Upload final Llama 3.2 3B Instruct NL2SQL LoRA adapter

.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

@@ -0,0 +1,133 @@
+---
+base_model: meta-llama/Llama-3.2-3B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- peft
+- lora
+- text-to-sql
+- nl2sql
+- spider
+- sqlite
+---
+
+# Llama 3.2 3B Instruct NL2SQL LoRA
+
+This repository contains the final LoRA adapter for the Llama 3.2 3B Instruct model line used in a master's thesis project on local large language models for NL2SQL generation.
+
+The adapter generates SQLite queries from natural-language questions and a relational database schema. It must be loaded together with the corresponding base model.
+
+## Base model
+
+- Model: `meta-llama/Llama-3.2-3B-Instruct`
+- Revision: `0cb88a4f764b7a12671c53f0838cd831a0843b95`
+- Access to the base model may require acceptance of Meta's license terms on Hugging Face.
+
+## Adapter
+
+- Method: LoRA supervised fine-tuning
+- LoRA rank: 8
+- LoRA alpha: 16
+- LoRA dropout: 0.05
+- Target modules: all suitable linear modules
+- Quantization during training: none
+- Maximum training sequence length: 2,048 tokens
+- Best checkpoint: `checkpoint-509`
+- The published root adapter corresponds to the selected best checkpoint.
+
+SHA-256 of `adapter_model.safetensors`:
+
+`fcd4241f7a2e8e0388f13f0dd9517486cbee43fc3169c983a54e7b716c0e502d`
+
+## Training configuration
+
+- Training examples: 25,000
+- Spider Train examples: 6,960
+- SQL Create Context examples: 18,040
+- Validation set: MixedVal2500-v2
+- Validation examples: 2,500
+- Learning rate: `1e-4`
+- Scheduler: constant
+- Train batch size: 2
+- Gradient accumulation steps: 4
+- Effective batch size: 8
+- Seed: 42
+- Maximum epochs: 5
+- Early stopping patience: 2
+- Early stopping threshold: 0.001
+- Precision: FP16
+- Gradient checkpointing: enabled
+- Attention implementation: FlashAttention 2
+
+Spider Dev was not used for training, validation, early stopping, or checkpoint selection.
+
+## Evaluation
+
+The final adapter was evaluated on all 1,032 Spider Dev cases.
+
+Zero-shot evaluation:
+
+- Execution Match Accuracy: 61.05% (630/1,032)
+- Execution Success Rate: 86.82% (896/1,032)
+- Maximum input length: 2,048 tokens
+- Maximum generated tokens: 256
+
+The corresponding starting model achieved an Execution Match Accuracy of 55.04% (568/1,032) under the same zero-shot evaluation condition.
+
+## Intended prompt behavior
+
+The model is instructed to return only a valid SQLite query:
+
+- no explanation
+- no Markdown
+- no comments
+- no unnecessary tables or columns
+- only `SELECT` or `WITH` queries
+- output terminated with a semicolon
+
+The native Llama chat template and the exact project-specific prompt construction are documented in the accompanying GitHub repository.
+
+## Loading
+
+~~~python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model_id = "meta-llama/Llama-3.2-3B-Instruct"
+adapter_id = "mehmet1899/llama32-3b-instruct-nl2sql-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model_id,
+    revision="0cb88a4f764b7a12671c53f0838cd831a0843b95",
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    revision="0cb88a4f764b7a12671c53f0838cd831a0843b95",
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+~~~
+
+## Reproducibility
+
+Code, training and evaluation configurations, environment information, run manifests, and result summaries are available at:
+
+`https://github.com/md181099/nl2sql-masterthesis`
+
+The files `training_metadata.json`, `training_history.csv`, and `training_history.jsonl` provide additional training provenance.
+
+## Limitations
+
+- The adapter was evaluated primarily on the Spider benchmark and SQLite databases.
+- Performance on other database systems or unseen schema conventions is not guaranteed.
+- Access to the base model is governed by Meta's model license.
+- Execution Match depends on the database contents and the execution-based evaluation procedure.
+- The model may still generate invalid, incomplete, or semantically incorrect SQL.
+- The adapter should not be used to execute unrestricted queries against production databases without validation and access controls.

SHA256SUMS

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+fcd4241f7a2e8e0388f13f0dd9517486cbee43fc3169c983a54e7b716c0e502d  adapter_model.safetensors
+bcfbdd3b4b74206c9a61aff857b91e32cfe6f54dd329eb134eccf566b2be8c41  README.md
+5816fce10444e03c2e9ee1ef8a4a1ea61ae7e69e438613f3b17b69d0426223a4  chat_template.jinja
+58d954db45152000b3596db19f22648213abd8a65893610380e527b7c87802b6  tokenizer_config.json
+6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b  tokenizer.json
+f5365d265fe999047a78dc0ebb94d8deb563eabee0ccdadc4de149f280ceb7ea  training_metadata.json
+5947a547d51049836504823fbc88b1b621376614e798654b0bf82240d3373f69  training_history.csv
+2e40d8195699ea3b1a158430527f4bbd8468c922c004670d94f31cfc4e77c71b  training_history.jsonl

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.2-3B-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": 16,
+  "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": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "q_proj",
+    "o_proj",
+    "gate_proj",
+    "k_proj",
+    "v_proj",
+    "down_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:fcd4241f7a2e8e0388f13f0dd9517486cbee43fc3169c983a54e7b716c0e502d
+size 48679352

chat_template.jinja

@@ -0,0 +1,93 @@
+{{- 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 %}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
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+size 17209920

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}

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