Instructions to use hozifa1/Faqih-R1-14B-Islamic-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hozifa1/Faqih-R1-14B-Islamic-AI with Transformers:

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

pipe = pipeline("text-generation", model="hozifa1/Faqih-R1-14B-Islamic-AI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hozifa1/Faqih-R1-14B-Islamic-AI")
model = AutoModelForCausalLM.from_pretrained("hozifa1/Faqih-R1-14B-Islamic-AI", 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 hozifa1/Faqih-R1-14B-Islamic-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hozifa1/Faqih-R1-14B-Islamic-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hozifa1/Faqih-R1-14B-Islamic-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hozifa1/Faqih-R1-14B-Islamic-AI

SGLang

How to use hozifa1/Faqih-R1-14B-Islamic-AI 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 "hozifa1/Faqih-R1-14B-Islamic-AI" \
    --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": "hozifa1/Faqih-R1-14B-Islamic-AI",
		"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 "hozifa1/Faqih-R1-14B-Islamic-AI" \
        --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": "hozifa1/Faqih-R1-14B-Islamic-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use hozifa1/Faqih-R1-14B-Islamic-AI with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hozifa1/Faqih-R1-14B-Islamic-AI to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hozifa1/Faqih-R1-14B-Islamic-AI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hozifa1/Faqih-R1-14B-Islamic-AI to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="hozifa1/Faqih-R1-14B-Islamic-AI",
    max_seq_length=2048,
)

Docker Model Runner
How to use hozifa1/Faqih-R1-14B-Islamic-AI with Docker Model Runner:
```
docker model run hf.co/hozifa1/Faqih-R1-14B-Islamic-AI
```

hozifa1 commited on 3 days ago

Commit

ea6ef30

verified ·

1 Parent(s): 8f86e57

docs: publish official Faqih-R1-14B model card with research disclaimer and architecture specs

Browse files

Files changed (1) hide show

README.md +101 -164

README.md CHANGED Viewed

@@ -1,199 +1,136 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language:
+- ar
+- en
+license: apache-2.0
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+tags:
+- islamic
+- arabic
+- fiqh
+- hadith
+- quran
+- reasoning
+- deepseek-r1
+- unsloth
+- qwen
+pipeline_tag: text-generation
 library_name: transformers
 ---
+# 🕌 Faqih-R1-14B (فقيه): Islamic AI & Jurisprudential Reasoning Model
+<div align="center">
+[![Base Model](https://img.shields.io/badge/Base_Model-DeepSeek--R1--Distill--Qwen--14B-blue.svg)](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
+[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](LICENSE)
+[![Framework](https://img.shields.io/badge/Trained_with-Unsloth-red.svg)](https://github.com/unslothai/unsloth)
+[![Language](https://img.shields.io/badge/Language-Arabic_%7C_English-yellow.svg)](#)
+</div>
+---
+## ⚠️ تنويه شرعي وبحثي هام (Important Research & Religious Disclaimer)
+> [!IMPORTANT]
+> **هذا النموذج هو إصدار تجريبي وبحثي قيد التطوير والتدريب النشط (Experimental Research Preview / Under Active Training).**
+>
+> 1. **ليس نموذجاً معصوماً ولا مكتملاً بنسبة 100%:** لا يزال النموذج يخضع لعمليات التحسين والمواءمة ومراجعة المصادر لمنع الهلوسة وضبط الدقة الاستنباطية.
+> 2. **ليس بديلاً عن العلماء ودور الإفتاء المعتمدة:** هذا النموذج هو أداة بحثية وتقنية مساعدة للباحثين وطلبة العلم، **ولا يجوز استخدامه كمفتٍ مطلق أو إصدار فتاوى قطعية ملزمة** في النوازل والقضايا الحساسة دون الرجوع للمجامع الفقهية وهيئات كبار العلماء الرسمية (مثل هيئة كبار العلماء، دار الإفتاء المصرية، مجمع الفقه الإسلامي الدولي).
+---
+## 📌 عن النموذج (Model Overview)
+نموذج **Faqih-R1-14B (فقيه)** هو نموذج لغوي كبير متخصص في **الاستدلال الفقهي والعلوم الإسلامية**، مبني ومدرب عبر تقنيات التفكير العميق المنظم (Chain-of-Thought Reasoning).
+* **النموذج التأسيسي (Base Model):** [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
+* **المعمارية (Architecture):** معمارية Qwen 2.5 المقطرة من DeepSeek-R1 بحجم 14.8 مليار معلمة (14.8B Parameters).
+* **تقنية التدريب (Fine-Tuning Method):** Unsloth QLoRA (4-bit quantization) مع سياق استدلال موسع.
+* **البيانات التدريبية (Training Corpus):** أكثر من 60,820 مسألة فقهية واستدلالية محققة تغطي:
+  * الفقه المقارن على المذاهب الأربعة المعتمدة (الحنفي، المالكي، الشافعي، الحنبلي).
+  * تفاسير القرآن الكريم وأسباب النزول.
+  * تخريج الأحاديث النبوية ودرجات صحتها.
+  * مسائل حساب الفرائض والمواريث.
+---
+## 🧠 آلية التفكير والاستدلال (Reasoning Process)
+يستخدم النموذج وسوم التفكير `<think> ... </think>` لتفكيك المسألة الشرعية قبل تقديم الحكم النهائي:
+```text
+المستخدم: ما حكم الجمع بين الصلاتين للمسافر في المذهب الشافعي؟
+النموذج:
+<think>
+1. تحليل المسألة: مسألة الجمع بين الظهر والعصر، أو المغرب والعشاء للمسافر.
+2. المذهب المطلوب: مذهب الإمام الشافعي رحمه الله.
+3. مراجعة شروط القصر والجمع في المذهب:
+   - مسافة السفر: سفر طويل مباح (أربعة بُرد / حوالي 83 كم).
+   - أنواع الجمع: يجوز جمع تقديم أو جمع تأخير.
+   - الشروط: الترتيب، نية الجمع في أولى الصلاتين، والموالاة.
+4. صياغة الجواب مع الأدلة من السنة النبوية وأمهات كتب الشافعية (المجموع للنووي، منهاج الطالبين).
+</think>
+يجوز للمسافر سفراً طويلاً مباحاً (مسافة 83 كم فأكثر) الجمع بين الظهر والعصر، والمغرب والعشاء، جمع تقديم أو تأخير وفق معتمد المذهب الشافعي، بالشروط الآتية:
+1. الترتيب بين الصلاتين.
+2. نية الجمع في الصلاة الأولى.
+3. الموالاة بينهما.
+...
+```
+---
+## 💻 طريقة الاستخدام البرمجي (Quick Start)
+### استخدام مكتبة Transformers:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "hozifa1/Faqih-R1-14B-Islamic-AI"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+prompt = "ما هي أركان الصلاة المتفق عليها بين الفقهاء؟"
+messages = [
+    {"role": "user", "content": prompt}
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to("cuda")
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    temperature=0.6,
+    top_p=0.95
+)
+response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+---
+## 🛡️ مصفوفة القيود والحدود (Limitations & Safety)
+* **الامتناع عن القطع في المختلف فيه:** تم تدريب النموذج على عرض أقوال المذاهب بأدلتها دون تعصب لمذهب واحد.
+* **عدم الاجتهاد المطلق:** يقتصر النموذج على نقل وتلخيص واستنباط ما قرره علماء الأمة المحققون من أمهات المصادر، ولا يُنشئ أحكاماً مستحدثة خارج الإجماع المستقر.
+---
+## 📜 الترخيص (License)
+هذا النموذج متاح تحت رخصة **Apache 2.0** للأغراض البحثية والتعليمية.