diff --git a/README.md b/README.md
index f3bc0608bf..05977bad73 100644
--- a/README.md
+++ b/README.md
@@ -23,11 +23,11 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
 | Unsloth supports | Free Notebooks | Performance | Memory use |
 |-----------|---------|--------|----------|
 | **Llama 3 (8B)**      | [▶️ Start for free](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing)               | 2x faster | 60% less |
-| **Mistral v0.3 (7B)**    | [▶️ Start for free](https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing)               | 2.2x faster | 73% less |
+| **Mistral Nemo (12B)** | [▶️ Start for free](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)               | 2x faster | 60% less |
 | **Gemma 2 (9B)**      | [▶️ Start for free](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2x faster | 63% less |
 | **Phi-3 (mini)** | [▶️ Start for free](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |
-| **Phi-3 (medium)** | [▶️ Start for free](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)               | 2x faster | 50% less |
 | **Ollama**     | [▶️ Start for free](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)               | 1.9x faster | 43% less |
+| **Mistral v0.3 (7B)**    | [▶️ Start for free](https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing)               | 2.2x faster | 73% less |
 | **ORPO**     | [▶️ Start for free](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing)               | 1.9x faster | 43% less |
 | **DPO Zephyr**     | [▶️ Start for free](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 43% less |
 | **TinyLlama**  | [▶️ Start for free](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)               | 3.9x faster | 74% less |
@@ -39,8 +39,9 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
 - Click [here](https://github.com/unslothai/unsloth/wiki) for detailed documentation for Unsloth.
 
 ## 🦥 Unsloth.ai News
+- 📣 NEW! [Mistral Nemo-12b](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) both Base and Instruct now supported
 - 📣 NEW! [Gemma-2-9b](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) and Gemma-2-27b now supported
-- 📣 UPDATE! [Phi-3 mini](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) model updated
+- 📣 UPDATE! [Phi-3 mini](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) model updated. [Phi-3 Medium](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing) 2x faster finetuning.
 - 📣 NEW! Continued Pretraining [notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) for other languages like Korean!
 - 📣 NEW! Qwen2 now works
 - 📣 [Mistral v0.3 Base](https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing) and [Mistral v0.3 Instruct]
diff --git a/unsloth/models/gemma.py b/unsloth/models/gemma.py
index bc70b993ae..ce89ad3be6 100644
--- a/unsloth/models/gemma.py
+++ b/unsloth/models/gemma.py
@@ -210,22 +210,24 @@ class GemmaFixedRotaryEmbedding(torch.nn.Module):
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
+        # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
+        self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
 
         # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(seq_len=max_position_embeddings, device=device, dtype=torch.get_default_dtype())
+        self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
     pass
 
     def _set_cos_sin_cache(self, seq_len, device, dtype):
         # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
         # in FP32. They are applied (multiplied) in FP32 as well.
-        self.max_seq_len_cached = seq_len
+        self.current_rope_size = seq_len
 
         # The difference is we do division explicity instead of t * (1/x) ie we do t/x.
         freq_exponents = (2.0 / self.dim) * (
             torch.arange(self.dim // 2, dtype = torch.int64, device = "cpu").float()
         )
         timescale = self.base**freq_exponents
-        positions = torch.arange(self.max_seq_len_cached, device = "cpu", dtype = torch.int64).float()
+        positions = torch.arange(self.current_rope_size, device = "cpu", dtype = torch.int64).float()
         radians_new = positions[..., None] / timescale[None, None, :]
         radians_new = radians_new.squeeze(0)
 
@@ -239,7 +241,7 @@ class GemmaFixedRotaryEmbedding(torch.nn.Module):
 
     def forward(self, x, position_ids=None, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
+        if seq_len > self.current_rope_size:
             self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
 
         return (
@@ -247,6 +249,13 @@ class GemmaFixedRotaryEmbedding(torch.nn.Module):
             self.sin_cached[:seq_len].to(dtype=x.dtype),
         )
     pass
+
+    def extend_rope_embedding(self, x, seq_len):
+        if seq_len <= self.current_rope_size: return
+        # Iteratively grow by increments of 8192
+        self.current_rope_size = int(round(seq_len / 8192)) * 8192
+        self._set_cos_sin_cache(self.current_rope_size, device = "cuda:0", dtype = x.dtype)
+    pass
 pass
 
 
@@ -263,14 +272,14 @@ class GemmaFixedLinearScalingRotaryEmbedding(GemmaFixedRotaryEmbedding):
     def _set_cos_sin_cache(self, seq_len, device, dtype):
 # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
         # in FP32. They are applied (multiplied) in FP32 as well.
-        self.max_seq_len_cached = seq_len
+        self.current_rope_size = seq_len
 
         # The difference is we do division explicity instead of t * (1/x) ie we do t/x.
         freq_exponents = (2.0 / self.dim) * (
             torch.arange(self.dim // 2, dtype = torch.int64, device = "cpu").float()
         )
         timescale = self.base**freq_exponents
-        positions = torch.arange(self.max_seq_len_cached, device = "cpu", dtype = torch.int64).float()
+        positions = torch.arange(self.current_rope_size, device = "cpu", dtype = torch.int64).float()
         positions = positions /  self.scaling_factor
         radians_new = positions[..., None] / timescale[None, None, :]
         radians_new = radians_new.squeeze(0)
diff --git a/unsloth/models/mistral.py b/unsloth/models/mistral.py
index e0e034fc5c..ed6207bb06 100644
--- a/unsloth/models/mistral.py
+++ b/unsloth/models/mistral.py
@@ -299,8 +299,9 @@ class FastMistralModel(FastLlamaModel):
             attention_module   = MistralAttention,
         )
         # Just for Mistral Nemo models!
-        function = patch_mistral_nemo_attention(function)
-        if True:#init_name is not None:
+        if function is not None:
+            function = patch_mistral_nemo_attention(function)
+            # if True:#init_name is not None:
             exec(function, globals())
             MistralAttention.__init__  = eval(init_name)
         pass