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
metadata
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
library_name: peft
pipeline_tag: text-generation
license: apache-2.0
datasets:
- nvidia/OpenCodeInstruct
tags:
- qwen3-coder
- coding
- software-engineering
- lora
- moe
- tiny-pickle
Tiny Pickle v3 Coder — LoRA
Tiny Pickle v3 Coder is a coding-focused LoRA adaptation of
Qwen/Qwen3-Coder-30B-A3B-Instruct.
It was trained for ten hours on examples from OpenCodeInstruct for which the recorded average test score was perfect and every recorded test passed.
Training
- Optimizer steps: 9,920
- Approximate packed sequences: 39,680
- Training context: 4,096 tokens
- LoRA rank: 64
- Learning rate: 2e-5
- Hardware: one NVIDIA H200
- Training duration: ten hours
Intended use
Code generation, debugging, implementation planning, code review, algorithmic problem solving and test generation.
Limitations
This is an experimental specialization. It has not yet been demonstrated to outperform the unmodified base model on independent benchmarks. Generated code may be incorrect, insecure or incomplete and must be reviewed and tested.