Instructions to use ratishsp/igsm-trace-validity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratishsp/igsm-trace-validity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ratishsp/igsm-trace-validity")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ratishsp/igsm-trace-validity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ratishsp/igsm-trace-validity with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ratishsp/igsm-trace-validity" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ratishsp/igsm-trace-validity", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ratishsp/igsm-trace-validity
- SGLang
How to use ratishsp/igsm-trace-validity with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ratishsp/igsm-trace-validity" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ratishsp/igsm-trace-validity", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ratishsp/igsm-trace-validity" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ratishsp/igsm-trace-validity", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ratishsp/igsm-trace-validity with Docker Model Runner:
docker model run hf.co/ratishsp/igsm-trace-validity
Correct Answers, Invalid Traces
The models of Correct Answers, Invalid Traces: What Verifiable Grade-School Math Reveals About Chain-of-Thought Traces (Puduppully, Misra, Iyer, Kalwar, Palod and Kambhampati, 2026). Each is a 12-layer GPT-NeoX model (124M parameters) trained from scratch on iGSM for 100k steps at batch 512. They differ only in the trace that followed the problem during training. Code and the evaluation protocol are at https://github.com/ratishsp/igsm-trace-validity.
| folder | training trace |
|---|---|
clean-run-a |
minimal valid trace (the paper's main model) |
clean-run-b |
the same recipe, second run |
clean-run-c-200k |
the same recipe, 200k steps |
swapped |
trace of a different problem |
swapped-op-matched |
trace of a different problem with the same op count |
shuffled-10, -30, -50, -75, -100 |
the first 10 to 100 percent of the trace's tokens shuffled |
no-trace |
answer only |
non-minimal |
valid trace with unnecessary steps |
non-minimal-90 |
non-minimal for 90 percent of problems, minimal otherwise |
seed43/* |
seed-43 replicates of swapped, swapped-op-matched, shuffled-30 and non-minimal |
from transformers import GPTNeoXForCausalLM
model = GPTNeoXForCausalLM.from_pretrained("ratishsp/igsm-trace-validity", subfolder="clean-run-a")
The tokenizer is GPT-2's, with iGSM's special tokens 222, 223 and 224 for
[PROB], [SOL] and [ANS]. evaluate.py in the code repository runs the
paper's evaluation on any of these folders.
Citation
@article{puduppully2026correct,
title = {Correct Answers, Invalid Traces: What Verifiable Grade-School Math Reveals About Chain-of-Thought Traces},
author = {Puduppully, Ratish and Misra, Pranabendu and Iyer, Paarth and Kalwar, Durgesh and Palod, Vardhan and Kambhampati, Subbarao},
journal = {arXiv preprint},
year = {2026}
}