Added model card (#1)

- Added model card (b46a87ba8c3e0f7b1652ec2059925866811534f9)


Co-authored-by: Lynn Langit <lynnlangit@users.noreply.huggingface.co>

README.md

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+---
+license: gemma
+base_model: google/gemma-2-2b
+library_name: transformers
+tags:
+- text-generation
+- gemma2
+- local-inference
+- bitsandbytes
+- fine-tuned
+pipeline_tag: text-generation
+---
+
+# Gemma-2-Racer
+
+`gemma2racer` is a specialized optimization of Google's **Gemma 2** architecture. This model is fine-tuned and configured specifically for "racing" performance—prioritizing high-speed token generation and low-memory overhead for local LLM deployment.
+
+---
+
+## Model Summary
+
+The following table outlines the core technical specifications for the Gemma-2-Racer model.
+
+| Feature | Details |
+| :--- | :--- |
+| **Developed by** | [Rabimba Karanjai](https://huggingface.co/rabimba) |
+| **Model Type** | Causal Language Model (Transformer-based) |
+| **Base Model** | [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) |
+| **Architecture** | Gemma-2 |
+| **Optimization Strategy** | 4-bit Quantization, `torch.compile`, and BitsAndBytes |
+| **Primary Language** | English |
+| **License** | [Gemma Terms of Use](https://ai.google.dev/gemma/terms) |
+
+---
+
+## Intended Use
+
+This model is designed for developers and researchers who require state-of-the-art performance on consumer-grade hardware. It is specifically optimized for:
+
+* **Real-time Interaction:** Minimized "Time To First Token" (TTFT) for chat applications.
+* **Local Privacy:** Small enough to run entirely offline on standard laptops or edge devices.
+* **Efficient Inference:** Optimized to fit into 2GB - 4GB of VRAM depending on your quantization settings.
+
+---
+
+## Quickstart Guide
+
+To get the model running with the "Racer" performance presets, follow these steps:
+
+1.  **Install Requirements:**
+    Update your environment with the necessary libraries for quantization and acceleration.
+    ```bash
+    pip install -U transformers accelerate bitsandbytes
+    ```
+
+2.  **Login to Hugging Face:**
+    Ensure you have accepted the Gemma license on the official Google repository and authenticate locally.
+    ```bash
+    huggingface-cli login
+    ```
+
+3.  **Python Implementation:**
+    Use the following code snippet to load the model in its optimized 4-bit state.
+    ```python
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    import torch
+
+    model_id = "rabimba/gemma2racer"
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        device_map="auto",
+        load_in_4bit=True,
+        torch_dtype=torch.bfloat16
+    )
+
+    prompt = "Explain quantum physics like I'm a race car driver."
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    
+    outputs = model.generate(**inputs, max_new_tokens=150)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+    ```
+
+---
+
+## Performance Profiles
+
+The "Racer" moniker refers to the model's ability to be tuned for different hardware constraints:
+
+* **The Speedster (Linux/CUDA):** After loading, use `model = torch.compile(model)` to utilize kernel fusion for significantly higher throughput.
+* **The Daily Driver (Standard GPU):** Standard 4-bit loading via BitsAndBytes provides a perfect balance of speed and 2.6B parameter intelligence.
+* **The Endurance Run (Low VRAM):** Can be run with heavy CPU offloading via `accelerate` for systems with limited or no dedicated graphics memory.
+
+---
+
+## Limitations and Ethical Considerations
+
+* **Accuracy:** Like all large language models, this model may hallucinate. Users should verify critical information.
+* **Bias:** This model inherits biases present in the Gemma-2 base training data.
+* **Safety:** While safety filters are present, it is recommended that users implement their own moderation layers for public-facing deployments.
+
+---
+
+## Citation
+
+If you use this model in your research or commercial projects, please cite it as follows:
+
+```bibtex
+@misc{gemma2racer2024,
+  author = {Rabimba Karanjai},
+  title = {Gemma-2-Racer: Optimized Local Inference},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/rabimba/gemma2racer}}
+}