Update README.md

README.md

@@ -1,202 +1,72 @@
 ---
 library_name: peft
 base_model: mistralai/Mixtral-8x7B-v0.1
+license: apache-2.0
+datasets:
+- knowrohit07/know_sql
+language:
+- en
+pipeline_tag: translation
 ---
+## SQL-Converter Mixtral 8x7B v0.1
 
-# Model Card for Model ID
+**Convert Natural Language to SQL**
 
-<!-- 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. -->
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-#### 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. -->
+### Overview
+Mixtral-8x7B-sql-ft-v1 is fine-tuned from Mixtral 8x7B to convert natural language to SQL queries.
 
-**BibTeX:**
+### Base Model
+mistralai/Mixtral-8x7B-v0.1
 
-[More Information Needed]
+### Fine-Tuning
+- **Dataset**: 5,000 natural language-SQL pairs.
 
-**APA:**
+### Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
 
-[More Information Needed]
+base_model_id = 'mistralai/Mixtral-8x7B-v0.1'
+adapter_id = 'sharadsin/Mixtral-8x7B-sql-ft-v1'
 
-## Glossary [optional]
+bnb_config = BitsAndBytesConfig(
+  load_in_4bit = True,
+  bnb_4bit_use_double_quant = True,
+  bnb_4bit_compute_dtype = torch.bfloat16,
+  bnb_4bit_quant_type = "nf4",
+)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+base_model = AutoModelForCausalLM.from_pretrained(
+  base_model_id,
+  quantization_config = bnb_config,
+  device_map = "auto",
+  trust_remote_code = True,
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token = True, trust_remote_code = True)
 
-[More Information Needed]
+ft_model = PeftModel.from_pretrained(base_model, adapter_id)
 
-## More Information [optional]
+eval_prompt= """SYSTEM: Use the following contextual information to concisely answer the question.
 
-[More Information Needed]
+USER: CREATE TABLE EmployeeInfo (EmpID INTEGER, EmpFname VARCHAR, EmpLname VARCHAR, Department VARCHAR, Project VARCHAR,Address VARCHAR, DOB DATE, Gender CHAR)
+===
+Write a query to fetch details of employees whose EmpLname ends with an alphabet 'A' and contains five alphabets?
+ASSISTANT:"""
 
-## Model Card Authors [optional]
+model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+ft_model.eval()
+with torch.inference_mode():
+    print(tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=70,top_k=4, penalty_alpha = 0.6, repetition_penalty=1.15)[0], skip_special_tokens= False))
 
-## Model Card Contact
+```
 
-[More Information Needed]
+### Limitations
+- Less accurate with very complex queries.
+- Generates extra gibberish content after providing the answers.
 
 
 ### Framework versions