---
library_name: transformers
tags:
- code
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

This model is fined-tuned based of Gemma model by Google's Gemini family and trained for generating general python codes, from Machine learning, to Web developing and data processing.
- **Developed by:** Ali Bidaran

- **Language(s) (NLP):** English


## Uses
This model can be used for generating python codes in different usages such as data processing, designing ML and DL algorithms and implementing the backends of web applications
with common frameworks such as Flask. Users can obtain the generated text by giving required prompts and instructions to the model.

<!-- 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
``` python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer

model_id = "alibidaran/Gemma2_Python_instruction"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)


tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0})
prompt = """
Connect to a MongoDB database, select all documents from the collection ‘customers’ where the 'age' field is greater than 30 and the 'gender' field is 'female'. Then, for each selected document, retrieve the corresponding document from another collection called 'orders' based on the 'customer_id' field. Finally, display the documents in ascending order based on the 'last_name' field. The expected time complexity for retrieving the documents from the 'customers' collection should be O(n), where n is the total number of documents in the collection. The expected time complexity for retrieving the corresponding documents from the 'orders' collection for each selected document should also be O(n), where n is the total number of selected documents.
"""
text=f"<s> ##Instruction: {prompt}: ##Output "
inputs=tokenizer(text,return_tensors='pt').to('cuda')
outputs=model.generate(**inputs,max_new_tokens=400,do_sample=True,top_p=0.92,top_k=10,temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

```
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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