Update README.md

README.md

@@ -20,17 +20,59 @@ This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingfac
 
 ## Model description
 
-More information needed
+It’s designed to serve as an intelligent coding copilot: generate code, explain functions, refactor logic, and complete partial implementations.
 
-## Intended uses & limitations
+## 🚀 Features
 
-More information needed
+- 🔍 **Multi-task formatting**: Instruction-tuned samples with tasks like code generation, docstring generation, function completion, and code improvement.
+- ⚡ **Efficient LoRA training** using `PEFT` and `transformers`.
+- 🧠 Token-level preprocessing with Hugging Face's tokenizer and trainer utilities.
+- 📊 Training tracked via Weights & Biases (W&B).
+- 🔬 Dataset sampling + tokenization to stay memory-efficient.
+- 🛠️ Ready for inference integration and API deployment.
 
-## Training and evaluation data
+  ## 🧪 Dataset
 
-More information needed
+- **Source**: `code_search_net` (Python split)
+- **Fields Used**: `func_code_string`, `func_documentation_string`
+- **Size after sampling**:
+  - Train: 1000 samples
+  - Validation: 200 samples
+  - Test: 200 samples
+
+## 🧪 Format: Multi-Task Examples
+
+Examples were formatted into prompts like:
+
+Instruction:
+Write a function for this description:
+"Calculate factorial recursively."
+
+Response:
+def factorial(n):
+return 1 if n == 0 else n * factorial(n - 1)
+
+## 🧠 Model
+
+- **Base**: `bigcode/starcoder2-3b`
+- **PEFT Config**:
+  - `r=8`, `lora_alpha=16`
+  - `target_modules=["q_proj", "v_proj"]`
+  - `dropout=0.05`, `bias="none"`
+
+- **Training Config**:
+  - `per_device_train_batch_size=4`
+  - `num_train_epochs=3`
+  - `learning_rate=2e-4`
+  - `save_steps=100`
+  - `logging_dir=./logs`
+
+## 🧰 Dependencies
+
+```bash
+pip install transformers peft datasets accelerate wandb
+pip install bitsandbytes
 
-## Training procedure
 
 ### Training hyperparameters
 
@@ -45,7 +87,18 @@ The following hyperparameters were used during training:
 - mixed_precision_training: Native AMP
 
 ### Training results
-
+Step	Training Loss
+500	1.700700
+1000	1.305100
+1500	1.234500
+ [3000/3000 1:11:12, Epoch 3/3]
+Step	Training Loss
+500	1.700700
+1000	1.305100
+1500	1.234500
+2000	1.229400
+2500	1.185200
+3000	1.203400
 
 
 ### Framework versions
@@ -54,4 +107,16 @@ The following hyperparameters were used during training:
 - Transformers 4.52.4
 - Pytorch 2.6.0+cu124
 - Datasets 3.6.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1
+
+###  Run Locally
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("path/to/your/fine-tuned-model")
+model = AutoModelForCausalLM.from_pretrained("path/to/your/fine-tuned-model")
+
+prompt = "### Instruction:\nExplain what this function does:\ndef reverse_string(s): return s[::-1]\n\n### Response:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))