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Model: ashishsahu2008/qwen2.5-3b-text2sql
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/Qwen2.5-3B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
+tags:
+- text-to-sql
+- sql
+- qwen2
+- lora
+- qlora
+- unsloth
+- trl
+datasets:
+- b-mc2/sql-create-context
+---
+
+# Qwen2.5-3B Text-to-SQL
+
+A fine-tuned version of **Qwen2.5-3B-Instruct** that converts natural-language
+questions into SQL queries, given a database schema. Trained with LoRA/QLoRA on a
+single free GPU.
+
+- **Developed by:** ashishsahu2008
+- **Base model:** [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct)
+- **Fine-tuned on:** [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
+- **License:** apache-2.0
+- **Live demo:** https://huggingface.co/spaces/ashishsahu2008/text2sql-demo
+- **Code / write-up:** https://github.com/ashishsahu2008/fine-tuning
+
+## What it does
+
+Give it a `CREATE TABLE` schema and a plain-English question, and it returns only
+the SQL query that answers the question — no explanations or markdown, just runnable SQL.
+
+## Results
+
+Evaluated on a held-out test split (n=200) the model never saw during training.
+Both the base and fine-tuned models were scored with identical string normalization
+(lowercase, strip `;`, canonicalize quotes), so the comparison is apples-to-apples.
+
+| Model                          | Exact-match accuracy |
+|--------------------------------|:--------------------:|
+| Qwen2.5-3B-Instruct (base)     |        41.5%         |
+| **This model (fine-tuned)**    |      **72.5%**       |
+
+Only 0.96% of parameters (~30M of 3.1B) were trained via LoRA. Much of the gain
+comes from the base model learning to emit clean, executable SQL instead of wrapping
+answers in prose or markdown fences.
+
+> **Note on the metric:** exact-match undercounts semantically-correct queries that
+> are written differently (column order, aliases, whitespace). A stronger evaluation
+> would use AST comparison (`sqlglot`) or execution accuracy on a benchmark with
+> populated databases (Spider / BIRD); `sql-create-context` ships schemas only.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "ashishsahu2008/qwen2.5-3b-text2sql"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+
+schema = "CREATE TABLE employees (name VARCHAR, salary INTEGER, department VARCHAR)"
+question = "List the names of employees in Sales earning over 50000."
+
+messages = [
+    {"role": "system", "content": "You are a SQL expert. Given a database schema "
+                                   "and a question, output only the SQL query that answers it."},
+    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion: {question}"},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
+).to(model.device)
+out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Prompt format
+
+The model expects the schema and question in a single user turn, with this system prompt:
+
+```
+System:  You are a SQL expert. Given a database schema and a question, output only the SQL query that answers it.
+User:    Schema:
+         <CREATE TABLE ...>
+
+         Question: <your question>
+```
+
+## Training details
+
+- **Method:** supervised fine-tuning (SFT) with LoRA on a 4-bit quantized base (QLoRA)
+- **LoRA config:** rank 16, alpha 16, applied to attention + MLP projections
+- **Data:** 3,000-row subset of `b-mc2/sql-create-context` (2,700 train / 300 test)
+- **Hyperparameters:** 2 epochs, learning rate 2e-4, effective batch size 8
+- **Hardware:** single Colab T4, ~25 minutes
+- **Frameworks:** [Unsloth](https://github.com/unslothai/unsloth) + TRL `SFTTrainer`
+
+## Limitations
+
+- Trained on **single-table** `CREATE TABLE` schemas; complex multi-join databases
+  are out of distribution.
+- Assumes the schema is provided in the prompt — it does not know any tables you
+  don't give it.
+- SQL dialect follows the training data (broadly SQLite-compatible); it does not
+  target a specific engine's extensions.
+
+Fine-tuned with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.

chat_template.jinja

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+{%- 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 %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 70,
+    "model_type": "qwen2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.6.9",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 32768,
+  "pad_token_id": 151665,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

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tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

tokenizer_config.json

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+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
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+  "is_local": false,
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+      "normalized": false,
+      "special": true
+    }
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+  "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"
+}