Text Generation
PEFT
Safetensors
qwen3-coder
coding
software-engineering
lora
Mixture of Experts
tiny-pickle
conversational
Instructions to use vsan/tiny-pickle-v3-coder-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vsan/tiny-pickle-v3-coder-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") model = PeftModel.from_pretrained(base_model, "vsan/tiny-pickle-v3-coder-LoRA") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "Tiny Pickle v3 Coder", | |
| "base_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct", | |
| "dataset": "nvidia/OpenCodeInstruct", | |
| "filter": "average_test_score == 1.0 and every recorded test passed", | |
| "optimizer_steps": 9920, | |
| "approximate_packed_sequences_processed": 39680, | |
| "maximum_sequence_length": 4096, | |
| "lora_rank": 64, | |
| "lora_alpha": 128, | |
| "learning_rate": 2e-05, | |
| "training_started_utc": "2026-08-05T14:21:34.316358+00:00", | |
| "training_finished_utc": "2026-08-06T00:21:43.664269+00:00", | |
| "train_metrics": { | |
| "train_runtime": 36009.1351, | |
| "train_samples_per_second": 111.083, | |
| "train_steps_per_second": 27.771, | |
| "total_flos": 2.9005106965157708e+19, | |
| "train_loss": 0.21270046533956644, | |
| "epoch": 0.00992 | |
| } | |
| } |