updated readme

README.md

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-
 # llama-7b-glora 🦙
 
 This model was built via parameter-efficient GLoRA finetuning of [llama-7b](https://huggingface.co/huggyllama/llama-7b) on the shareGPT dataset. We adapt only the attention layers using GLoRA.
 
-Model license: This model is under a non-commercial license (see the LICENSE file) same as LLaMA.
+* Model license: This model is under a same license (see the LICENSE file) as LLaMA.
+* GLoRA implementation: [script](https://github.com/Arnav0400/peft/blob/main/src/peft/tuners/glora.py)
+
+## Model Description
+
+The architecture is similar to LLaMA-7B, but the bias is true for attention layers.
+
+## Limitations and Biases
+_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
+
+This model can produce factually incorrect output, and should not be relied on to produce factually accurate information.
+This model was trained on various public datasets.
+While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+## How to Use
+
+Install and import the package dependencies:  
+
+```python
+!pip install -q -U huggingface_hub transformers torch accelerate
+```
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+```
+
+Basic model loading:
+
+```python
+model = AutoModelForCausalLM.from_pretrained(
+    "MBZUAI-LLM/LLaMA-7B-GLoRA-ShareGPT",
+    use_auth_token=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("MBZUAI-LLM/LLaMA-7B-GLoRA-ShareGPT")
+```
+
+Once loaded, the model and tokenizer can be used with the following code:
+
+```python
+def llama_generate(
+    model: AutoModelForCausalLM,
+    tokenizer: AutoTokenizer,
+    prompt: str,
+    max_new_tokens: int = 128,
+    temperature: float = 0.92,
+) -> str:
+    """
+    Initialize the pipeline
+    Uses Hugging Face GenerationConfig defaults
+        https://huggingface.co/docs/transformers/v4.29.1/en/main_classes/text_generation#transformers.GenerationConfig
+    Args:
+        model (transformers.AutoModelForCausalLM): Model for text generation
+        tokenizer (transformers.AutoTokenizer): Tokenizer for model
+        prompt (str): Prompt for text generation
+        max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
+        temperature (float, optional): The value used to modulate the next token probabilities.
+            Defaults to 1.0
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    inputs = tokenizer(
+        [prompt],
+        return_tensors="pt",
+        return_token_type_ids=False,
+    ).to(
+        device
+    )  # tokenize inputs, load on device
+    # when running Torch modules in lower precision, it is best practice to use the torch.autocast context manager.
+    with torch.autocast("cuda", dtype=torch.bfloat16):
+        response = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            return_dict_in_generate=True,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    decoded_output = tokenizer.decode(
+        response["sequences"][0],
+        skip_special_tokens=True,
+    )  # grab output in natural language
+    return decoded_output[len(prompt) :]  # remove prompt from output
+```
+
+We can now generate text! For example:
+
+```python
+prompt = "You are a helpful assistant. Tell me a recipe for vegan banana bread.\n"
+response = llama_generate(
+    model,
+    tokenizer,
+    prompt,
+    max_new_tokens=500,
+    temperature=0.92,
+)
+print(response)
+```
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
+
+## Citation for GLoRA
+
+```
+@misc{chavan2023oneforall,
+      title={One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning}, 
+      author={Arnav Chavan and Zhuang Liu and Deepak Gupta and Eric Xing and Zhiqiang Shen},
+      year={2023},
+      eprint={2306.07967},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+
+---