|
|
--- |
|
|
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)) |
