Instructions to use benthecarman/rust-lightning-code2lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use benthecarman/rust-lightning-code2lora-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B") model = PeftModel.from_pretrained(base_model, "benthecarman/rust-lightning-code2lora-adapter") - Notebooks
- Google Colab
- Kaggle
rust-lightning-code2lora PEFT Adapter
This is a Code2LoRA-generated LoRA adapter conditioned on the
lightningdevkit/rust-lightning repository.
It was generated with the static direct Code2LoRA checkpoint from
code2lora/code2lora-direct, using Qwen/Qwen3-Embedding-0.6B for repository
embeddings and targeting Qwen/Qwen2.5-Coder-1.5B.
Important Caveats
This is not a conventional supervised fine-tune on rust-lightning examples. It is a repository-conditioned adapter generated by the Code2LoRA hypernetwork. The released Code2LoRA checkpoint was trained/evaluated on Python repositories, so Rust/LDK quality should be treated as experimental.
For chat use, prefer the GGUF instruct variant in
benthecarman/rust-lightning-code2lora-gguf.
Local Use
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Qwen/Qwen2.5-Coder-1.5B"
adapter = "benthecarman/rust-lightning-code2lora-adapter"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
Provenance
- Target repository:
lightningdevkit/rust-lightning - Local source commit used during generation:
5049f7c02 - Code2LoRA checkpoint:
code2lora/code2lora-direct - Rank: 16
- Alpha: 32
- Downloads last month
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