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README.md

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-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
+license: openrail
+datasets:
+- bugdaryan/spider-natsql-wikisql-instruct
+language:
+- en
+tags:
+- cod
 ---
+# Model Card for Fine-Tuned Wizard Coder SQL-Generation Model
 
-# Model Card for Model ID
+## Overview
 
-<!-- Provide a quick summary of what the model is/does. -->
+- **Model Name**: WizardCoderSQL-15B-V1.0
+- **Repository**: [GitHub Repository](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder)
+- **License**: [OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
+- **Fine-Tuned Model Name**: WizardCoderSQL-15B-V1.0
+- **Fine-Tuned Dataset**: [bugdaryan/spider-natsql-wikisql-instruct](https://huggingface.co/dataset/bugdaryan/spider-natsql-wikisql-instruct)
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+## Description
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [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 Data 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 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+This is a fine-tuned version of the Wizard Coder 15B model specifically designed for SQL generation tasks. The model has been fine-tuned on the [bugdaryan/spider-natsql-wikisql-instruct](https://huggingface.co/dataset/bugdaryan/spider-natsql-wikisql-instruct) dataset to empower it with the ability to generate SQL queries based on natural language instructions.
 
+## Model Details
 
+- **Base Model**: Wizard Coder 15B
+- **Fine-Tuned Model Name**: WizardCoderSQL-15B-V1.0
+- **Fine-Tuning Parameters**:
+  - QLoRA Parameters:
+    - LoRA Attention Dimension (lora_r): 64
+    - LoRA Alpha Parameter (lora_alpha): 16
+    - LoRA Dropout Probability (lora_dropout): 0.1
+  - bitsandbytes Parameters:
+    - Use 4-bit Precision Base Model (use_4bit): True
+    - Compute Dtype for 4-bit Base Models (bnb_4bit_compute_dtype): float16
+    - Quantization Type (bnb_4bit_quant_type): nf4
+    - Activate Nested Quantization (use_nested_quant): False
+  - TrainingArguments Parameters:
+    - Number of Training Epochs (num_train_epochs): 1
+    - Enable FP16/BF16 Training (fp16/bf16): False/True
+    - Batch Size per GPU for Training (per_device_train_batch_size): 48
+    - Batch Size per GPU for Evaluation (per_device_eval_batch_size): 4
+    - Gradient Accumulation Steps (gradient_accumulation_steps): 1
+    - Enable Gradient Checkpointing (gradient_checkpointing): True
+    - Maximum Gradient Norm (max_grad_norm): 0.3
+    - Initial Learning Rate (learning_rate): 2e-4
+    - Weight Decay (weight_decay): 0.001
+    - Optimizer (optim): paged_adamw_32bit
+    - Learning Rate Scheduler Type (lr_scheduler_type): cosine
+    - Maximum Training Steps (max_steps): -1
+    - Warmup Ratio (warmup_ratio): 0.03
+    - Group Sequences into Batches with Same Length (group_by_length): True
+    - Save Checkpoint Every X Update Steps (save_steps): 0
+    - Log Every X Update Steps (logging_steps): 25
+  - SFT Parameters:
+    - Maximum Sequence Length (max_seq_length): 500
+
+## Performance
+
+- **Fine-Tuned Model Metrics**: (Provide any relevant evaluation metrics if available)
+
+## Dataset
+
+- **Fine-Tuned Dataset**: [bugdaryan/spider-natsql-wikisql-instruct](https://huggingface.co/dataset/bugdaryan/spider-natsql-wikisql-instruct)
+- **Dataset Description**: This dataset contains natural language instructions paired with SQL queries. It serves as the training data for fine-tuning the Wizard Coder model for SQL generation tasks.
+
+## Model Card Information
+
+- **Maintainer**: Spartak Bughdaryan
+- **Contact**: bugdaryan@gmail.com
+- **Date Created**: September 15, 2023
+- **Last Updated**: September 15, 2023
+
+## Usage
+
+To use this fine-tuned model for SQL generation tasks, you can load it using the Hugging Face Transformers library in Python. Here's an example of how to use it:
+
+```python
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    pipeline
+)
+import torch
+
+model_name = 'bugdaryan/WizardCoderSQL-15B-V1.0'
+
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+pipe = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+tables = "CREATE TABLE sales ( sale_id number PRIMARY KEY, product_id number, customer_id number, salesperson_id number, sale_date DATE, quantity number, FOREIGN KEY (product_id) REFERENCES products(product_id), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (salesperson_id) REFERENCES salespeople(salesperson_id)); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number, FOREIGN KEY (product_id) REFERENCES products(product_id)); CREATE TABLE customers ( customer_id number PRIMARY KEY, name text, address text ); CREATE TABLE salespeople ( salesperson_id number PRIMARY KEY, name text, region text ); CREATE TABLE product_suppliers ( supplier_id number PRIMARY KEY, product_id number, supply_price number );"
+
+question = 'Find the salesperson who made the most sales.'
+
+prompt = f"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Convert text to SQLite query: {question} {tables} ### Response:"
+
+ans = pipe(prompt, max_new_tokens=200)
+print(ans[0]['generated_text'])
+
+```