Initial release: postgres-llm-qlora

QLoRA adapter for PostgreSQL SQL/text-to-SQL. Base: Qwen/Qwen2.5-Coder-3B-Instruct. Trained on neurondb/neurondb-postgresql-sql (37k steps, 3 epochs). Includes adapter weights, tokenizer, and model card.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,129 @@
----
-license: apache-2.0
----
+---
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+library_name: peft
+tags:
+  - sql
+  - postgresql
+  - text-to-sql
+  - code
+  - lora
+  - qlora
+  - qwen2
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+# postgres-llm-qlora
+
+QLoRA fine-tuned adapter for **PostgreSQL SQL / text-to-SQL** on [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct). Trained on the [neurondb/neurondb-postgresql-sql](https://huggingface.co/datasets/neurondb/neurondb-postgresql-sql) dataset. Use this adapter with the base model to generate PostgreSQL-compatible SQL from natural language instructions. Use this adapter with the base model to generate PostgreSQL-compatible SQL from natural language instructions.
+
+## Model details
+
+- **Base model:** [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
+- **Training dataset:** [neurondb/neurondb-postgresql-sql](https://huggingface.co/datasets/neurondb/neurondb-postgresql-sql) (~212k rows: question, schema, sql, with sources including PostgreSQL regression tests, docs, contrib, pgTAP, plpgsql, sql_create_context, community SQL datasets)
+- **Method:** QLoRA (4-bit base + LoRA), rank 64, alpha 128
+- **Training:** 37,299 steps, 3 epochs on the dataset train split
+- **Final metrics:** ~0.34 loss, ~89% mean token accuracy
+
+## Usage
+
+### With transformers + PEFT (recommended on 8GB GPU: load base in 4-bit)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+
+adapter_path = "YOUR_USER/postgres-llm-qlora"  # or local path
+base_model = "Qwen/Qwen2.5-Coder-3B-Instruct"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(model, adapter_path)
+tokenizer = AutoTokenizer.from_pretrained(adapter_path, trust_remote_code=True)
+model.eval()
+
+prompt = "Create a table users with columns id (serial primary key), name (text), email (text);"
+text = f"### Instruction:\n{prompt}\n\n### Response:\n"
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    out = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+response = tokenizer.decode(out[0][inputs["input_ids"].size(1):], skip_special_tokens=True)
+print(response)
+```
+
+### With pipeline (after loading model as above)
+
+```python
+# After loading model + tokenizer as above
+from transformers import pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=model.device)
+out = pipe("### Instruction:\nList all tables.\n\n### Response:\n", max_new_tokens=128, do_sample=False)
+print(out[0]["generated_text"])
+```
+
+## Prompt format
+
+Use the same instruction format as in training:
+
+```
+### Instruction:
+<your natural language or task>
+
+### Response:
+```
+
+The model will generate SQL (and optionally an explanation) after `### Response:`.
+
+## Training data
+
+This model was fine-tuned on **[neurondb/neurondb-postgresql-sql](https://huggingface.co/datasets/neurondb/neurondb-postgresql-sql)** (~212k instruction pairs). The dataset includes:
+
+- **Sources:** PostgreSQL regression tests, official docs, contrib modules, pgTAP tests, PL/pgSQL source, sql_create_context, community SQL datasets (e.g. Spider/WikiSQL-derived), synthetic text-to-SQL
+- **Content:** `question`, optional `schema` (DDL), `sql` (PostgreSQL/PL-pgSQL), optional `explanation`
+- **Splits:** train / validation / test; training used the train split only
+
+See the [dataset card](https://huggingface.co/datasets/neurondb/neurondb-postgresql-sql) for schema, difficulty distribution, categories, and license.
+
+## License
+
+Apache 2.0. Base model (Qwen2.5-Coder) follows its original license.
+
+## Citation
+
+If you use this adapter, please cite the **training dataset** and the base model / PEFT:
+
+**Dataset:**
+```bibtex
+@dataset{neurondb_postgresql_sql_2026,
+  title={NeuronDB PostgreSQL SQL & PL/pgSQL Instruction Dataset},
+  author={NeuronDB Team},
+  year={2026},
+  url={https://huggingface.co/datasets/neurondb/neurondb-postgresql-sql},
+}
+```
+
+**PEFT:**
+```bibtex
+@software{peft,
+  title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning},
+  author = {Hugging Face},
+  year = {2023},
+  url = {https://github.com/huggingface/peft}
+}
+```

adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-Coder-3B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "up_proj",
+    "gate_proj",
+    "o_proj",
+    "down_proj",
+    "v_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2e5a8710d4cd6c53c6fc851e8e027b0f7f757f0166bc606a71458e4ca2fe288
+size 479005064

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

tokenizer.json

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+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

@@ -0,0 +1,29 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_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|>"
+  ],
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|im_end|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}