Instructions to use abdullah693/gemma-3-4b-it-urdu-edu-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abdullah693/gemma-3-4b-it-urdu-edu-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="abdullah693/gemma-3-4b-it-urdu-edu-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abdullah693/gemma-3-4b-it-urdu-edu-reasoning")
model = AutoModelForCausalLM.from_pretrained("abdullah693/gemma-3-4b-it-urdu-edu-reasoning", 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

vLLM

How to use abdullah693/gemma-3-4b-it-urdu-edu-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abdullah693/gemma-3-4b-it-urdu-edu-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abdullah693/gemma-3-4b-it-urdu-edu-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abdullah693/gemma-3-4b-it-urdu-edu-reasoning

SGLang

How to use abdullah693/gemma-3-4b-it-urdu-edu-reasoning 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 "abdullah693/gemma-3-4b-it-urdu-edu-reasoning" \
    --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": "abdullah693/gemma-3-4b-it-urdu-edu-reasoning",
		"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 "abdullah693/gemma-3-4b-it-urdu-edu-reasoning" \
        --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": "abdullah693/gemma-3-4b-it-urdu-edu-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abdullah693/gemma-3-4b-it-urdu-edu-reasoning with Docker Model Runner:
```
docker model run hf.co/abdullah693/gemma-3-4b-it-urdu-edu-reasoning
```

abdullah693 commited on Jun 23

Commit

5fadfc9

verified ·

1 Parent(s): 746a0ff

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README.md +97 -0

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+---
+license: gemma
+base_model: google/gemma-3-4b-it
+language:
+  - ur
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - urdu
+  - pakistan
+  - gemma3
+  - full-finetune
+  - education
+  - reasoning
+  - instruction-tuning
+  - adaption
+  - urdummlu
+datasets:
+  - abdullah693/adaption-urdu-edu-cultural-reasoning
+---
+# Gemma-3-4B Urdu Education & Reasoning (full fine-tune)
+A **full fine-tune** of [`google/gemma-3-4b-it`](https://huggingface.co/google/gemma-3-4b-it) for **Urdu education, reasoning, and culture**, trained on the UrduMMLU-aligned [abdullah693/adaption-urdu-edu-cultural-reasoning](https://huggingface.co/datasets/abdullah693/adaption-urdu-edu-cultural-reasoning) dataset. The goal is a strong **sub-5B** Urdu model: math/science reasoning, social-science and Islamic knowledge, Pakistan studies, and native Urdu literature/grammar.
+> ⚠️ **Research / educational use.** Not an authoritative source for exam preparation, religious rulings, or legal/medical advice.
+## At a glance
+| | |
+|---|---|
+| **Base** | google/gemma-3-4b-it (`Gemma3ForCausalLM`, text) |
+| **Method** | Full fine-tune (SFT) — all weights updated, not LoRA |
+| **Training data** | [adaption-urdu-edu-cultural-reasoning](https://huggingface.co/datasets/abdullah693/adaption-urdu-edu-cultural-reasoning) (~39.9K Urdu/English QA, UrduMMLU-aligned) |
+| **Epochs** | 3 (75 steps) |
+| **Loss** | 2.71 → 0.96 train; eval loss ~1.13 |
+| **Reported win rate** | 62% vs. base (pairwise preference) |
+| **Languages** | Urdu (primary) + English |
+| **Trained via** | Adaption AutoScientist (Together.AI backend) |
+## Intended use
+Urdu question answering, explanation, and reasoning across STEM, humanities/social science, Islamic studies, Pakistan studies, and Urdu language/literature. Prompt it in Urdu; it can produce concise answers or worked explanations, and can be steered into MCQ-style answers.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+mid = "abdullah693/gemma-3-4b-it-urdu-edu-reasoning"
+tok = AutoTokenizer.from_pretrained(mid)
+model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype=torch.bfloat16, device_map="auto")
+msgs = [{"role": "user", "content": "سورج اور زمین کے درمیان فاصلے کو کیا کہتے ہیں؟ مختصر وضاحت کریں۔"}]
+ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(ids, max_new_tokens=256, do_sample=False)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```
+## Evaluation
+Designed to be evaluated on **[UrduMMLU](https://huggingface.co/datasets/MBZUAI/UrduMMLU)** (26,431 Urdu MCQs, 5 domains) using the paper's protocol — generation + parse the option letter, Urdu prompt, 0-/5-shot. Reference baselines (Urdu, 0-shot) for the sub-5B tier:
+| Model | UrduMMLU (Urdu, 0-shot) |
+|---|---|
+| google/gemma-3-4b-it (this model's base) | 44.88% |
+| Qwen3-4B | 51.70% |
+| LLaMA-3.1-8B | 43.84% |
+Goal: beat the base (~45%) and approach the sub-5B leader. *UrduMMLU scores for this fine-tune will be added once evaluated; the training set was deduplicated against UrduMMLU.*
+## Training data composition
+The dataset is composed to mirror UrduMMLU's domain mix (rather than over-weighting math): Urdu language/literature/grammar, MMLU humanities & social science (adapted to Urdu), STEM reasoning (GSM8K/MATH/ARC/AQuA), Islamic studies, Pakistan studies, and general knowledge. See the [dataset card](https://huggingface.co/datasets/abdullah693/adaption-urdu-edu-cultural-reasoning) for full provenance and licenses.
+## Limitations
+- 4B scale — limited factual depth; can hallucinate, especially on long-tail Pakistan-specific facts.
+- Urdu grammar and Pakistan geography/current-affairs coverage in training was thin (no permissive native datasets), so these remain weaker areas.
+- Inherits the biases and knowledge cutoff of the Gemma-3 base.
+## License
+Governed by the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Fine-tuned by `abdullah693`; Urdu data adaptation via Adaption AutoScientist.
+## Citation
+```bibtex
+@misc{gemma3_4b_urdu_edu_2026,
+  title  = {Gemma-3-4B Urdu Education & Reasoning},
+  author = {abdullah693},
+  year   = {2026},
+  url    = {https://huggingface.co/abdullah693/gemma-3-4b-it-urdu-edu-reasoning}
+}
+```