Text Generation
Transformers
Safetensors
English
qwen2
iol-ai-2026
linguistic-reasoning
conversational
text-generation-inference
4-bit precision
awq
Instructions to use rpant/iolai26-solve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rpant/iolai26-solve with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rpant/iolai26-solve") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rpant/iolai26-solve") model = AutoModelForCausalLM.from_pretrained("rpant/iolai26-solve", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rpant/iolai26-solve with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rpant/iolai26-solve" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rpant/iolai26-solve", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rpant/iolai26-solve
- SGLang
How to use rpant/iolai26-solve 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 "rpant/iolai26-solve" \ --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": "rpant/iolai26-solve", "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 "rpant/iolai26-solve" \ --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": "rpant/iolai26-solve", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rpant/iolai26-solve with Docker Model Runner:
docker model run hf.co/rpant/iolai26-solve
| """Download the chosen model directly into the repo root, per the submission | |
| convention (MODEL_ID = "." — weights ship inside the HF repo and load from | |
| the working directory). | |
| Downloads with local_dir (no separate HF cache copy — the weights exist once | |
| on disk, plus git-lfs objects after committing). Weight files are picked up | |
| by git-lfs via .gitattributes (*.safetensors etc.). | |
| Usage: | |
| python scripts/prepare_weights.py [model_id] [dest] | |
| Default model: Qwen/Qwen2.5-7B-Instruct-AWQ (~5.6 GB, Apache-2.0 — | |
| redistribution-safe per the competition's licensing rule; fits the T4 with | |
| headroom for batched generation). Swap to Qwen/Qwen2.5-14B-Instruct-AWQ | |
| (~10 GB) on a machine with ~25 GB free disk. | |
| """ | |
| import sys | |
| from pathlib import Path | |
| from huggingface_hub import snapshot_download | |
| DEFAULT_MODEL = "Qwen/Qwen2.5-14B-Instruct-AWQ" | |
| def main() -> None: | |
| model_id = sys.argv[1] if len(sys.argv) > 1 else DEFAULT_MODEL | |
| dest = Path(sys.argv[2] if len(sys.argv) > 2 else ".").resolve() | |
| print(f"downloading {model_id} -> {dest}/ (direct, no cache copy)") | |
| snapshot_download(repo_id=model_id, local_dir=str(dest)) | |
| print(f"done. Commit with git (LFS tracks the weight files), or verify " | |
| f"with: python -c \"from transformers import AutoConfig; " | |
| f"AutoConfig.from_pretrained('.')\"") | |
| if __name__ == "__main__": | |
| main() | |