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Model: Arogyasami/Llama-2-7b-text2sql-finetune Source: Original Platform
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---
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license: mit
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---
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```markdown
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# Llama-2 7B Text-to-SQL (Fine-Tuned with LoRA)
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## 📌 Model Overview
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This repository contains a fine-tuned version of Meta's **Llama-3 (8B)**, optimized specifically for the **Natural Language to SQL (Text-to-SQL)** generation task. By converting conversational English into executable SQL queries, this model is designed to bridge the gap between non-technical stakeholders and complex relational databases.
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- **Developer:** DanielMartin Arogyasami
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- **Base Model:** Meta-Llama-3-8B
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- **Task:** Text-to-SQL (Code Generation)
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- **Fine-Tuning Methodology:** Low-Rank Adaptation (LoRA) / PEFT
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- **Language:** English, SQL
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- **License:** Meta Llama 3 Community License / MIT (for fine-tuned weights)
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## 🎯 Intended Use Cases
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This model is highly specialized for deployment in regulated enterprise environments (e.g., healthcare, finance), where data sovereignty is paramount.
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- **Enterprise Data Retrieval:** Empowering business users to query databases using natural language, significantly reducing reliance on specialized SQL programmers.
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- **Agentic AI Workflows:** Serving as the SQL-generation agent within larger Retrieval-Augmented Generation (RAG) and enterprise AI architectures.
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- **Privacy-Preserving Analytics:** Allowing companies to run text-to-SQL conversions entirely on-premises or within air-gapped Virtual Private Clouds (VPCs). This ensures compliance with HIPAA and FDA CFR Part 11, as no sensitive data is transmitted to external proprietary APIs.
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## ⚙️ Technical Details & Training
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This model was trained using **Parameter-Efficient Fine-Tuning (PEFT)**. Specifically, **LoRA (Low-Rank Adaptation)** was applied to the foundational Llama-3 model. This approach adapts the foundational reasoning capabilities of Llama-3 to the strict syntax requirements of SQL generation, while significantly reducing computational overhead.
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* **Adapter:** LoRA
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* **Target Modules:** Attention weights (`q_proj`, `v_proj`)
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* **Precision:** FP16 / 4-bit Quantization (QLoRA) supported for edge-deployment.
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* **Architecture:** Auto-Regressive Transformer.
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## 🚀 How to Use (Inference)
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You can load this model and run inference using the `transformers` and `peft` libraries from Hugging Face.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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# Load base model and tokenizer
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base_model_id = "meta-llama/Meta-Llama-3-8B"
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adapter_id = "Arogyasami/Llama-3-8b-text2sql-finetune"
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tokenizer = AutoTokenizer.from_pretrained(base_model_id)
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base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float16, device_map="auto")
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# Load LoRA adapter
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model = PeftModel.from_pretrained(base_model, adapter_id)
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# Define your schema and question
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schema = "CREATE TABLE Employees (ID int, Name varchar(255), Department varchar(255), Salary int);"
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question = "What is the average salary of employees in the Sales department?"
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prompt = f"Given the following database schema:\n{schema}\n\nWrite a SQL query to answer this question: {question}\n\nSQL:"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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# Generate SQL
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## ⚠️ Limitations & Bias
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- **Deterministic Requirement:** Generative AI models can "hallucinate." While fine-tuned to minimize this, generated queries must be validated before execution, especially in clinical contexts where erroneous data retrieval could impact regulatory submissions.
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- **Schema Complexity:** While the model performs exceptionally well on standard schemas, highly complex schemas with dozens of nested joins may reduce accuracy.
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- **Security:** Always execute generated SQL in a safe, read-only environment. The model does not inherently enforce database permissions.
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## 🌍 Environmental Impact
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By utilizing LoRA instead of full-parameter fine-tuning, the computational cost and carbon footprint required to train this model were drastically reduced. Its 8B parameter size allows it to be served efficiently on consumer-grade hardware (e.g., NVIDIA RTX 4090 or A10G), democratizing advanced AI access.
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## 🤝 Acknowledgements & Citation
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- Meta for the foundational Llama-3 architecture.
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- If this model assists in your research or enterprise architecture, please cite:
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Arogyasami, DanielMartin. (2024). *Llama-3 8B Text-to-SQL (Fine-Tuned with LoRA)*. Hugging Face.
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```
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||||
"model.layers.6.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
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||||
"model.layers.6.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
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||||
"model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
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||||
"model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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||||
"model.layers.7.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
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||||
"model.layers.7.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
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||||
"model.layers.7.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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|
||||
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.norm.weight": "pytorch_model-00002-of-00002.bin"
|
||||
}
|
||||
}
|
||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "</s>",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93400
tokenizer.json
Normal file
93400
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
32
tokenizer_config.json
Normal file
32
tokenizer_config.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user