mervp
/

SQLGenie

@@ -1,202 +1,84 @@
 ---
 base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
 library_name: peft
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## 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
-<!-- 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 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. -->
-**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]
-### Framework versions
-- PEFT 0.14.0

 ---
 base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
 library_name: peft
+tags:
+  - text-generation
+  - sql
+  - peft
+  - lora
+  - rslora
+  - unsloth
+  - llama3
+  - instruction-tuned
+---
+# SQLGenie - LoRA Fine-Tuned LLaMA 3B for Text-to-SQL Generation
+**SQLGenie** is a lightweight LoRA adapter fine-tuned on top of Unsloth’s 4-bit LLaMA 3 (3B) model. It is designed to convert natural language instructions into valid SQL queries with minimal compute overhead, making it ideal for integrating into data-driven applications,or chat interfaces.
+it has been trained over 100K types of text based on various different domains such as Education, Technical, Health and more
 ## Model Details
 ### Model Description
+- **Developed by:** Merwin
+- **Model type:** PEFT adapter (LoRA) for Causal Language Modeling
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Fine-tuned from model:** [unsloth/llama-3.2-3b-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-3b-unsloth-bnb-4bit)
+### Model Sources
+- **Repository:** https://huggingface.co/<your-username>/SQLGenie
 ## Uses
 ### Direct Use
+This model can be directly used to generate SQL queries from natural language prompts. Example use cases include:
+- Building AI assistants for databases
+- Enhancing Query tools with NL-to-SQL capabilities
+- Automating analytics queries in various domains
 ## Bias, Risks, and Limitations
+While the model has been fine-tuned for SQL generation, it may:
+- Produce invalid SQL for a very few edge cases
+- Infer incorrect table or column names not present in prompt
+- Assume a generic SQL dialect (closer to MySQL/PostgreSQL Databases)
 ### Recommendations
+Always validate and test generated queries before execution in a production database.
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/llama-3.2-3b-unsloth-bnb-4bit",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3.2-3b-unsloth-bnb-4bit")
+model = PeftModel.from_pretrained(base_model, "mervp/SQLGenie")
+prompt = "List the customers from Canada."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+#
+OR
+#
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="mervp/SQLGenie",
+    max_seq_length=2048,
+    dtype=None,
+    # load_in_4bit=True,
+)