Update README.md

README.md

@@ -5,200 +5,81 @@ base_model: codellama/CodeLlama-7b-hf
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+4 bit general purpose text-to-SQL model.
 
+Takes 5677MiB of GPU memory.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+Provide the CREATE statement of the target table(s) in the context of your prompt and ask a question to your database. The model outputs a query to answer the question.
 
+Data used for fine tuning: https://huggingface.co/datasets/b-mc2/sql-create-context
 
 - **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]
+- **Finetuned from model [optional]:** codellama/CodeLlama-7b-hf
 
 ## 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. -->
+This model can be coupled with a chat model like llama2-chat to convert the output into a text answer.
 
 ### 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. -->
+```python
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-### Testing Data, Factors & Metrics
 
-#### Testing Data
+# load model
+base_model = "GTimothee/sql-code-llama-4bits"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    load_in_4bit=True,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+model.eval()
 
-<!-- This should link to a Dataset Card if possible. -->
+# load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
 
-[More Information Needed]
+eval_prompt = """You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.
 
-#### Factors
+You must output the SQL query that answers the question.
+### Input:
+Which Class has a Frequency MHz larger than 91.5, and a City of license of hyannis, nebraska?
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Context:
+CREATE TABLE table_name_12 (class VARCHAR, frequency_mhz VARCHAR, city_of_license VARCHAR)
 
-[More Information Needed]
+### Response:
+"""
 
-#### Metrics
+model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
+with torch.no_grad():
+    print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
+```
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Outputs:
 
-[More Information Needed]
+```python
+### Response:
+SELECT class FROM table_name_12 WHERE frequency_mhz > 91.5 AND city_of_license = "hyannis, nebraska"
+```
 
-### 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]
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-#### Hardware
-
-[More Information Needed]
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-#### 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]
 
+## Bias, Risks, and Limitations
 
-### Framework versions
+- potential security issues if there is a malicious use. If you execute blindly the SQL queries that are being generated by end users you could lose data, leak information etc. 
+- may be mistaken depending on the way the prompt has been written. 
+ 
+### Recommendations
 
-- PEFT 0.8.2
+- Make sure that you check the generated SQL before applying it if the model is used by end users directly.
+- The model works well when used on simple tables and simple queries. If possible, try to break a complex query into multiple simple queries.