Update README.md

README.md

@@ -1,210 +1,129 @@
 ---
-base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
+license: apache-2.0
 tags:
-- base_model:adapter:unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
+  - text-to-sql
+  - llama3
+  - lora
+  - sql-generation
+  - code-generation
+library_name: transformers
+base_model: unsloth/Meta-Llama-3.1-8B
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# Llama3 SQL Translator
 
-<!-- Provide a quick summary of what the model is/does. -->
+**Llama3 SQL Translator** is a LoRA fine-tuned version of the 8B parameter Llama 3.1 model. It is designed to translate natural language database queries into executable SQL statements and provide human-readable explanations. The model streamlines query generation for structured databases and enables non-technical users to interact with relational data more effectively.
 
+## Table of Contents
 
+1. [Model Details](#model-details)  
+2. [Intended Uses](#intended-uses)  
+3. [Limitations & Warnings](#limitations--warnings)  
+4. [Training Overview](#training-overview)  
+5. [Evaluation](#evaluation)  
+6. [Usage Example](#usage-example)  
+7. [Technical Specifications](#technical-specifications)  
+8. [Citation & Contact](#citation--contact)
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
+- **Model Type:** Causal language model
+- **Architecture:** Llama 3.1 (8B parameters)
+- **Fine-Tuning Method:** Parameter-efficient fine-tuning (LoRA)
+- **Base Model:** unsloth/Meta-Llama-3.1-8B
+- **Language:** English
+- **Tokenizer:** Llama 3 tokenizer (compatible with Meta's original)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Intended Uses
 
-#### Preprocessing [optional]
+### Primary Use
+- Translating natural language prompts into valid SQL queries.
+- Providing explanations of the generated SQL logic.
 
-[More Information Needed]
+### Example Input
+```text
+Database schema: CREATE TABLE employees (id INT, name TEXT, salary FLOAT);
+Prompt: List all employees with salary over 50000.
+```
 
+### Example Output
+```text
+SQL: SELECT name FROM employees WHERE salary > 50000;
+Explanation: This query retrieves all employee names where the salary is greater than 50000.
+```
 
-#### Training Hyperparameters
+### Not Intended For
+- General chat, Q&A, or non-database related tasks.
+- Use without human review in critical systems or production databases.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+## Limitations & Warnings
 
-#### Speeds, Sizes, Times [optional]
+- **Schema Dependency:** The model relies heavily on accurate and complete schema descriptions.
+- **SQL Safety:** The output SQL should not be executed without manual validation. Injection risks must be mitigated.
+- **Complex Queries:** Deeply nested subqueries, advanced joins, or vendor-specific SQL dialects may produce suboptimal results.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+## Training Overview
 
-[More Information Needed]
+- The model was trained on a large-scale synthetic dataset containing pairs of natural language instructions, database schemas, corresponding SQL queries, and their step-by-step explanations. The dataset covers a wide range of relational data scenarios and query types, including filtering, aggregation, joins, and nested logic.
+- Fine-tuned on a single A100 GPU using:
+  - `max_seq_length=1024`
+  - `batch_size=2`, `gradient_accumulation_steps=2`
+  - LoRA with 4-bit quantization
+  - `packing=True` to maximize throughput
+  - Trained for 1 epoch (~5 hours)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+| Metric                  | Result         |
+|-------------------------|----------------|
+| SQL compilation success | > 95%          |
+| Manual output quality   | ~90%+          |
+| Explanation clarity     | High           |
 
-**BibTeX:**
+*Note: Evaluation was based on random sampling and manual review. Formal benchmarks will be added later.*
 
-[More Information Needed]
+## Usage Example
 
-**APA:**
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-[More Information Needed]
+model_id = "happyhackingspace/llama3-sql-translator"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-## Glossary [optional]
+prompt = """Below is an instruction that describes a task, paired with an input that provides further context.
+Write a response that appropriately completes the request.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Instruction
+Database schema: CREATE TABLE sales (id INT, product TEXT, price FLOAT);
 
-[More Information Needed]
+### Input:
+Prompt: Show all products priced over 100.
 
-## More Information [optional]
+### Response:"""
 
-[More Information Needed]
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-## Model Card Authors [optional]
+## Technical Specifications
 
-[More Information Needed]
+- **Architecture:** Llama 3.1 - 8B
+- **Quantization:** 4-bit via bitsandbytes
+- **Fine-tuning:** LoRA
+- **Frameworks:** Transformers, TRL, PEFT, Unsloth
 
-## Model Card Contact
+## Citation & Contact
 
-[More Information Needed]
-### Framework versions
+```bibtex
+@misc{llama3_sql_translator_2025,
+  title = {Llama3 SQL Translator},
+  author = {happyhackingspace},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/happyhackingspace/llama3-sql-translator}}
+}
+```
 
-- PEFT 0.16.0
+**Contact:** For questions or contributions, feel free to open an issue on the Hugging Face model page.