Update README.md

README.md

@@ -1,14 +1,52 @@
 ---
-license: gemma
+base_model: google/gemma-3-1b-it
+language:
+- en
 library_name: transformers
-pipeline_tag: text-generation
-extra_gated_heading: Access Gemma on Hugging Face
-extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
-  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
-  Face and click below. Requests are processed immediately.
-extra_gated_button_content: Acknowledge license
-base_model: google/gemma-3-1b-pt
+license: gemma
+tags:
+- unsloth
+- transformers
+- gemma3
+- gemma
+- google
 ---
+<div>
+  <p style="margin-bottom: 0; margin-top: 0;">
+    <strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
+  </p>
+  <p style="margin-bottom: 0;">
+    <em>Unsloth's Gemma 3 <a href="https://unsloth.ai/blog/dynamic-4bit">Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 4-bit.</em>
+  </p>
+  <div style="display: flex; gap: 5px; align-items: center; ">
+    <a href="https://github.com/unslothai/unsloth/">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
+    </a>
+    <a href="https://discord.gg/unsloth">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
+    </a>
+    <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device">
+      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
+    </a>
+  </div>
+<h1 style="margin-top: 0rem;">✨ Fine-tune Gemma 3 with Unsloth!</h1>
+</div>
+
+- Fine-tune Gemma 3 (12B) for free using our Google [Colab notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3.ipynb)!
+- Read our Blog about Gemma 3 support: [unsloth.ai/blog/gemma3](https://unsloth.ai/blog/gemma3)
+- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
+- Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF.
+
+| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
+|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
+| **GRPO with Gemma 3 (12B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma_3_(12B)-GRPO.ipynb)               | 2x faster | 80% less |
+| **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2.4x faster | 58% less |
+| **Llama-3.2 (11B vision)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)               | 2x faster | 60% less |
+| **Qwen2.5 (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb)               | 2x faster | 60% less |
+| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)               | 2x faster | 50% less |
+| **Mistral (7B)**    | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb)               | 2.2x faster | 62% less |
+
+<br>
 
 # Gemma 3 model card
 
@@ -58,87 +96,6 @@ for everyone.
         question, analysis of image content, or a summary of a document
     -   Total output context of 8192 tokens
 
-### Usage
-
-Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library with the version made for Gemma 3:
-
-```sh
-
-$ pip install git+https://github.com/huggingface/[email protected]
-
-```
-
-Then, copy the snippet from the section that is relevant for your use case.
-
-#### Running with the `pipeline` API
-
-With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.
-
-```python
-from transformers import pipeline
-
-pipe = pipeline("text-generation", model="google/gemma-3-1b-it", device="cuda", torch_dtype=torch.bfloat16)
-
-messages = [
-    [
-        {
-            "role": "system",
-            "content": [{"type": "text", "text": "You are a helpful assistant."},]
-        },
-        {
-            "role": "user",
-            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
-        },
-    ],
-]
-
-output = pipe(messages, max_new_tokens=50)
-```
-
-#### Running the model on a single / multi GPU
-
-```python
-from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
-import torch
-
-model_id = "google/gemma-3-1b-it"
-
-quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-
-model = Gemma3ForCausalLM.from_pretrained(
-    model_id, quantization_config=quantization_config
-).eval()
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-messages = [
-    [
-        {
-            "role": "system",
-            "content": [{"type": "text", "text": "You are a helpful assistant."},]
-        },
-        {
-            "role": "user",
-            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
-        },
-    ],
-]
-inputs = tokenizer.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-).to(model.device).to(torch.bfloat16)
-
-
-with torch.inference_mode():
-    outputs = model.generate(**inputs, max_new_tokens=64)
-
-outputs = tokenizer.batch_decode(outputs)
-```
-
-
 ### Citation
 
 ```none