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
| 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. | |