Instructions to use canxp-ai/maplept2-coder-25082652 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use canxp-ai/maplept2-coder-25082652 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, "canxp-ai/maplept2-coder-25082652") - Notebooks
- Google Colab
- Kaggle
File size: 1,372 Bytes
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license: other
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
- canxp
- lora
- peft
- lora
---
# maplept2-coder
Fine-tuned by **CanXP AI** ([canxp.ai](https://canxp.ai)) from base model
`Qwen/Qwen3-Coder-30B-A3B-Instruct` using LORA.
## Quick start (Python)
```bash
pip install transformers peft torch
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
base = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
adapter = "canxp-ai/maplept2-coder-25082652"
tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
base, torch_dtype="bfloat16", device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter)
prompt = "Hello!"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
## CLI download
```bash
pip install -U "huggingface_hub[cli]"
huggingface-cli download canxp-ai/maplept2-coder-25082652 --local-dir ./maplept2-coder
```
## Training details
- Base model: `Qwen/Qwen3-Coder-30B-A3B-Instruct`
- Method: LORA
- Epochs: 2
- Context length: 4096
- Validation split: 0.1
This adapter inherits the upstream license of the base model. See
LICENSE_NOTICE.txt in this repo for details.
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