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
File size: 751 Bytes
11c29a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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
}
} |