shreyash-pandey-katni

SQLForge-Mistral-7B-QLoRA

README

License: apache-2.0

Results

Evaluated on a held-out internal text-to-SQL test set (500 examples, schema-aware prompt):

Table with columns: Model, Exact Set Match, Valid SQL
Model	Exact Set Match	Valid SQL
`mistralai/Mistral-7B-v0.3` (base)	9.2%	86.2%
SQLForge-Mistral-7B-QLoRA	87.0%	97.4%

A 9.5× improvement in exact-match accuracy and a +11.2 pp lift in syntactic validity, delivered by a 336 MB LoRA adapter.

Usage

python

import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel BASE  = "mistralai/Mistral-7B-v0.3"ADAPT = "shreyash-pandey-katni/SQLForge-Mistral-7B-QLoRA" tok = AutoTokenizer.from_pretrained(BASE)model = AutoModelForCausalLM.from_pretrained(    BASE,    torch_dtype=torch.bfloat16,    device_map="auto",)model = PeftModel.from_pretrained(model, ADAPT)model.eval() schema   = "CREATE TABLE employees (id INT, name TEXT, dept TEXT, salary REAL);"question = "List the names of employees in the Engineering department earning over 100000." prompt = f"""[INST] You are an expert SQL assistant. Given the schema and question, write a single SQL query. Schema:{schema} Question: {question} [/INST]""" inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

4-bit inference (single 12 GB GPU)

python

from transformers import BitsAndBytesConfig bnb = BitsAndBytesConfig(    load_in_4bit=True,    bnb_4bit_quant_type="nf4",    bnb_4bit_compute_dtype=torch.bfloat16,)model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(model, ADAPT)

Training Details

Base model: mistralai/Mistral-7B-v0.3
Method: QLoRA (4-bit NF4 base, BF16 LoRA adapters) via trl.SFTTrainer
Datasets:
- spider — cross-domain text-to-SQL
- b-mc2/sql-create-context — schema-grounded SQL
- Combined: 90,719 train / 3,929 val rows
Prompt format: [INST] … [/INST] (Mistral instruction template). Loss is masked to apply only on tokens after [/INST] (the SQL response).
Max sequence length: 512 tokens

LoRA configuration

Table with columns: Setting, Value
Setting	Value
Rank `r`	64
Alpha	16 (scale = α/r = 0.25, conservative — preserves reasoning)
Dropout	0.05
RSLoRA	enabled (1/√r scaling for r=64 stability)
Target modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
Bias	none

Training hyperparameters

Table with columns: Setting, Value
Setting	Value
Epochs	3
Effective batch size	8 (1 × 8 grad-accum)
Learning rate	2e-4
LR scheduler	cosine
Warmup ratio	0.03
Precision	BF16
Gradient checkpointing	enabled
Optimizer	AdamW (transformers default)
Checkpoint	`checkpoint-34020` (final)

Hardware

GPU: 1× NVIDIA RTX 3080 Ti (12 GB VRAM)
Peak VRAM: ~8.25 GB (4-bit NF4 base + BF16 adapters + grad-ckpt)
Training time: ~4–8 hours

Limitations

Schema sensitivity: trained with explicit CREATE TABLE schemas in the prompt; performance degrades without schema context.
Single-query bias: optimized for single SELECT statements; multi-statement scripts, DDL, and procedural SQL are out of scope.
Dialect: training data is primarily SQLite/ANSI; vendor-specific syntax (T-SQL, PL/pgSQL, BigQuery functions) may not generalize.
English-only: prompts and questions are English; non-English NL queries untested.
No execution safety: generated SQL should be sandboxed/parameterized before running against production databases.

Evaluation

The metrics above use exact_set_match (gold and predicted normalized SQL match exactly as token sets) and valid_sql_pct (predicted SQL parses with sqlparse/sqlite3). See evaluate_sql.py in the training repository for the full eval harness.

Files

Table with columns: File, Purpose
File	Purpose
`adapter_model.safetensors`	LoRA weights (~336 MB)
`adapter_config.json`	PEFT/LoRA configuration
`tokenizer.json` / `tokenizer_config.json`	Mistral tokenizer
`optimizer.pt`, `scheduler.pt`, `rng_state.pth`, `training_args.bin`, `trainer_state.json`	Training-state snapshot (resume-capable)

License

Released under Apache 2.0, the same license as the base model mistralai/Mistral-7B-v0.3.

Citation

bibtex

@misc{sqlforge_mistral7b_qlora_2026,  title  = {SQLForge-Mistral-7B-QLoRA: A QLoRA Text-to-SQL Adapter for Mistral-7B-v0.3},  author = {Shreyash Pandey Katni},  year   = {2026},  url    = {https://huggingface.co/shreyash-pandey-katni/SQLForge-Mistral-7B-QLoRA}}

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Model Details

Model Provider

shreyash-pandey-katni

Model Tree

Base

mistralai/Mistral-7B-v0.3

Adapter

this model

Input Modalities

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Output Modalities

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README

License: apache-2.0

Results

Evaluated on a held-out internal text-to-SQL test set (500 examples, schema-aware prompt):

Table with columns: Model, Exact Set Match, Valid SQL
Model	Exact Set Match	Valid SQL
`mistralai/Mistral-7B-v0.3` (base)	9.2%	86.2%
SQLForge-Mistral-7B-QLoRA	87.0%	97.4%

A 9.5× improvement in exact-match accuracy and a +11.2 pp lift in syntactic validity, delivered by a 336 MB LoRA adapter.

Usage

python

import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel BASE  = "mistralai/Mistral-7B-v0.3"ADAPT = "shreyash-pandey-katni/SQLForge-Mistral-7B-QLoRA" tok = AutoTokenizer.from_pretrained(BASE)model = AutoModelForCausalLM.from_pretrained(    BASE,    torch_dtype=torch.bfloat16,    device_map="auto",)model = PeftModel.from_pretrained(model, ADAPT)model.eval() schema   = "CREATE TABLE employees (id INT, name TEXT, dept TEXT, salary REAL);"question = "List the names of employees in the Engineering department earning over 100000." prompt = f"""[INST] You are an expert SQL assistant. Given the schema and question, write a single SQL query. Schema:{schema} Question: {question} [/INST]""" inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel BASE  = "mistralai/Mistral-7B-v0.3"ADAPT = "shreyash-pandey-katni/SQLForge-Mistral-7B-QLoRA" tok = AutoTokenizer.from_pretrained(BASE)model = AutoModelForCausalLM.from_pretrained(    BASE,    torch_dtype=torch.bfloat16,    device_map="auto",)model = PeftModel.from_pretrained(model, ADAPT)model.eval() schema   = "CREATE TABLE employees (id INT, name TEXT, dept TEXT, salary REAL);"question = "List the names of employees in the Engineering department earning over 100000." prompt = f"""[INST] You are an expert SQL assistant. Given the schema and question, write a single SQL query. Schema:{schema} Question: {question} [/INST]""" inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

4-bit inference (single 12 GB GPU)

python

from transformers import BitsAndBytesConfig bnb = BitsAndBytesConfig(    load_in_4bit=True,    bnb_4bit_quant_type="nf4",    bnb_4bit_compute_dtype=torch.bfloat16,)model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(model, ADAPT)

from transformers import BitsAndBytesConfig bnb = BitsAndBytesConfig(    load_in_4bit=True,    bnb_4bit_quant_type="nf4",    bnb_4bit_compute_dtype=torch.bfloat16,)model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(model, ADAPT)

Training Details

Base model: mistralai/Mistral-7B-v0.3
Method: QLoRA (4-bit NF4 base, BF16 LoRA adapters) via trl.SFTTrainer
Datasets:
- spider — cross-domain text-to-SQL
- b-mc2/sql-create-context — schema-grounded SQL
- Combined: 90,719 train / 3,929 val rows
Prompt format: [INST] … [/INST] (Mistral instruction template). Loss is masked to apply only on tokens after [/INST] (the SQL response).
Max sequence length: 512 tokens

LoRA configuration

Table with columns: Setting, Value
Setting	Value
Rank `r`	64
Alpha	16 (scale = α/r = 0.25, conservative — preserves reasoning)
Dropout	0.05
RSLoRA	enabled (1/√r scaling for r=64 stability)
Target modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
Bias	none

Training hyperparameters

Table with columns: Setting, Value
Setting	Value
Epochs	3
Effective batch size	8 (1 × 8 grad-accum)
Learning rate	2e-4
LR scheduler	cosine
Warmup ratio	0.03
Precision	BF16
Gradient checkpointing	enabled
Optimizer	AdamW (transformers default)
Checkpoint	`checkpoint-34020` (final)

Hardware

GPU: 1× NVIDIA RTX 3080 Ti (12 GB VRAM)
Peak VRAM: ~8.25 GB (4-bit NF4 base + BF16 adapters + grad-ckpt)
Training time: ~4–8 hours

Limitations

Schema sensitivity: trained with explicit CREATE TABLE schemas in the prompt; performance degrades without schema context.
Single-query bias: optimized for single SELECT statements; multi-statement scripts, DDL, and procedural SQL are out of scope.
Dialect: training data is primarily SQLite/ANSI; vendor-specific syntax (T-SQL, PL/pgSQL, BigQuery functions) may not generalize.
English-only: prompts and questions are English; non-English NL queries untested.
No execution safety: generated SQL should be sandboxed/parameterized before running against production databases.

Evaluation

Files

Table with columns: File, Purpose
File	Purpose
`adapter_model.safetensors`	LoRA weights (~336 MB)
`adapter_config.json`	PEFT/LoRA configuration
`tokenizer.json` / `tokenizer_config.json`	Mistral tokenizer
`optimizer.pt`, `scheduler.pt`, `rng_state.pth`, `training_args.bin`, `trainer_state.json`	Training-state snapshot (resume-capable)

License

Released under Apache 2.0, the same license as the base model mistralai/Mistral-7B-v0.3.

Citation

bibtex

@misc{sqlforge_mistral7b_qlora_2026,  title  = {SQLForge-Mistral-7B-QLoRA: A QLoRA Text-to-SQL Adapter for Mistral-7B-v0.3},  author = {Shreyash Pandey Katni},  year   = {2026},  url    = {https://huggingface.co/shreyash-pandey-katni/SQLForge-Mistral-7B-QLoRA}}

@misc{sqlforge_mistral7b_qlora_2026,  title  = {SQLForge-Mistral-7B-QLoRA: A QLoRA Text-to-SQL Adapter for Mistral-7B-v0.3},  author = {Shreyash Pandey Katni},  year   = {2026},  url    = {https://huggingface.co/shreyash-pandey-katni/SQLForge-Mistral-7B-QLoRA}}