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README.md

@@ -1,89 +1,47 @@
 ---
 library_name: peft
 base_model: declare-lab/flan-alpaca-base
-datasets:
-- knowrohit07/know_sql
-license: mit
-language:
-- en
-pipeline_tag: text2text-generation
-tags:
-- sql
-- query
-- database
 ---
 
-## Model Details
-
-### Model Description
-
-This model is based on the declare-lab/flan-alpaca-base model finetuned with knowrohit07/know_sql dataset.
+# Model Card for Model ID
 
-- **Developed by:** Jonathan Jordan
-- **Model type:** FLAN Alpaca
-- **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-- **Finetuned from model:** declare-lab/flan-alpaca-base
-
-## Uses
+<!-- Provide a quick summary of what the model is/does. -->
 
-The model generates a string of SQL query based on a question and MySQL table schema. 
-You can modify the table schema to match MySQL table schema if you are using different type of SQL database (e.g. PostgreSQL, Oracle, etc).
-The generated SQL query can be run perfectly on the python SQL connection (e.g. psycopg2, mysql_connector, etc).
 
-#### Limitations
-1. The question MUST be in english
-2. Keep in mind about the difference in data type naming between MySQL and the other SQL databases
-3. The output always starts with SELECT *, you can't choose which columns to retrieve.
-4. Aggregation function is not supported
 
-### Input Example
-```python
-"""Question: what is What was the result of the election in the Florida 18 district?\nTable: table_1341598_10 (result VARCHAR, district VARCHAR)\nSQL: """
-```
-### Output Example
-```python
-"""SELECT * FROM table_1341598_10 WHERE district = "Florida 18""""
-```
-
-### How to use
-Load model
+## Model Details
 
-```python
-from peft import get_peft_config, get_peft_model, TaskType
-from peft import PeftConfig, PeftModel
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+### Model Description
 
-model_id = "jonathanjordan21/flan-alpaca-base-finetuned-lora-knowSQL"
-config = PeftConfig.from_pretrained(model_id)
-model_ = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True)
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+<!-- Provide a longer summary of what this model is. -->
 
-model = PeftModel.from_pretrained(model_, model_id)
-model = get_peft_model(model,config)
-```
 
-Model inference
 
-```python
-question = "server of user id 11 with status active and server id 10"
-table = "table_name_77 ( user id INTEGER, status VARCHAR, server id INTEGER )"
+- **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]
 
-test = f"""Question: {question}\nTable: {table}\nSQL: """
+### Model Sources [optional]
 
-p = tokenizer(test, return_tensors='pt')
+<!-- Provide the basic links for the model. -->
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-print("output :", tokenizer.batch_decode(model.to(device).generate(**p.to(device),max_new_tokens=50),skip_special_tokens=True)[0])
+## 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. -->
 
-## Performance
+### Direct Use
 
-### Speed Performance
-The model inference takes about 2-3 seconds to run with Google Colab Free Tier CPU
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+[More Information Needed]
 
 ### Downstream Use [optional]
 
@@ -247,4 +205,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ### Framework versions
 
 
-- PEFT 0.6.2
+- PEFT 0.6.2

adapter_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a01743504ce860af99541c786ceb2897d01358a226ed32bbb6e364ac06689249
+oid sha256:6333ab988e3f3a31ecaa5e417b5e624c45f1510e12c59e11305e420a4ad70fcd
 size 7101392