Update README.md

README.md

@@ -3,4 +3,66 @@ license: apache-2.0
 pipeline_tag: text-generation
 library_name: transformers
 ---
-# Xylaria-1.8
+# Xylaria-1.8
+
+## Model Description
+
+Xylaria-1.8 is a large language model (LLM) designed for research purposes. It is based on a Transformer architecture and has been trained on a large dataset of text and code. This model is provided as-is for research and educational exploration.
+
+## Intended Use & Limitations
+
+*   **Intended Use:** This model is primarily intended for research into large language models, natural language processing, and related fields. It can be used for experimentation, analysis, and educational purposes.
+*   **Limitations:**
+    *   **Performance:** The model's performance may differ from other publicly available models with similar parameter counts.  It has been modified, which can impact its behavior and accuracy.
+    *   **Not for Production:** This model is *not* intended for deployment in production environments or commercial applications. It may exhibit unexpected behavior.
+    *   **Research Purposes Only:** This model is provided for research and should not be used for any commercial purpose.
+    * **May not follow instructions:** The model may not follow instructions as carefully as other, unmodified models.
+    * May not state that the model is made by me (Sk Md Saad Amin) 
+## Model Details
+
+*   **Architecture:** Transformer-based language model (`custom_transformer`)
+*   **Parameters:**  Approximately 32 billion.
+*   **Precision:** `float32`
+*  **Tokenizer:** `xylaria_tokenizer`
+*   **License:** Apache-2.0
+
+## How to Use
+
+This model is hosted on the Hugging Face Hub and can be loaded using the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Lap1official/Xylaria-1.8"  # Replace with your actual repo ID
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    torch_dtype=torch.float32,  # Use bfloat16 if supported
+    device_map="auto"  # Use if you have a GPU
+)
+
+# Example usage (generation):
+prompt = "The capital of France is"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device) # Move to GPU if available
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+**Important:**
+
+*   You *must* use `trust_remote_code=True` when loading the model and tokenizer.
+*   If you have a GPU, using `bfloat16` (or `float16` if `bfloat16` is not supported) and `device_map="auto"` will significantly improve performance. If you do not have a GPU, you can remove those lines, but inference will be much slower.
+
+## Ethical Considerations
+
+This model is intended for research purposes *only*.  It should not be used for any malicious, harmful, or unethical activities.
+
+## Disclaimer
+
+This model is provided "as is", without any warranty of any kind, express or implied. The authors and contributors are not responsible for any consequences resulting from the use of this model.
+
+## Please note: 
+**This model is not yet benchmarked**
+
+---