zabibutechnika/Fikra-Model-2-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M
```

Lemonade

How to use zabibutechnika/Fikra-Model-2-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fikra-Model-2-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use zabibutechnika/Fikra-Model-2-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zabibutechnika/Fikra-Model-2-4B-GGUF: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 zabibutechnika/Fikra-Model-2-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use zabibutechnika/Fikra-Model-2-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zabibutechnika/Fikra-Model-2-4B-GGUF: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 "zabibutechnika/Fikra-Model-2-4B-GGUF: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"

Zabibu Fikra Model 2 - 4B — GGUF

GGUF distribution of zabibutechnika/Fikra-Model-2-4B — a Fikra-specialized fine-tune of Qwen/Qwen3.5-4B.

Files

file	quant	size	sha256
`Fikra-Model-2-4B-Q4_K_M.gguf`	Q4_K_M	2.71 GB	`805d386f01e363cc555e78a750fe4c5f45eb11478348c9d1df8d3834b0d0ef23`
`Fikra-Model-2-4B-f16.gguf`	F16	8.42 GB	`bdd3e4597838591fe6c4364dc65e811dce0ca7ac3358592bb83e9496ecac37e8`

Q4_K_M — recommended. Best size/quality tradeoff, runs on CPU. F16 — full-precision reference; numerically equivalent to the fp16 safetensors.

Run

llama-cli    -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Fikra-Model-2-4B-Q4_K_M.gguf -p "Who are you?" --temp 0
llama-server -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Fikra-Model-2-4B-Q4_K_M.gguf --port 8080 -c 8192 -ngl 99

Caveats

MMLU inherited from Qwen3.5-4B, not created by Fikra training. See the fp16 model card for the full evaluation.
Q4_K_M not re-evaluated. Expect 1–3 pt loss vs fp16 on short-form evals. F16 GGUF inherits fp16 numbers exactly.
<think> reasoning-tag leakage in raw output — strip <think>...</think> in post-processing.
Not red-teamed.

Downloads last month: 92

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

16-bit

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Model tree for zabibutechnika/Fikra-Model-2-4B-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

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zabibutechnika/Fikra-Model-2-4B

Quantized

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