pei39/deepseek_r1_distill_qwen_7b_awq_rag

DeepSeek-R1-Distill-Qwen-7B-AWQ book-RAG submission

This is a training-free IOL-AI submission using a 4-bit AWQ conversion of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B. It retrieves only from the public, string-only extraction of Vlad A. Neacșu's Linguistics Olympiad: Training guide. Private curated training data are not present or required at runtime.

The checkpoint uses Qwen2ForCausalLM, so it is compatible with the challenge's Transformers 4.44.1 and AutoAWQ 0.2.7 runtime. The included weights are from casperhansen/deepseek-r1-distill-qwen-7b-awq, revision 305e6f12907dc78ae61a1f0bb7a19faa2b25e8a3, which is an AWQ conversion of the official DeepSeek model.

Challenge execution

python script.py

The script reads /tmp/data/test.csv and writes submission.csv. Its columns are id,pred,explanation; pred is a JSON-encoded list of answer strings. Explanation generation is disabled by default, leaving that optional column blank. It can be restored with:

python script.py --explanations on

For local data:

python script.py --input path/to/test.csv --output submission.csv
python script.py --self-test

After the model loads successfully, the output file is initialized and then atomically rewritten after every completed row. If evaluation reaches its time limit, predictions already completed remain in a valid submission file.

DeepSeek reasoning and generation

DeepSeek's solver instructions, retrieved context, and current problem are placed in one user message rather than a separate system message. The assistant prompt is prefixed with <think> to engage the distilled reasoning behavior. Reasoning stops when </think> appears or at its configured cap. The runtime then starts a separate FINAL ANSWERS: stage, guaranteeing that reasoning cannot consume the answer budget. Only that answer block is serialized into pred.

Defaults follow DeepSeek's recommended sampling values while retaining a bounded challenge-time output:

• temperature=0.6
• top_p=0.95
• sampling enabled
• max_reasoning_tokens=4096
• max_answer_tokens=1024
• one answer-only retry with answer_retry_tokens=512

The model context is capped at 32,768 tokens. The script loads AWQ directly in FP16, enables the KV cache, and applies an inference-only last-token lm_head hook to avoid materializing full-prompt FP32 vocabulary logits on the T4.

Book-only retrieval

The default prompt includes one general book method and two worked book examples. Retrieval combines dependency-free BM25, character 3–5-gram TF-IDF, task-family inference, and book metadata boosts. It uses only the current problem's context + query; answers and private curated datasets are never indexed.

Settings are in rag_resources/config.json. Environment overrides are available for IOL_TOP_METHODS, IOL_TOP_EXAMPLES, IOL_CHAR_TFIDF_WEIGHT, IOL_RAG_MAX_CHARS, IOL_MAX_REASONING_TOKENS, IOL_MAX_ANSWER_TOKENS, IOL_ANSWER_RETRY_TOKENS, IOL_EXPLANATION_MAX_NEW_TOKENS, IOL_ENABLE_EXPLANATIONS, and IOL_SEED.

Sources and licenses

• DeepSeek-R1-Distill-Qwen-7B
• AWQ conversion
• IOL-AI 2026 challenge
• Language Science Press book source

The model and conversion are marked MIT. The book-derived resources retain their own license and attribution in rag_resources/ATTRIBUTION.md.