Vedant3907
/

Text-to-Sql-llama3.1-8B

@@ -4,199 +4,105 @@ tags:
 - unsloth
 - trl
 - sft
 ---
-# 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. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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]

 - unsloth
 - trl
 - sft
+datasets:
+- Clinton/Text-to-sql-v1
+language:
+- en
+base_model:
+- unsloth/Meta-Llama-3.1-8B
+pipeline_tag: text-generation
 ---
+# Model Card for `Vedant3907/Text-to-Sql-llama3.1-8B`
 ### Model Description
+This model is a fine-tuned version of **`unsloth/Meta-Llama-3.1-8B`** optimized for **Text-to-SQL generation** tasks. The fine-tuning was done using the **Unsloth library** with LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning. The training data consists of the first 5000 rows of the **Clinton/Text-to-sql-v1** dataset.
+- **Developed by**: Vedant Rajpurohit
+- **Model type**: Causal Language Model
+- **Language(s)**: English
+- **Fine-tuned from model**: `unsloth/Meta-Llama-3.1-8B`
+- **Model size**: 8.03B parameters
+- **Precision**: BF16
 ### Direct Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+# Load the model and tokenizer from the Hugging Face Hub
+model_name = "Vedant3907/Text-to-Sql-llama3.1-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
+model.eval()
+# Define your test prompt
+sql_prompt = """Below are SQL table schemas paired with instruction that describes a task.
+Using valid SQLite, write a response that appropriately completes the request for the provided tables.
+### Instruction: What is the 2007 result when the 2010 result was 2r, at the US Open?
+### Input: CREATE TABLE table_name_91 ( tournament VARCHAR )
+### Response:"""
+# Tokenize input
+inputs = tokenizer(sql_prompt, return_tensors="pt").to("cuda")
+# Generate SQL query
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    do_sample=True,  # Use sampling for more diverse outputs
+)
+# Decode and print the generated output
+generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Generated SQL Query:")
+print(generated_sql)
+#SELECT 2007 FROM table_name_91 WHERE 2010 = "2r" AND tournament = "us open"
+```
+## Bias, Risks, and Limitations
+- The model was only trained on first 5000 rows for 250 steps.
+- The model may generate incorrect or ambiguous SQL queries for instructions that are unclear or outside the training distribution.
 ## Training Details
+### Dataset
+- **Dataset Name**: `Clinton/Text-to-sql-v1`
+- **Rows Used**: First 5000 rows of the dataset.
 ### Training Procedure
+The model was fine-tuned using the **Unsloth library** with LoRA adapters, enabling efficient training. Below are the hyperparameters used:
+```python
+TrainingArguments(
+    per_device_train_batch_size = 2,
+    gradient_accumulation_steps = 4,
+    warmup_steps = 10,  # 4% of 250 steps
+    max_steps = 250,
+    learning_rate = 1e-4,
+    fp16 = not is_bfloat16_supported(),
+    bf16 = is_bfloat16_supported(),
+    logging_steps = 10,
+    optim = "adamw_8bit",
+    weight_decay = 0.01,
+    lr_scheduler_type = "cosine",
+    seed = 3407,
+    output_dir = "outputs",
+    report_to = "none"
+)
+```
 #### Hardware
+- Trained on google colab with its T4 GPU