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Model: ihebaker10/text2sql-7b-v3-16 Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- text2sql
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- sql
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- pgvector
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- grpo
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- lora
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- safetensors
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- bf16
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---
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# text2sql-7b-v3-16
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## Model Details
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`text2sql-7b-v3-16` is an 8B-parameter causal language model fine-tuned for text-to-SQL generation. It accepts a natural-language question plus schema context and returns SQL inside the notebook's required answer format.
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- Model type: causal language model, text generation
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- Primary task: text-to-SQL
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- Base model family: `Arctic-Text2SQL-R1-7B`
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- Output format: `<think>...</think><answer>SQL</answer>`
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- Tensor format: Safetensors
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- Precision: `BF16`
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- Fine-tuning method: PEFT LoRA adapters merged into the final model artifact
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- Final artifact path used locally: `outputs/text2sql-7b-v3-16/merged-text2sql-7b-v3-16`
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## Intended Technical Use
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Use this model to generate PostgreSQL-style SQL from a natural-language question and schema description. The fine-tuning set contains both plain SQL examples and pgvector retrieval examples using `embed_query()` with vector-distance operators.
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This model is not a SQL execution engine. Generated SQL should be parsed, reviewed, and run in a controlled environment before use.
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## How to Use
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "ihebaker10/text2sql-7b-v3-16"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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messages = [
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{
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"role": "system",
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"content": "Generate SQL. Return the final SQL inside <answer>...</answer>.",
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},
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{
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"role": "user",
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"content": "Schema:\n...\n\nQuestion:\nList the latest 10 records.",
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},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=768,
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temperature=0.2,
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top_p=0.95,
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do_sample=False,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training Data
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The training data contains 10,000 text-to-SQL examples loaded from a MongoDB collection and split into 9,001 training examples and 999 validation examples.
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| Split / Type | Examples | Share |
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|---|---:|---:|
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| Train | 9,001 | 90.0% |
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| Validation | 999 | 10.0% |
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| SQL-only | 3,931 | 39.3% |
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| pgvector | 6,069 | 60.7% |
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Each record contains an identifier, `is_sql_only`, natural-language question, reference SQL query, reasoning hint, schema text, table list, and version.
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## Training Procedure
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Training used a three-stage PEFT workflow:
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| Stage | Objective | Data | Schedule |
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|---|---|---|---|
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| Stage 1a | SQL-only SFT warm-up | SQL-only examples | 1 epoch |
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| Stage 1b | Mixed SFT | Natural mixed SQL + pgvector split | 2 epochs |
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| Stage 2 | GRPO refinement | Natural mixed SQL + pgvector split | about 0.5 epoch, capped by max steps |
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The LoRA adapter was merged into the base weights before publishing the final Safetensors artifact.
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## Hyperparameters
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| Parameter | Stage 1a SFT | Stage 1b SFT | Stage 2 GRPO |
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|---|---:|---:|---:|
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| Learning rate | `1e-4` | `7e-5` | `1e-05` |
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| Scheduler | `cosine` | `cosine` | `SchedulerType.COSINE` |
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| Warmup ratio | `0.05` | `0.03` | `0.05` |
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| Per-device train batch size | `1` | `1` | `2` |
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| Gradient accumulation | `16` | `16` | `4` |
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| Effective batch size | `16` | `16` | `8` |
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| Weight decay | `0.01` | `0.01` | `0.01` |
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| Max grad norm | `1.0` | `1.0` | `1.0` |
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| Precision | `bf16` | `bf16` | `bf16` |
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| Optimizer | `adamw_torch` | `adamw_torch` | `adamw_torch` |
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| Max sequence length | `1536` | `1536` | n/a |
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| Max prompt length | n/a | n/a | `1024` |
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| Max completion length | n/a | n/a | `192` |
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| Rollouts per prompt | n/a | n/a | `2` |
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| Temperature | n/a | n/a | `0.8` |
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| Top-p | n/a | n/a | `0.95` |
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| KL beta | n/a | n/a | `0.002` |
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| Early stopping patience | n/a | n/a | `2` |
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LoRA configuration:
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| Parameter | Value |
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|---|---:|
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| Rank | `64` |
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| Alpha | `64` |
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| Dropout | `0.05` |
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| Bias | `none` |
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| Target modules | `down_proj`, `gate_proj`, `k_proj`, `o_proj`, `q_proj`, `up_proj`, `v_proj` |
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## Reward Functions
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Stage 2 used the following reward checks:
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- `format_reward`: validates the `<think>` and `<answer>` response structure.
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- `sql_syntax_reward`: rewards SQL that parses successfully.
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- `pgvector_usage_reward`: checks whether `embed_query()` is used only when expected.
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- `execution_reward`: optionally compares generated SQL results with reference SQL when `DB_URL` is configured.
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## Evaluation
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Positive class for precision, recall, and F1 is pgvector usage, meaning the generated SQL contains `embed_query()`.
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| Split | N | Format | Syntax | Use accuracy | Precision | Recall | F1 | Exact match |
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|---|---:|---:|---:|---:|---:|---:|---:|---:|
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| SQL-only | 393 | 100.0% | 99.5% | 89.8% | 0.0% | 0.0% | 0.0% | 33.1% |
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| pgvector | 606 | 100.0% | 99.0% | 73.8% | 100.0% | 73.8% | 84.9% | 11.7% |
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| combined | 999 | 100.0% | 99.2% | 80.1% | 91.8% | 73.8% | 81.8% | 20.1% |
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Stage eval losses:
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| Stage | Eval loss |
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|---|---:|
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| Stage 1a | `0.013554` |
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| Stage 1b | `0.002467` |
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| Stage 2 | `0.000403` |
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Detailed pgvector decision metrics from the saved final merged-model evaluation:
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| Split | TP | FP | FN | TN | SQL-only specificity | Embed false positive rate |
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|---|---:|---:|---:|---:|---:|---:|
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| SQL-only | 0 | 40 | 0 | 353 | 89.8% | 10.2% |
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| pgvector | 447 | 0 | 159 | 0 | 0.0% | 0.0% |
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| combined | 447 | 40 | 159 | 353 | 89.8% | 10.2% |
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Detailed saved stage metrics:
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| Stage | Train loss | Eval loss | Train runtime | Eval runtime | Train samples/s | Eval samples/s | Train steps/s | Eval steps/s | Total FLOPs |
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|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
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| Stage 1a | `2.460437` | `0.013554` | 2478s | 167.1s | 1.428 | 5.979 | 0.089 | 5.979 | `2.222e+17` |
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| Stage 1b | `0.049128` | `0.002467` | 12935s | 164.9s | 1.392 | 6.057 | 0.087 | 6.057 | `1.165e+18` |
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| Stage 2 | `0.000392` | `0.000403` | 36192s | 2772.4s | 0.124 | 0.360 | 0.016 | 0.045 | `0.000e+00` |
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## Limitations
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- SQL should be validated before execution.
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- Exact-match SQL is strict and may undercount semantically equivalent queries.
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- pgvector recall on the validation split is lower than precision, so some retrieval-style prompts may be answered as plain SQL.
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- Execution-based reward depends on `DB_URL`; without it, training optimizes formatting, SQL syntax, and pgvector usage only.
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## Technical Environment
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- Frameworks: `transformers`, `peft`, `trl`, `datasets`, `torch`
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- Attention implementation: `sdpa`
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- Gradient checkpointing: enabled
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- CUDA memory fraction in notebook: `0.98`
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- TF32 matmul: enabled
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- TensorBoard logging: enabled for all training stages
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## Environmental Impact
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Carbon emissions were not measured. Recorded training runtimes were approximately:
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| Stage | Runtime |
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|---|---:|
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| Stage 1a | 2478 seconds |
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| Stage 1b | 12935 seconds |
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| Stage 2 | 36192 seconds |
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Total recorded training runtime was approximately 14.3 hours on a local CUDA GPU.
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config.json
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209
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|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|im_end|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user