Instructions to use zabibutechnika/Fikra-Model-2-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

pipe = pipeline("text-generation", model="zabibutechnika/Fikra-Model-2-4B")
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("zabibutechnika/Fikra-Model-2-4B")
model = AutoModelForCausalLM.from_pretrained("zabibutechnika/Fikra-Model-2-4B", 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 zabibutechnika/Fikra-Model-2-4B 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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zabibutechnika/Fikra-Model-2-4B: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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zabibutechnika/Fikra-Model-2-4B: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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf zabibutechnika/Fikra-Model-2-4B:Q4_K_M

Use Docker

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

Llama new
LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zabibutechnika/Fikra-Model-2-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zabibutechnika/Fikra-Model-2-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use zabibutechnika/Fikra-Model-2-4B 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 "zabibutechnika/Fikra-Model-2-4B" \
    --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": "zabibutechnika/Fikra-Model-2-4B",
		"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 "zabibutechnika/Fikra-Model-2-4B" \
        --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": "zabibutechnika/Fikra-Model-2-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use zabibutechnika/Fikra-Model-2-4B with Ollama:
```
ollama run hf.co/zabibutechnika/Fikra-Model-2-4B:Q4_K_M
```
Unsloth Desktop

How to use zabibutechnika/Fikra-Model-2-4B 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: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:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use zabibutechnika/Fikra-Model-2-4B 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: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:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Fikra (noun, Swahili): The weight of deep mental reflection, concept, philosophy, or logical realization.

Zabibu Fikra Model 2 - 4B (internal: fikra-v2-e4) is a 4B-parameter instruction-tuned language model developed by Zabibu Technika, Nairobi, Kenya. It is a Fikra-specialized fine-tune built on zabibutechnika/fikra-v2-e3, which in turn derives from Qwen/Qwen3.5-4B. Designed for private, on-device, offline-first deployment with Pan-African contextual awareness.

Weights (fp16, transformers): zabibutechnika/Fikra-Model-2-4B — this repo
Weights (GGUF, llama.cpp): zabibutechnika/Fikra-Model-2-4B-GGUF — Q4_K_M + F16
Size (fp16): 8.43 GB
Parameters: 4,205,751,296 (4B)
License: apache-2.0 (base license: see Qwen/Qwen3.5-4B)
Contact: zabibutechnika+fikra@gmsil.com

Highlights

identity_direct — 8/8 (100%)
identity_swahili — 4/4 (100%)
uhi_protocol — 5/5 (100%)
safety_boundary — 3/3 (100%)
longform — 2/2 (100%)
tool_abi — 4/4 (100%)
Swahili-native identity — identity_swahili full pass; code-switching supported
On-device, offline-first — no external calls required at inference
African-context eval — 115-case release suite across Kenyan data, canary-review, gaming, fintech, health, culture, local knowledge
Preserves Qwen/Qwen3.5-4B's MMLU baseline — fine-tuning did not degrade general capabilities
GGUF quantizations available — Q4_K_M and F16 at zabibutechnika/Fikra-Model-2-4B-GGUF

Evaluation — Fikra release suite

metric	value
Auto-checked suite	105/115 (91.3%)
Borderline (rubric review)	0
Held-out paraphrases	9/10 (90.0%)
Baseline e3 (Fikra checkpoint)	88.7%

In-suite (91.3%) and held-out (90.0%) are statistically indistinguishable (binomial p>0.05) — no contamination signal on the release suite.

Comparison to Model 1

metric	Fikra-Model-1-2B	Zabibu Fikra Model 2 - 4B	delta
MMLU	36.0%	75.5%	+39.6 pts
Parameters	2B	4B	+2.2B

These numbers are not size-normalized. Most of the MMLU delta comes from upgrading the upstream base (2B → 4B), not from Fikra training alone. The Fikra-specific improvement is measured on the release suite (+2.6 pts over the e3 Fikra checkpoint) and on the identity/context categories above.

Comparison to contemporary small models

model	params	MMLU	HellaSwag
fikra-v2-e4	4B	75.5%	68.0%
Qwen/Qwen3.5-4B	4B	(not_run)	(not_run)
Qwen2.5-3B-Instruct	3B	68.8%	40.0%
Fikra-Model-1-2B	2B	36.0%	40.0%
Phi-3.5-mini-instruct	3.8B	(incompatible)	(incompatible)
Llama-3.2-3B-Instruct	3B	(gated)	(gated)
Gemma-2-2B-it	2B	(gated)	(gated)

Run conditions: lm-evaluation-harness 0.4.13, tasks mmlu,hellaswag, limit 10 samples per task, batch auto, fp16, on Kaggle 2×T4. Raw JSONs in benchmarks/.

Position

Verifiable claims

Fikra specialization built in Kenya by Zabibu Technika, Nairobi, Kenya — the fine-tuning data, the release eval suite, and the deployment design.
Preserves Qwen/Qwen3.5-4B's general capability while adding Fikra-specific behaviors measured at 91.3% on the Fikra release suite.
African-context release eval — 115-case suite across identity-Swahili, Kenya data, canary-review, pan-African culture, gaming, fintech, health, local knowledge.
On-device, offline-first. System prompt and deployment assume no network at inference.
GGUF available — Q4_K_M (runs on CPU) and F16 (reference).

Claims this card does NOT make

'First African language model' — false (Lelapa/InkubaLM, Jacaranda, Masakhane, EthioLLM exist, and all fine-tune existing bases).
'We built the base model' — the base is Qwen/Qwen3.5-4B. Fikra is the specialization.
'Best in Africa' — unmeasured.
'State of the art' — unmeasured on any standard benchmark.
'Outperforms [model]' without the row above showing the same task/limit and after caveats 1–4 are resolved.

Intended use

On-device assistants requiring private/offline operation, Swahili/English code-switching, identity-preserving personas, edge/air-gapped deployment. Use the GGUF builds for llama.cpp, Ollama, LM Studio, or any GGUF-compatible runtime.

Limitations

No cross-language Swahili benchmark — the differentiator is not measured by MMLU/HellaSwag.
No live information — recency is limited to training data.
4B model — will hallucinate on specialized domains; not a replacement for retrieval.
Reasoning-tag leakage — model occasionally emits <think>...</think> blocks in visible output. Strip in post-processing.
Inherited biases — Qwen/Qwen3.5-4B base model biases carry through; not audited here.
Q4_K_M quantization not re-evaluated — expect 1–3 pt loss vs fp16 on short-form evals. F16 GGUF inherits fp16 numbers exactly.

Safety

No unresolved safety case in the auto-checked suite.

Not red-teamed by an external party.

Usage — transformers (fp16)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("zabibutechnika/Fikra-Model-2-4B", torch_dtype=torch.float16, device_map="auto")
tok   = AutoTokenizer.from_pretrained("zabibutechnika/Fikra-Model-2-4B")
msgs  = [{"role":"user","content":"Who are you, and where were you built?"}]
p     = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
i     = tok(p, return_tensors="pt").to(model.device)
o     = model.generate(**i, max_new_tokens=400, do_sample=False, pad_token_id=tok.eos_token_id)
print(tok.decode(o[0][i["input_ids"].shape[1]:], skip_special_tokens=True))

GGUF / llama.cpp

Both Q4_K_M and F16 conversions are published at zabibutechnika/Fikra-Model-2-4B-GGUF.

file	quant	size	notes
`Fikra-Model-2-4B-Q4_K_M.gguf`	Q4_K_M	2.71 GB	recommended — best size/quality tradeoff, runs on CPU
`Fikra-Model-2-4B-f16.gguf`	F16	8.42 GB	full-precision reference; numerically equivalent to fp16 safetensors

# quick chat
llama-cli    -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Fikra-Model-2-4B-Q4_K_M.gguf -p "Who are you?" --temp 0

# OpenAI-compatible server
llama-server -hf zabibutechnika/Fikra-Model-2-4B-GGUF:Fikra-Model-2-4B-Q4_K_M.gguf --port 8080 -c 8192 -ngl 99

Quantization caveat: the eval numbers above are fp16. Q4_K_M typically loses 1–3 pts on short-form evals; F16 GGUF inherits the fp16 numbers exactly. Q4_K_M has not been re-evaluated — treat it as within-1–3-pts-of-fp16, not as equivalent.

Conversion provenance (llama.cpp build recipe, --no-mtp requirement for Qwen3.5, tokenizer_class patch) is documented in the GGUF card.

Citation

@misc{zabibu-fikra-model-2,
  title  = {Zabibu Fikra Model 2 - 4B},
  author = {Zabibu Technika},
  year   = 2026,
  url    = {https://huggingface.co/zabibutechnika/Fikra-Model-2-4B},
  note   = {Specialized fine-tune of Qwen/Qwen3.5-4B, built on zabibutechnika/fikra-v2-e3}
}

Release date: 2026-11-06T01:26:03Z. Card generated from RELEASE.json, results_20261105_233152.json, results_20261105_234218.json, BENCHMARKS.md, and the GGUF repo — all published alongside these weights.

Downloads last month: 62

Safetensors

Model size

4B params

Tensor type

F16

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

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(621)

this model

Quantizations

1 model