Update README.md

README.md

@@ -5,7 +5,45 @@ datasets:
 - HuggingFaceH4/ultrafeedback_binarized
 language:
 - en
-base_model: google/gemma-7b
 pipeline_tag: question-answering
 library_name: transformers
----
+---
+
+Model Description: armaGPT is a finetuned version of Gemma 7b, a pre-trained language model developed by Google. It is designed to generate human-like text based on the input it receives. And armaGPT is finetuned using DPO Training for fair and safe generation.
+
+Model Architecture: The architecture of armaGPT is based on the transformer model, which is a type of recurrent neural network (RNN) that uses self-attention mechanisms to process input sequences.
+
+Model Size: The model has approximately 7 billion parameters.
+
+
+
+### Context Length
+Models are trained on a context length of 8192 tokens.
+
+#### Running the model on a CPU
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("sidharthsajith7/armaGPT")
+model = AutoModelForCausalLM.from_pretrained("sidharthsajith7/armaGPT")
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a single / multi GPU
+
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("sidharthsajith7/armaGPT")
+model = AutoModelForCausalLM.from_pretrained("sidharthsajith7/armaGPT", device_map="auto")
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```