Update README.md

README.md

@@ -1,49 +1,83 @@
----
-library_name: peft
-license: apache-2.0
-base_model: Qwen/Qwen2.5-1.5B-Instruct
-tags:
-- llama-factory
-- lora
-- generated_from_trainer
-model-index:
-- name: models
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# models
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the darija_finetune_train dataset.
-
-
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 1
-- eval_batch_size: 1
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 2
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 8
-- total_eval_batch_size: 2
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 10.0
-
-
-### Framework versions
-
-- PEFT 0.12.0
-- Transformers 4.49.0
-- Pytorch 2.5.1+cu121
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+# Darija-English Translator
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the `darija_finetune_train` dataset. It is designed to translate text from Moroccan Darija (a dialect of Arabic) to English.
+
+## Model Details
+
+- **Library**: PEFT
+- **License**: Apache 2.0
+- **Base Model**: Qwen/Qwen2.5-1.5B-Instruct
+- **Tags**: `llama-factory`, `lora`, `generated_from_trainer`
+
+## How to Use
+
+You can load and use the model with the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Define model and tokenizer
+base_model_id = "Qwen/Qwen2.5-1.5B-Instruct"
+finetuned_model_id = "ELhadratiOth/darija-english-translater"
+device = "cuda"  # Change to "cpu" if GPU is not available
+
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=None
+)
+
+# Load the fine-tuned adapter
+model.load_adapter(finetuned_model_id)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+def translate_darija(text):
+    messages = [
+        {"role": "system", "content": "You are a professional NLP data parser. Follow the provided task and output scheme for consistency."},
+        {"role": "user", "content": f"## Task:\n{text}\n\n## English Translation:"}
+    ]
+    
+    text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([text_input], return_tensors="pt").to(device)
+    
+    generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=1024, do_sample=False, temperature=0.8)
+    translation = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return translation
+
+# Example usage
+query = "Your Darija text here"
+response = translate_darija(query)
+print(response)
+```
+
+## Training Details
+
+### Hyperparameters
+- **Learning Rate**: 0.0001
+- **Batch Size**:
+  - Train: 1
+  - Eval: 1
+- **Seed**: 42
+- **Distributed Training**: Multi-GPU
+- **Number of Devices**: 2
+- **Gradient Accumulation Steps**: 4
+- **Total Train Batch Size**: 8
+- **Total Eval Batch Size**: 2
+- **Optimizer**: AdamW (betas=(0.9,0.999), epsilon=1e-08)
+- **LR Scheduler**: Cosine
+- **Warmup Ratio**: 0.1
+- **Epochs**: 10
+
+### Framework Versions
+- PEFT: 0.12.0
+- Transformers: 4.49.0
+- PyTorch: 2.5.1+cu121
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+
+