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Model: A-Kishore/llama-3.2-3b-text2sql
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
+tags:
+  - text-generation-inference
+  - transformers
+  - unsloth
+  - llama
+  - text-to-sql
+  - peft
+  - lora
+license: apache-2.0
+language:
+  - en
+datasets:
+  - gretelai/synthetic_text_to_sql
+---
+
+# 🔮 Llama-3.2-3B-Instruct Text-to-SQL
+
+A fine-tuned version of [`unsloth/Llama-3.2-3B-Instruct-bnb-4bit`](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit) for generating SQL queries from natural language questions and database DDL schemas.
+
+[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Finetuned with Unsloth](https://img.shields.io/badge/Finetuned%20with-Unsloth-blue.svg)](https://github.com/unslothai/unsloth)
+
+---
+
+## Model Summary
+
+| Attribute | Value |
+|:---|:---|
+| **Base Model** | `unsloth/Llama-3.2-3B-Instruct-bnb-4bit` |
+| **Task** | Text-to-SQL (natural language → SQL) |
+| **Fine-Tuning Method** | LoRA (PEFT) via Unsloth + TRL |
+| **Training Dataset** | `gretelai/synthetic_text_to_sql` (50k samples) |
+| **Trainable Parameters** | ~0.75% of base model |
+| **Export Format** | Merged FP16 (`merged_16bit`) |
+| **License** | Apache-2.0 + Meta Llama 3 Community License |
+| **Developer** | A-Kishore |
+
+---
+
+## Evaluation Results
+
+Evaluated on the first 200 samples of the `gretelai/synthetic_text_to_sql` test split using greedy decoding. ROUGE F-measures reported.
+
+| Model | ROUGE-1 | ROUGE-2 | ROUGE-L |
+|:---|:---:|:---:|:---:|
+| Base Model (`unsloth/Llama-3.2-3B-Instruct-bnb-4bit`) | 0.2908 | 0.2016 | 0.2651 |
+| Fine-Tuned (`A-Kishore/llama-3.2-3b-text2sql`) | **0.8486** | **0.7232** | **0.8151** |
+| **Improvement** | **+191.82%** | **+258.73%** | **+207.47%** |
+
+**Metric interpretation:**
+- **ROUGE-1** (unigram overlap) reflects accurate retrieval of schema identifiers and SQL keywords.
+- **ROUGE-2** (bigram overlap) captures structural alignment of consecutive SQL constructs (e.g. `GROUP BY`, `ORDER BY`).
+- **ROUGE-L** (longest common subsequence) tracks overall query flow including nested clauses and join ordering.
+
+> **Note:** ROUGE measures lexical overlap, not SQL executability. A query may score slightly lower due to stylistic differences (alias names, join ordering) while still being functionally equivalent. See [Limitations](#limitations).
+
+---
+
+## How to Use
+
+The model weights are fully merged in 16-bit precision and load with standard `transformers` or `unsloth`.
+
+### Prompt Format
+
+Always use this exact template — the model was trained on it:
+
+```
+###TASK
+Generate the SQL query to answer the following question
+
+### Database Schema
+{sql_context}
+
+### Question
+{sql_prompt}
+
+### SQL Query
+```
+
+### (a) Standard `transformers`
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "A-Kishore/llama-3.2-3b-text2sql"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+prompt = """###TASK
+Generate the SQL query to answer the following question
+
+### Database Schema
+{sql_context}
+
+### Question
+{sql_prompt}
+
+### SQL Query
+"""
+
+sql_context = "CREATE TABLE employees (id INT, name TEXT, department TEXT, salary REAL);"
+sql_prompt = "What is the average salary per department?"
+
+inputs = tokenizer(
+    prompt.format(sql_context=sql_context, sql_prompt=sql_prompt),
+    return_tensors="pt"
+).to("cuda")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    use_cache=True,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+sql = result.split("### SQL Query")[-1].strip()
+print(sql)
+# SELECT department, AVG(salary) FROM employees GROUP BY department;
+```
+
+### (b) Unsloth Fast Inference
+
+```python
+import torch
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="A-Kishore/llama-3.2-3b-text2sql",
+    max_seq_length=768,
+    dtype=torch.float16,
+    load_in_4bit=False,
+)
+FastLanguageModel.for_inference(model)
+
+prompt = """###TASK
+Generate the SQL query to answer the following question
+
+### Database Schema
+{sql_context}
+
+### Question
+{sql_prompt}
+
+### SQL Query
+"""
+
+sql_context = "CREATE TABLE employees (id INT, name TEXT, department TEXT, salary REAL);"
+sql_prompt = "What is the average salary per department?"
+
+inputs = tokenizer(
+    prompt.format(sql_context=sql_context, sql_prompt=sql_prompt),
+    return_tensors="pt"
+).to("cuda")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    temperature=None,
+    do_sample=False,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+sql = result.split("### SQL Query")[-1].strip()
+print(sql)
+```
+
+---
+
+## Training Details
+
+### Dataset
+
+- **Source:** [`gretelai/synthetic_text_to_sql`](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
+- **Training split:** 50,000 shuffled samples from `train`
+- **Evaluation split:** First 200 samples from `test`
+
+### LoRA Configuration
+
+| Parameter | Value |
+|:---|:---|
+| Rank (`r`) | `16` |
+| Alpha (`lora_alpha`) | `16` |
+| Target Modules | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
+| Bias | `none` |
+
+LoRA freezes the base model weights and injects trainable rank-decomposition matrices into all attention and MLP projections. Only ~0.75% of parameters are updated, dramatically reducing VRAM usage and preventing catastrophic forgetting.
+
+### Hyperparameters
+
+| Parameter | Value |
+|:---|:---|
+| Optimizer | `paged_adamw_8bit` |
+| Learning Rate | `2e-4` |
+| LR Scheduler | `linear` |
+| Warmup Steps | `5` |
+| Epochs | `1` |
+| Per-Device Batch Size | `8` |
+| Gradient Accumulation | `1` |
+| Max Sequence Length | `768` |
+| Sequence Packing | `True` |
+| Mixed Precision | `fp16` |
+| Experiment Tracking | Weights & Biases |
+
+Training was accelerated using the `unsloth` library, which provides optimized GPU kernels for 4-bit quantized training (~2× faster than standard configurations).
+
+---
+
+## Repository
+
+Training and evaluation code: [a-kishore-dev/llama-text2sql-finetune](https://github.com/a-kishore-dev)
+
+Notebooks included:
+- `Text_to_SQL_Finetuning.ipynb` — dataset prep, LoRA config, training, export
+- `evaluate_model.ipynb` — ROUGE evaluation comparing base vs fine-tuned
+
+---
+
+## Limitations
+
+- **SQL executability:** ROUGE is a lexical proxy. High ROUGE does not guarantee a query will execute or return logically correct results. A query with different aliases or reordered joins may score lower despite being equivalent.
+- **Out-of-distribution schemas:** Performance degrades on high-cardinality databases, deeply nested subqueries, or DDL patterns that diverge significantly from the training distribution.
+- **Single epoch:** The model was trained for one epoch on 50k samples. Further training may improve generalization.
+
+---
+
+## License
+
+The model adapter is released under **Apache 2.0**. The underlying base model is governed by the [Meta Llama 3 Community License Agreement](https://llama.meta.com/llama3/license/). Users must comply with both.
+
+---
+
+## Acknowledgements
+
+- **Unsloth** — optimized kernels for 4-bit training and sequence packing
+- **Hugging Face** — `trl` (`SFTTrainer`) and `transformers`
+- **Meta AI** — Llama 3.2 open weights
+- **Gretel AI** — synthetic Text-to-SQL dataset
+
+---
+
+## Author
+
+**A-Kishore** · [GitHub](https://github.com/a-kishore-dev) · [HuggingFace](https://huggingface.co/A-Kishore)

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "float16",
+    "eos_token_id": 128009,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 3072,
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 24,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "pad_token_id": 128004,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "factor": 32.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_theta": 500000.0,
+        "rope_type": "llama3"
+    },
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.6.3",
+    "use_cache": true,
+    "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.5.0"
+}

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