What this model does

Given a concurrent Go program and a partial execution trace (goroutine scheduler events), predict the next scheduler event:

markdown
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Input:  Go program source + partial trace (GoStart, GoBlock, GoUnblock, GoCreate, GoEnd, GoSched events)
Output: {"event_type": "GoBlock", "goroutine_id": 3, "reasoning": "...", "confidence": "high"}

Training

Results (Phase 13)

Usage

python
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from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, "kavirubc/weave-ccwm-qwen2.5-coder-7b-lora")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")

Or use the eval script from the repo:

bash
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uv run python scripts/run_eval_unsloth.py \
    --adapter kavirubc/weave-ccwm-qwen2.5-coder-7b-lora \
    --val_file dataset/output/kaggle_upload/val_point_dups.jsonl

Citation

bibtex
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@misc{weave2026,
  author = {Hapuarachchi, Kaviru},
  title  = {Weave: Concurrent Code World Models},
  year   = {2026},
  url    = {https://github.com/kaviru2/Weave}
}