NumbersStation
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nsql-llama-2-7B

@@ -29,81 +29,6 @@ The general SQL queries are the SQL subset from [The Stack](https://huggingface.
 We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
-## Evaluation Results
-We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
-### Spider Benchmark (Text-to-SQL Standard Evaluation)
-NSQL-llama-2-7B was evaluated on the Spider benchmark, the standard academic evaluation for Text-to-SQL systems.
-#### Overall Performance
-| Model | Size | Execution Accuracy | Matching Accuracy |
-|-------|------|-------------------|-------------------|
-| **NSQL-llama-2-7B** | 7B | 78.1% | **66.3%** |
-| GPT-4 | ~1.8T | 76.2% | 41.9% |
-| GPT-3.5 Chat | — | 72.8% | 44.2% |
-| Llama-2-7B (base) | 7B | 29.1% | 19.3% |
-| Llama-2-70B | 70B | 61.5% | 35.4% |
-#### Performance by Query Complexity
-| Query Type | NSQL-llama-2-7B | GPT-4 | NSQL Advantage |
-|------------|-----------------|-------|----------------|
-| **Join Queries** | **53.7%** | ~37.6% | **+43% relative** |
-| **Nested Queries** | **57.2%** | ~37.1% | **+54% relative** |
-| Simple Queries | 91.4% | Higher | GPT-4 advantage |
-#### Key Findings
-1. **Complex Query Performance:** NSQL-llama-2-7B significantly outperforms GPT-4 on complex queries:
-   - +43% improvement on Join queries
-   - +54% improvement on Nested queries
-2. **Matching Accuracy:** NSQL achieves 66.3% matching accuracy vs. GPT-4's 41.9% (+24.4 points), indicating more structurally correct SQL generation.
-3. **Efficiency:** NSQL achieves near-parity with GPT-4 on overall execution (78.10% vs 76.2%) while being ~250× smaller.
-4. **Local Deployment:** The 7B parameter size enables local deployment on commodity hardware, preserving data privacy.
-#### Why This Matters
-GPT-4 achieves marginally higher overall execution accuracy primarily through superior performance on simple single-table queries. However, enterprise SQL workloads typically involve:
-- Multiple table joins
-- Nested subqueries
-- Complex business logic
-On these complex query types, NSQL substantially outperforms GPT-4 while enabling privacy-preserving local deployment.
-### GeoQuery Benchmark
-| Model | Size | Execution Accuracy | Matching Accuracy |
-|-------|------|-------------------|-------------------|
-| NSQL-llama-2-7B | 7B | 26.5% | 30.4% |
-| GPT-4 | ~1.8T | 55.1% | 39.1% |
-*Note: GeoQuery is a narrower benchmark; Spider is the primary industry standard for Text-to-SQL evaluation.*
-### NSQL Model Family Comparison
-| Model | Size | Spider Exec | Spider Match |
-|-------|------|-------------|--------------|
-| NSQL-350M | 350M | 51.7% | 45.6% |
-| NSQL-2B | 2B | 59.3% | 53.2% |
-| NSQL-6B | 6B | 63.6% | 57.4% |
-| **NSQL-llama-2-7B** | **7B** | **78.1%** | **66.3%** |
----
-## Evaluation Methodology
-- **Benchmark:** Spider (Yu et al., 2018)
-- **Metric - Execution Accuracy:** Percentage of queries returning correct results
-- **Metric - Matching Accuracy:** Percentage of queries structurally matching ground truth
-- **Query Type Breakdown:** Join, Nested, Simple categories per Spider schema
 ## Training Procedure
 NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs.

 We evaluate our models on two text-to-SQL benchmarks: Spider and GeoQuery.
 ## Training Procedure
 NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs.