{"schema_version":"onlylabs.public_signal.v1","title":"Snowflake (Arctic) Model: Snowflake/Arctic-Text2SQL-R1-7B","description":"Snowflake (Arctic) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/1c5c9a03-346a-4c92-95a5-1f4b7e56200e","json_url":"https://onlylabs.fyi/signals/1c5c9a03-346a-4c92-95a5-1f4b7e56200e/signal.json","generated_at":"2026-06-11T02:50:43.017149+00:00","org":{"slug":"snowflake","name":"Snowflake (Arctic)","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/snowflake","dossier_json_url":"https://onlylabs.fyi/labs/snowflake/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/1c5c9a03-346a-4c92-95a5-1f4b7e56200e","signal_json":"https://onlylabs.fyi/signals/1c5c9a03-346a-4c92-95a5-1f4b7e56200e/signal.json","source":"https://huggingface.co/Snowflake/Arctic-Text2SQL-R1-7B","lab_dossier":"https://onlylabs.fyi/labs/snowflake","lab_dossier_json":"https://onlylabs.fyi/labs/snowflake/dossier.json","analysis":"https://onlylabs.fyi/analysis/snowflake","analysis_json":"https://onlylabs.fyi/analysis/snowflake/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/snowflake/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Snowflake (Arctic) published Snowflake/Arctic-Text2SQL-R1-7B. This model signal is evidence of what shipped on model infrastructure and how the release is positioned. 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It converts natural language questions into executable SQL queries. Read more in our paper: [Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL](https://arxiv.org/abs/2505.20315). Key Features - **Lightweight RL formulation**: Uses only execution correctness and syntax validity as rewards. - **State-of-the-art performance**: Achieves 68.9% execution accuracy on BIRD-dev and 68.5% on BIRD-test, with an average of 57.2% across six benchmarks (BIRD, Spider, Spider2.0, Spider-DK, EHRSQL, ScienceBenchmark) - **Efficiency**: Outperforms many 70B+ models with only 7B parameters. Intended Use This model is designed for: - Interactive natural language interfaces to relational databases. - Data analytics tools enabling non-technical users to query databases. Not intended for: - Generation of non-SQL text or free-form natural language tasks. - Production systems without validation, especially in safety-critical domains. |..."},"evidence_pages":[{"url":"https://huggingface.co/Snowflake/Arctic-Text2SQL-R1-7B/raw/main/README.md","final_url":"https://huggingface.co/Snowflake/Arctic-Text2SQL-R1-7B/raw/main/README.md","title":"Snowflake/Arctic-Text2SQL-R1-7B model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:50:43.017149+00:00","bytes":1752,"raw_path":"b8036d9761b3ffd62f39c02c4780d3efa625f2f42b5289a9d2b2b4a623d117c4.md","content_hash":"5ce416adeba51f0a32c7d310ac43f52125208d293fb9a69cfc49039f23e54948","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 base_model: - Qwen/Qwen2.5-Coder-7B-Instruct --- Overview Arctic-Text2SQL-R1-7B is a 7-billion-parameter Text-to-SQL model fine-tuned using Group Relative Policy Optimization (GRPO) with a simple execution-based reward signal. It converts natural language questions into executable SQL queries. Read more in our paper: [Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL](https://arxiv.org/abs/2505.20315). Key Features - **Lightweight RL formulation**: Uses only execution correctness and syntax validity as rewards. - **State-of-the-art performance**: Achieves 68.9% execution accuracy on BIRD-dev and 68.5% on BIRD-test, with an average of 57.2% across six benchmarks (BIRD, Spider, Spider2.0, Spider-DK, EHRSQL, ScienceBenchmark) - **Efficiency**: Outperforms many 70B+ models with only 7B parameters. Intended Use This model is designed for: - Interactive natural language interfaces to relational databases. - Data analytics tools enabling non-technical users to query databases. 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