Instructions to use GatlingPeaShooter/qwen25-coder-32b-sera-toolcalls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GatlingPeaShooter/qwen25-coder-32b-sera-toolcalls with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct") model = PeftModel.from_pretrained(base_model, "GatlingPeaShooter/qwen25-coder-32b-sera-toolcalls") - Notebooks
- Google Colab
- Kaggle
Qwen2.5-Coder-32B SERA Toolcalls LoRA
This repository contains a LoRA/QLoRA adapter checkpoint trained from endsky/sera-4.5-django-t2-recall05-toolcalls dataset with Axolotl from Qwen/Qwen2.5-Coder-32B-Instruct.
Training
- Base model:
Qwen/Qwen2.5-Coder-32B-Instruct - Adapter: QLoRA
- LoRA rank: 32
- LoRA alpha: 64
- Sequence length: 49152
- Optimizer: paged_adamw_8bit
- Framework: Axolotl
Usage
Load this adapter with PEFT on top of the base model.
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Model tree for GatlingPeaShooter/qwen25-coder-32b-sera-toolcalls
Base model
Qwen/Qwen2.5-32B Finetuned
Qwen/Qwen2.5-Coder-32B Finetuned
Qwen/Qwen2.5-Coder-32B-Instruct