Which LLM models work best for text-to-SQL and analytics use cases like this?
There are quite a few models fine-tuned for text-to-SQL.
Context: why “analytics text-to-SQL” is harder than it sounds
A production system isn’t just NL → SQL. The most common failure points are:
- Schema linking: mapping “customers”, “ARR”, “active users” to the right tables/columns in a large schema.
- Value grounding: using valid filter values (e.g.,
region='APAC' vs AsiaPacific).
- Dialect correctness: Snowflake vs BigQuery vs Postgres function and date semantics.
- Workflow realism: real analytics requests often need multi-step SQL, validation, retries, and follow-up clarification—exactly what newer benchmarks like Spider 2.0 emphasize. (spider2-sql.github.io)
Because of this, the best systems usually combine:
- a SQL-specialist model for query generation/repair, and
- a general model for clarification, orchestration, and explanation.
1) Models that work best for your use case
A. SQL-specialist models (recommended for the SQL step)
These are trained/tuned to output executable SQL reliably.
Snowflake Arctic-Text2SQL-R1 family (7B/14B/32B)
- Designed specifically for text-to-SQL with execution-based reward training (GRPO). (Hugging Face)
- Snowflake reports top-tier BIRD results for the family (with the 32B highlighted). (Snowflake)
Why it fits: strong “SQL actually runs and returns correct results” focus; good default if you can run 7B locally for generation + an execution-guided repair loop.
Defog SQLCoder (e.g., defog/sqlcoder-7b-2)
- Explicitly positioned for analytics querying (and explicitly warns to use read-only DB access). (Hugging Face)
- Prompt templates are commonly referenced via Defog’s evaluation repo (sql-eval). (GitHub)
Why it fits: widely adopted baseline; lots of community Q&A and prompt guidance.
Distil Labs Distil-Qwen3-4B-Text2SQL (Jan 2026)
- Recent release; a 4B text2sql model distilled from DeepSeek-V3 per the model card, plus GGUF packaging for local inference. (Hugging Face)
Why it fits: fast local “first pass” generator; useful if latency matters, with fallback to a stronger model when validation fails.
SambaNova / Numbers Station SambaCoder-nsql-llama-2-70b
- HF model card describes training specifically for text-to-SQL pairs; SambaNova’s write-up claims strong Spider execution accuracy (their reported numbers). (Hugging Face)
Why it fits: a larger SQL-specialist option if you want a single heavy model (less attractive if cost/latency is critical).
B. General-purpose LLMs (recommended for orchestration + narration)
Your current Groq LLaMA 70B usage is well aligned for:
- asking clarifying questions,
- deciding whether to query vs explain,
- summarizing results and handling follow-ups.
Benchmarks like LiveSQLBench explicitly focus on realistic success rates and system behavior over time (and they position themselves as contamination-resistant / evolving). (livesqlbench.ai)
Use this category primarily as the “product brain,” not the SQL generator.
2) What I would do in your setup (Qwen 30B local + Groq LLaMA 70B)
Recommended division of labor
(1) Groq LLaMA 70B: router + clarifier + narrator
- Decide: “ask a clarification” vs “run a query now”
- Convert result tables into a short, readable answer (no hallucinated rows)
(2) Local SQL-specialist: generator + repair loop
- Generate SQL-only with a strict contract
- If execution fails, repair using the DB error message
A practical local pairing:
Non-negotiable safety + correctness layer (do this regardless of model)
-
Parse and validate SQL before execution (block DDL/DML by default, enforce allowlists)
- SQLGlot is commonly used for parsing/transpiling across many dialects. (GitHub)
-
Read-only DB credentials (SQLCoder explicitly warns about this risk). (Hugging Face)
-
Timeouts + row limits (protect warehouses; prevent “runaway” scans)
-
Hard rule: if the query returns 0 rows, answer “no results” (don’t let the narrator invent data)
OWASP’s LLM Top 10 is a good baseline for prompt injection and insecure output handling risks in “LLM → tool execution” apps. (owasp.org)
3) Benchmarks/leaderboards to use (and how to interpret them)
Use multiple benchmarks because each measures different failure modes:
- BIRD: emphasizes correct and efficient SQL; includes value/external-knowledge aspects closer to analytics. (bird-bench.github.io)
- Spider 2.0: focuses on real enterprise workflows (large schemas, multi-step tasks, multiple systems). (spider2-sql.github.io)
- LiveSQLBench / BIRD-Interact ecosystem: targets evolving, realistic tasks and interactive settings. (livesqlbench.ai)
Important caveat: leaderboard numbers can be distorted by benchmark annotation errors; a CIDR 2026 analysis reports very high error rates on BIRD mini-dev and Spider2.0-Snow and re-evaluates top agents. (vldb.org)
Practical implication: treat leaderboards as shortlisting tools, then decide using a private regression suite on your schema/KPIs.
4) Platforms/communities with reliable prompts, best practices, and Python-ready examples
A. Prompt templates you can reuse directly
- Defog sql-eval prompt templates (commonly referenced as “the recommended SQLCoder prompt”). (GitHub)
- Anthropic prompt library (“SQL sorcerer”): a concrete text-to-SQL prompt pattern you can adapt even if you don’t use Claude. (Claude)
B. Python frameworks with proven SQL-agent patterns
- LangChain SQL Agent docs: outlines a practical sequence (table discovery → pick relevant tables → fetch schemas → generate → double-check → execute). (LangChain Docs)
- LlamaIndex SQL query engines: examples combining structured SQL with unstructured retrieval (“auto vector” style). (LlamaIndex)
- Hugging Face smolagents text-to-SQL example: emphasizes why naive “single-shot SQL” is brittle and shows an agent-style approach. (Hugging Face)
C. Provider/community cookbooks (especially relevant to your Groq usage)
- Groq community guide: JSON mode + function calling for SQL (predictable structured outputs; DuckDB example but transferable). (Groq Community)
- Groq API cookbook: includes “Text to SQL with JSON Mode” and “Verified SQL Function Calling” examples. (GitHub)
D. End-to-end open-source projects (useful for architecture patterns)
- Dataherald: NL→SQL engine aimed at enterprise QA over relational data. (GitHub)
- Vanna: “agentic retrieval” approach for text-to-SQL, popular Python project. (GitHub)
- DB-GPT: broader “AI native data app” framework; includes Text2SQL components and fine-tuning docs. (GitHub)
E. Where implementation pitfalls show up first (high-signal)
- Hugging Face model discussions (prompt templates, chat formatting issues, dialect confusion). (Hugging Face)
- Benchmark repos (Spider2 tooling, “tool-call Spider-Agent”, reproducibility notes). (GitHub)
5) A concrete “minimum robust” Python design (fits your stack)
-
Intent router (Groq 70B)
Output JSON: {action: ask|query, clarifying_question?, constraints?}
-
Schema/value retrieval (code)
- relevant tables/columns only
- join hints
- sample distinct values for likely filters
-
SQL generation (local SQL specialist)
Output SQL only (no markdown)
-
SQL validation (SQLGlot + allowlists) (GitHub)
-
Execute with read-only credentials + limits
-
Repair loop (local SQL specialist)
Provide DB error + minimal schema slice; retry 1–3 times
-
Narration (Groq 70B or local Qwen 30B)
Summarize only the returned rows; include rowcount; handle empty results deterministically.
6) “Best model” shortlist to try first (for your exact scenario)
- Primary local SQL model: Snowflake Arctic-Text2SQL-R1-7B (Hugging Face)
- Fast local first-pass: distil-labs Distil-Qwen3-4B-Text2SQL (Jan 2026) (Hugging Face)
- Strong baseline / lots of prompt guidance: defog/sqlcoder-7b-2 (Hugging Face)
- Keep Groq LLaMA 70B for: orchestration + clarifications + result narration (and use JSON mode / tool calling patterns). (Groq Community)