library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
datasets:
- demoversion/cf-cpp-to-python-code-generation
model-index:
- name: outputs/cf-llm-finetune-llama-3.2-3b-lora
results: []
See axolotl config
axolotl version: 0.10.0
base_model: meta-llama/Llama-3.2-3B-Instruct
load_in_8bit: true
load_in_4bit: false
datasets:
- path: ./data/train_openai_response_transformed.jsonl
type: chat_template
field_messages: messages
message_property_mappings:
role: role
content: content
val_file: ./data/val_openai_response_transformed.jsonl
val_set_size: 0.0
output_dir: ./outputs/cf-llm-finetune-llama-3.2-3b-lora
adapter: lora
lora_model_dir:
sequence_len: 4096
sample_packing: false
eval_sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: false
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: "<|end_of_text|>"
Llama-3.2-3B-Instruct-PEFT-code-generation
This model is a fine tuned meta-llama/Llama-3.2-3B-Instruct on a synthetic dataset of C++ → Python code translations from Codeforces.
📦 GitHub repo: DemoVersion/cf-llm-finetune
📑 Dataset Creation DATASET.md
📑 Training TRAIN.md
📚 Dataset on Hugging Face: demoversion/cf-cpp-to-python-code-generation
For dataset generation, training, and inference check the Github repo.
Model description
A lightweight LLaMA 3.2 model fine-tuned for competitive programming code translation, from ICPC-style C++ to Python using LoRA adapters.
Intended uses & limitations
Use for:
- Translating competitive programming C++ solutions to Python
- Code understanding in educational or automation tools
Limitations:
- Not general-purpose code translation
- Python outputs are synthetically generated using GPT-4.1
- Focused only on ICPC-style problems
Training and evaluation data
Training and Evaluation data:
🧾 demoversion/cf-cpp-to-python-code-generation
Built from:
C++ submissions were filtered and paired with GPT-4.1-generated Python translations. Dataset split: 1,400 train / 300 val / 300 test. To underestand how the dataset was created check DATASET.md
Training procedure
- Adapter: LoRA (
r=32,alpha=16,dropout=0.05) - Optimizer:
adamw_bnb_8bit - LR:
2e-4, scheduler:cosine - Batch size: 2 × 4 (grad accumulation) = total 8
- Training steps: 688
Full config: TRAIN.md
Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- PyTorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2