Create README.md

README.md

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+---
+base_model: google/gemma-3-4b-it
+tags:
+- L-Mul,
+- optimazation
+- quantization
+- text-generation
+- research
+- experimental
+license: gemma
+---
+
+# L-Mul Optimized: google/gemma-3-4b-it
+
+This is a modified version of Google's [gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) model. The modification consists of replacing the standard attention mechanism with one that uses a custom, approximate matrix multiplication algorithm termed "L-Mul".
+
+This work was performed as part of a research project to evaluate the performance and accuracy trade-offs of algorithmic substitutions in transformer architectures.
+
+**This model is intended strictly for educational and scientific purposes.**
+
+## Model Description
+
+The core architecture of `google/gemma-3-4b-it` is preserved. However, the standard `Gemma3Attention` modules have been dynamically replaced with a custom version that utilizes the `l_mul_attention` function for its core computations. This function is defined in the `lmul.py` file included in this repository.
+
+-   **Base Model:** [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it)
+-   **Modification:** Replacement of standard attention with L-Mul approximate attention.
+-   **Primary Use-Case:** Research and educational analysis of algorithmic impact on LLMs.
+
+## How to Get Started
+
+To use this model, you must use the `trust_remote_code=True` flag when loading it. This is required to execute the custom `lmul.py` file that defines the new attention mechanism.
+
+You can load the model directly from this repository using the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Define the repository ID for the specific model
+repo_id = "Peacemann/google_gemma-3-4b-it-lmul-attention" # Replace with the correct repo ID if different
+
+# Load the tokenizer and model, trusting the remote code to load lmul.py
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# Example usage
+prompt = "The L-Mul algorithm is an experimental method for..."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=50)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Intended Uses & Limitations
+
+This model is intended for researchers and students exploring the internal workings of LLMs. It is a tool for visualizing and analyzing the effects of fundamental algorithmic changes.
+
+**This model is NOT intended for any commercial or production application.**
+
+The modification is experimental. The impact on the model's performance, safety alignment, accuracy, and potential for generating biased or harmful content is **unknown and untested**.
+
+## Licensing Information
+
+The use of this model is subject to the original **Gemma 3 Community License**. By using this model, you agree to the terms outlined in the license.