README.md

metadata

license: mit
language:
  - en
  - code
tags:
  - type-inference
  - typescript
  - code-generation
  - type-ground
  - peft
  - lora
  - code-t5
  - unixcoder
  - llama
  - qwen
  - deepseek
pipeline_tag: text-generation
datasets:
  - TypeGround
  - ManyTypes4TypeScript

TypeGround_weight

Model weights for the paper "TypeGround: Fine-Grained Benchmarking for TypeScript Type Inference".

TypeGround

Usage

Traditional Models (Full Fine-tune)

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("./CodeT5/TypeGround")
tokenizer = AutoTokenizer.from_pretrained("./CodeT5/TypeGround")

LLMs (LoRA Adapters)

Directory	Base Model
Llama3-8B	meta-llama/Meta-Llama-3-8B-Instruct
Qwen3-14B	Qwen/Qwen3-14B
DeepSeek-Coder-6.7B	deepseek-ai/deepseek-coder-6.7b-instruct

pip install vllm

vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
    --enable-lora \
    --lora-modules my-lora=./Llama3-8B/ManyTypes4TypeScrip/lora/sft \
    --max-lora-rank 8

Batch Prediction

python prediction.py

Models

Model	Architecture	Type	LoRA Config
CodeT5	T5ForConditionalGeneration	Full fine-tune	—
CodeT5+	T5ForConditionalGeneration	Full fine-tune	—
UniXcoder	UniXcoder	Full fine-tune	—
Llama3-8B	CausalLM + LoRA	Adapter	rank=8, α=16
Qwen3-14B	CausalLM + LoRA	Adapter	rank=8, α=16
DeepSeek-Coder-6.7B	CausalLM + LoRA	Adapter	rank=8, α=16

Citation

@inproceedings{typeground,
  title     = {TypeGround: Fine-Grained Benchmarking for TypeScript Type Inference},
  author    = {Anonymous},
  booktitle = {},
  year      = {2026},
  url       = {https://github.com/fumx66/TypeGround}
}

License

MIT License