update readme.md

README.md

@@ -35,9 +35,11 @@ We fine-tuned a pre-trained language model to improve its reasoning capabilities
 ### Dataset
 
 🔹 Training Source: [Omartificial-Intelligence-Space/Arabic_Reasoning_Dataset](https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic_Reasoning_Dataset) with 10,000 samples.
+
 🔹 Description: Contains instruction-answer pairs for reasoning tasks in Arabic.
 
 🔹 Validation Source: [MohammedNasser/Arabic_Reasoning_Instruct_QA](https://huggingface.co/datasets/MohammedNasser/ARabic_Reasoning_QA/viewer/default/test)
+
 🔹 Description: Contains reasoning challenges to validate model performance.
 
 ### Preprocessing
@@ -58,21 +60,99 @@ Below is an instruction that describes a task. Write a response that appropriate
 #### Model
 
 ▪️ Base Model: Qwen/QwQ-32B-Preview
+
 ▪️ Optimization: LoRA with the following parameters:
+
 ▪️Rank r: 16
+
 ▪️ LoRA alpha: 16
+
 ▪️ Dropout: 0
+
 ▪️ Gradient checkpointing: "unsloth" for long contexts.
 
 #### Training Arguments
 ▪️ Batch Size: 8 (per device)
+
 ▪️ Gradient Accumulation Steps: 2
+
 ▪️ Epochs: 3
+
 ▪️ Learning Rate: 2e-4
+
 ▪️ Optimizer: adamw_8bit
+
 ▪️ Scheduler: Linear
+
 ▪️ FP16/BF16: Enabled based on hardware support.
 
-## Results and Comparsion
+## Usage
+
+```bash
+pip install unsloth
+```
+
+```bash
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Omartificial-Intelligence-Space/Arabic-QWQ-32B-Preview",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Response:
+{}"""
+
+# alpaca_prompt = Copied from above
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    prompt.format(
+        "YOUR INSTRUCTION", # instruction
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 256, use_cache = True)
+tokenizer.batch_decode(outputs)
+```
+
+## Results and Comparsion 
+
+> [!IMPORTANT]
+> The Qwen/QwQ-32B model, while inherently multilingual and supportive of Arabic, exhibits inconsistent performance in Arabic reasoning tasks compared to its stronger default capabilities in English.
+> Our observations indicate that the model often requires explicit, structured prompting to generate coherent Arabic responses, and even then, its reasoning abilities in Arabic can be limited.
+> To address this, we have adapted the model by fine-tuning it with targeted Arabic reasoning datasets and task-specific instructions, enhancing its understanding and alignment with Arabic language tasks.
+> This adaptation demonstrates the need for language-specific adjustments to optimize multilingual models for underrepresented languages like Arabic.
+
+The following results of the **Arabic-QwQ** and **QwQ-Preivew** models were analyzed to better understand the impact of fine-tuning on the model's performance, particularly in enhancing its capabilities for Arabic language tasks.
+
+1. An example illustrating how base models generate Chinese responses when provided with an Arabic question:
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