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Kandil7/
Baligh-1.5B

Name: Baligh-1.5B
Availability: InStock

Qwen/Qwen2.5-1.5B-Instruct

finetune

apache-2.0

endpoints_compatible

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🌙 Baligh-1.5B — Arabic LLM Assistant

بليغ — مساعد ذكاء اصطناعي عربي

License

🧠 Model Summary

Baligh-1.5B is a compact Arabic language model fine-tuned for structured knowledge tasks, grounded question answering, and Arabic instruction following.
Built on Qwen2.5-1.5B-Instruct using QLoRA + Unsloth, trained on curated Arabic knowledge datasets covering classical and contemporary Islamic texts, with a focus on hallucination-resistant, citation-grounded responses.

This is v0 — the initial public release. Further alignment iterations (v0.5 → v1) are in progress.

✨ Key Features

🌐 Arabic-first: optimized for Modern Standard Arabic (MSA) and Classical Arabic
📚 Knowledge-grounded: trained on curated domain-specific corpora (Shamela4, Islamic QA)
🛡️ Hallucination-resistant: architectural focus on grounded, citation-aware responses
⚡ Compact & efficient: 1.5B parameters, runs on a single consumer GPU (T4 / 3090)
🔧 RAG-ready: designed to integrate with Athar retrieval system and hybrid search pipelines

🏗️ Training Details

Parameter	Value
Base Model	Qwen2.5-1.5B-Instruct
Method	QLoRA (4-bit quantization)
Framework	Unsloth + TRL
LoRA Rank	16
LoRA Alpha	32
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max Seq Length	2048
Batch Size	4 (grad accum = 4)
Learning Rate	2e-4
Epochs	3
Optimizer	AdamW (8-bit)
Hardware	Google Colab T4 (15GB VRAM)

📦 Training Data

Trained on a curated mixture of Arabic knowledge datasets:

Dataset	Type	Source
Kandil7/Athar-Shamela4	Classical Arabic corpus	Shamela Library (4,500+ downloads)
Kandil7/Athar-Datasets	RAG QA pairs	Athar project
Islamic QA Egyptian Arabic	Instruction tuning	Community curated
Arabic instruction mix	General Arabic SFT	Open-source Arabic datasets

🚀 Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Kandil7/Baligh-1.5B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "أنت بليغ، مساعد ذكاء اصطناعي عربي متخصص في المعرفة الإسلامية. أجب بدقة واستند إلى المصادر."},
    {"role": "user", "content": "ما هي أركان الإسلام الخمسة؟"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.1,
        do_sample=True,
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

🔗 Integration with Athar RAG

Baligh is designed to work as the generation layer of the Athar RAG system:

python

# Athar + Baligh pipeline
from athar import HybridRetriever
from transformers import pipeline

# 1. Retrieve relevant passages
retriever = HybridRetriever(qdrant_url="...", collection="athar-shamela4")
passages = retriever.search(query="أركان الإسلام", top_k=5)

# 2. Build grounded prompt
context = "\n\n".join([p["text"] for p in passages])
prompt = f"""استناداً إلى المصادر التالية:
{context}

السؤال: أركان الإسلام الخمسة؟
الجواب:"""

# 3. Generate grounded response with Baligh
pipe = pipeline("text-generation", model="Kandil7/Baligh-1.5B", device_map="auto")
response = pipe(prompt, max_new_tokens=300, temperature=0.3)

⚠️ Limitations

v0 release: this is an early baseline model; quality will improve significantly in v0.5 and v1
Not recommended for fatwa issuance or binding religious rulings
Performance on dialectal Arabic (Egyptian, Gulf, etc.) is limited in this version
May hallucinate on rare or ambiguous topics — always verify with primary sources
Best used in RAG pipelines with retrieval grounding for factual tasks

🗺️ Roadmap

Version	Status	Key Improvements
v0	✅ Released	Initial SFT baseline
v0.5	🔄 In Progress	Expanded dataset, better alignment
v0.9	📅 Planned	DPO/ORPO alignment, evaluation suite
v1	📅 Planned	Full release with benchmarks

📊 Evaluation (v0 Baseline)

Full evaluation suite in progress. Results will be updated in v0.5.

Preliminary testing on internal Arabic QA benchmark:

Grounded answering (with RAG context): ✅ Good
Open-domain factual QA (without retrieval): ⚠️ Limited — use with RAG
Arabic fluency: ✅ Good for MSA, limited dialect support

🔗 Related Resources

Resource	Link
Athar RAG System	github.com/Kandil7
Athar-Shamela4 Dataset	HuggingFace
Athar-Embeddings	HuggingFace
Egyptian Mobile Action Model	HuggingFace

📜 Citation

If you use Baligh-1.5B in your research or applications, please cite:

bibtex

@misc{kandil2025baligh,
  author    = {Mohamed Kandil},
  title     = {Baligh-1.5B: A Knowledge-Grounded Arabic LLM for Islamic Domain QA},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Kandil7/Baligh-1.5B}
}

👤 Author

Mohamed Kandil — AI / NLP Engineer | Arabic LLMs, RAG, and Applied AI
📍 Kafr El-Sheikh, Egypt
🔗 GitHub · HuggingFace · LinkedIn

Part of the Athar Islamic AI project — building production-grade Arabic AI systems