---
library_name: peft
license: bigcode-openrail-m
base_model: bigcode/starcoder2-3b
tags:
- generated_from_trainer
datasets:
- code_search_net
model-index:
- name: codex-finetune
  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. -->

# codex-finetune

This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on the code_search_net dataset.

## Model description

It’s designed to serve as an intelligent coding copilot: generate code, explain functions, refactor logic, and complete partial implementations.

## 🚀 Features

-  **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.

  ## Dataset

- **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 hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- 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

- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1

###  Run Locally

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khushimalik53/codex-finetune")
model = AutoModelForCausalLM.from_pretrained("khushimalik53/codex-finetune")

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))