updated Readme

README.md

@@ -14,4 +14,75 @@ widget:
 language:
 - en
 pipeline_tag: text-generation
----
+---
+
+# Gemma-2b-it-finetuned-python-codes
+
+This model card corresponds to the 2B finetuned version of the Gemma-2b-it model. You can visit the model card of the [2B Gemma Instruct](https://huggingface.co/google/gemma-2b-it). 
+
+**Author**: Dishank Shah
+
+## Model Information
+
+Summary description and brief definition of inputs and outputs.
+
+### Description
+
+Gemma is a family of lightweight, state-of-the-art open models from Google,
+built from the same research and technology used to create the Gemini models.
+They are text-to-text, decoder-only large language models, available in English,
+with open weights, pre-trained variants, and instruction-tuned variants. Gemma
+models are well-suited for a variety of text generation tasks, including
+question answering, summarization, and reasoning. Their relatively small size
+makes it possible to deploy them in environments with limited resources such as
+a laptop, desktop or your own cloud infrastructure, democratizing access to
+state of the art AI models and helping foster innovation for everyone.
+
+### Usage
+
+Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
+
+#### Running the model on Google Colab CPU
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "shahdishank/gemma-2b-it-finetune-python-codes"
+HUGGING_FACE_TOKEN = "YOUR_TOKEN"
+tokenizer = AutoTokenizer.from_pretrained(model_name, token="HUGGING_FACE_TOKEN")
+model = AutoModelForCausalLM.from_pretrained(model_name, token="HUGGING_FACE_TOKEN")
+
+prompt_template = """\
+  user:\n{query} \n\n assistant:\n
+  """
+prompt = prompt_template.format(query="write a simple python function") # write your query here
+
+input_ids = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+outputs = model.generate(**input_ids, max_new_tokens=2000, do_sample=True, pad_token_id=tokenizer.eos_token_id)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Model Data
+
+Data used for model training [python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k).
+
+### Training Dataset
+
+These models were trained on a dataset of text data that includes a wide variety
+of python codes. Here are the key components:
+
+* Instruction: The instructional task to be performed / User input.
+* Input: Very short, introductive part of AI response or empty.
+* Output: Python code that accomplishes the task.
+* Text: All fields combined together.
+
+This diverse data source is crucial for training a powerful
+language model that can handle a wide variety of different tasks.
+
+### Usage
+
+This LLM can be used for:
+* Code generation
+* Debugging
+* Learn and understand various python coding styles