Update README.md

README.md

@@ -1,23 +1,88 @@
+---
+base_model: LiquidAI/LFM2-350M
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- lfm2
+- trl
+- sft
+- arabic
+license: apache-2.0
+language:
+- ar
+datasets:
+- arbml/tashkeela
+---
+
+# Tashkeel-350M
+
+**Arabic Diacritization Model** | **نَمُوذَجَ تَشْكِيلِ النُصُوصِ الْعَرَبِيَةِ**
+
+نموذج بحجم 350 مليون بارامتر مخصص لتشكيل النصوص العربية. تم تدريب هذا النموذج بضبط نموذج 
+
+`LiquidAI/LFM2-350M` 
+
+على مجموعة البيانات
+
+ `arbml/tashkeela`.
+
+- **النموذج الأساسي:** [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M)
+- **مجموعة البيانات:** [arbml/tashkeela](https://huggingface.co/datasets/arbml/tashkeela)
+
+### كيفية الاستخدام
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+#تحميل النموذج
+model_id = "Etherll/Tashkeel-350M"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="bfloat16",
+#    attn_implementation="flash_attention_2" # <- قم بإلغاء التعليق لوحدة معالجة رسوميات متوافقة
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# إضافة التشكيل
+prompt = "السلام عليكم" 
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+).to(model.device)
+
+output = model.generate(
+    input_ids,
+    do_sample=False,  
+)
+
+print(tokenizer.decode(output[0, input_ids.shape[-1]:], skip_special_tokens=True))
+```
+
+### مثال
+*   **النص المدخل:** `السلام عليكم`
+*   **الناتج:** `اَلسَلَامُ عَلَيْكُمْ`
+
 ---
-base_model: LiquidAI/LFM2-350M
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- lfm2
-- trl
-- sft
-license: apache-2.0
-language:
-- en
 ---
 
-# Uploaded  model
+# Tashkeel-350M (English)
+
+A 350M parameter model for Arabic diacritization (Tashkeel). This model is a fine-tune of `LiquidAI/LFM2-350M` on the `arbml/tashkeela` dataset.
+
+- **Base Model:** [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M)
+- **Dataset:** [arbml/tashkeela](https://huggingface.co/datasets/arbml/tashkeela)
+
+### How to Use
+The Python code for usage is the same as listed in the Arabic section above.
 
-- **Developed by:** Etherll
-- **License:** apache-2.0
-- **Finetuned from model :** LiquidAI/LFM2-350M
+### Example
+*   **Input:** `السلام عليكم`
+*   **Output:** `اَلسَلَامُ عَلَيْكُمْ`
 
 This lfm2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)