README.md

# Model Card for Model ID

# Model Name
Luxeai-anu-1-bit-70M

## Model Description
The Luxeai-anu-1-bit-70M Large Language Model (LLM) is my first trial to implement one-bit LLM based on the original paper - "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits". I have taken the pre-trained Mistral-7B-v0.3 and abideen/Cosmopedia-100k-pretrain dataset.

## Intended Use
- **Task**: Describe the specific tasks (e.g., sentiment analysis, text generation) the model is designed for.
- **Industries**: Mention any particular industries or applications where the model could be applied.
- **Users**: Identify the intended users (e.g., researchers, developers).

## How to Use
Provide code examples for loading and using the model:

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("username/model_name")
model = AutoModel.from_pretrained("username/model_name")

# Example usage
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)

README.md

@@ -7,4 +7,29 @@ datasets:
 metrics:
 - accuracy
 library_name: transformers
----
+---
+# Model Card for Model ID
+
+# Model Name
+Luxeai-anu-1-bit-70M
+
+## Model Description
+The Luxeai-anu-1-bit-70M Large Language Model (LLM) is my first trial to implement one-bit LLM based on the original paper - "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits". I have taken the pre-trained Mistral-7B-v0.3 and abideen/Cosmopedia-100k-pretrain dataset. 
+
+## Intended Use
+- **Task**: Describe the specific tasks (e.g., sentiment analysis, text generation) the model is designed for.
+- **Industries**: Mention any particular industries or applications where the model could be applied.
+- **Users**: Identify the intended users (e.g., researchers, developers).
+
+## How to Use
+Provide code examples for loading and using the model:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("username/model_name")
+model = AutoModel.from_pretrained("username/model_name")
+
+# Example usage
+inputs = tokenizer("Hello, world!", return_tensors="pt")
+outputs = model(**inputs)