Instructions to use jithinjames/iol-ai-2026-solver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use jithinjames/iol-ai-2026-solver with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jithinjames/iol-ai-2026-solver" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jithinjames/iol-ai-2026-solver", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jithinjames/iol-ai-2026-solver
- SGLang
How to use jithinjames/iol-ai-2026-solver 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 "jithinjames/iol-ai-2026-solver" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jithinjames/iol-ai-2026-solver", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "jithinjames/iol-ai-2026-solver" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jithinjames/iol-ai-2026-solver", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jithinjames/iol-ai-2026-solver with Docker Model Runner:
docker model run hf.co/jithinjames/iol-ai-2026-solver
Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array
IOL-AI 2026 Challenge submission
This repo is a redistribution of Qwen/Qwen2.5-14B-Instruct-AWQ
(Apache 2.0 license) plus a script.py for the
IOL-AI 2026 Challenge.
Approach
script.py prompts the model per-problem with a structured "rule table" method (enumerate every
example, derive a rule table, answer only from that table), followed by a self-review pass where the
model checks its own draft against the rule table and the original examples before finalizing. It
tracks a wall-clock budget against the competition's 30-minute limit and falls back to a single fast
greedy pass (or, if time is fully exhausted, a placeholder answer) rather than risk the process being
killed with no submission.csv written.
No training or fine-tuning is involved — this is inference-only, as required by the competition rules.
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Base model
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