diff --git a/README.md b/README.md
index 5e536e39..f7c916f1 100644
--- a/README.md
+++ b/README.md
@@ -250,6 +250,7 @@ loss.backward()
 | Llama4 (Text) & (Multimodal)      | `liger_kernel.transformers.apply_liger_kernel_to_llama4`   | RMSNorm, LayerNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy         |
 | LLaMA 2 & 3 | `liger_kernel.transformers.apply_liger_kernel_to_llama`   | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy        |
 | LLaMA 3.2-Vision | `liger_kernel.transformers.apply_liger_kernel_to_mllama`   | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy        |
+| Ministral   | `liger_kernel.transformers.apply_liger_kernel_to_ministral` | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy        |
 | Mistral     | `liger_kernel.transformers.apply_liger_kernel_to_mistral`  | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy        |
 | Mixtral     | `liger_kernel.transformers.apply_liger_kernel_to_mixtral`  | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy        |
 | Nemotron    | `liger_kernel.transformers.apply_liger_kernel_to_nemotron` | CrossEntropyLoss, FusedLinearCrossEntropy                               |
diff --git a/src/liger_kernel/transformers/__init__.py b/src/liger_kernel/transformers/__init__.py
index d47bdfca..7a0343b6 100644
--- a/src/liger_kernel/transformers/__init__.py
+++ b/src/liger_kernel/transformers/__init__.py
@@ -53,6 +53,7 @@ if TYPE_CHECKING:
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_llama  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_llama4  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_llava  # noqa: F401
+    from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_ministral  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_mistral  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_mixtral  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_mllama  # noqa: F401
@@ -125,6 +126,7 @@ def __getattr__(name: str):
         "apply_liger_kernel_to_llama",
         "apply_liger_kernel_to_llava",
         "apply_liger_kernel_to_llama4",
+        "apply_liger_kernel_to_ministral",
         "apply_liger_kernel_to_mistral",
         "apply_liger_kernel_to_mixtral",
         "apply_liger_kernel_to_nemotron",
@@ -210,6 +212,7 @@ if _TRANSFORMERS_AVAILABLE:
             "apply_liger_kernel_to_llama",
             "apply_liger_kernel_to_llava",
             "apply_liger_kernel_to_llama4",
+            "apply_liger_kernel_to_ministral",
             "apply_liger_kernel_to_mistral",
             "apply_liger_kernel_to_mixtral",
             "apply_liger_kernel_to_nemotron",
diff --git a/src/liger_kernel/transformers/model/ministral.py b/src/liger_kernel/transformers/model/ministral.py
new file mode 100644
index 00000000..971fb4f1
--- /dev/null
+++ b/src/liger_kernel/transformers/model/ministral.py
@@ -0,0 +1,145 @@
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from transformers.cache_utils import Cache
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+
+
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Cache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerCausalLMOutputWithPast]:
+    r"""
+    Copy paste Ministral's forward but replace torch cross entropy with liger fused linear cross entropy
+
+
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, MinistralForCausalLM
+
+    >>> model = MinistralForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410")
+    >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Ministral-8B-Instruct-2410")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+    kept_hidden_states = hidden_states[:, slice_indices, :]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    loss = None
+    logits = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits and labels is None and shift_labels is None:
+        raise ValueError("skip_logits is True, but labels and shift_labels are None")
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+
+        loss = None
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output_tuple = (logits,) + outputs[1:]
+        output = (loss,) + output_tuple if loss is not None else output_tuple
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with token_accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )
diff --git a/src/liger_kernel/transformers/monkey_patch.py b/src/liger_kernel/transformers/monkey_patch.py
index 39123a3a..40c762a3 100755
--- a/src/liger_kernel/transformers/monkey_patch.py
+++ b/src/liger_kernel/transformers/monkey_patch.py
@@ -21,6 +21,7 @@ from liger_kernel.transformers.model.gemma2 import lce_forward as gemma2_lce_for
 from liger_kernel.transformers.model.gpt_oss import lce_forward as gpt_oss_lce_forward
 from liger_kernel.transformers.model.llama import lce_forward as llama_lce_forward
 from liger_kernel.transformers.model.llava import lce_forward as llava_lce_forward
+from liger_kernel.transformers.model.ministral import lce_forward as ministral_lce_forward
 from liger_kernel.transformers.model.mistral import lce_forward as mistral_lce_forward
 from liger_kernel.transformers.model.mixtral import lce_forward as mixtral_lce_forward
 from liger_kernel.transformers.model.nemotron import lce_forward as nemotron_lce_forward
@@ -619,6 +620,69 @@ def apply_liger_kernel_to_mllama(
                     _patch_layer_norm_module(layer.post_attention_layernorm)
 
 
+def apply_liger_kernel_to_ministral(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Ministral models
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is True.
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is True.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+        swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is True.
+        model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
+        loaded. Default is None.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.ministral import modeling_ministral
+    from transformers.models.ministral.modeling_ministral import MinistralModel
+
+    if rope:
+        modeling_ministral.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_ministral.MinistralRMSNorm = LigerRMSNorm
+    if cross_entropy:
+        modeling_ministral.CrossEntropyLoss = LigerCrossEntropyLoss
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(ministral_lce_forward, model)
+        else:
+            modeling_ministral.MinistralForCausalLM.forward = ministral_lce_forward
+
+    if swiglu:
+        modeling_ministral.MinistralMLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: MinistralModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
 def apply_liger_kernel_to_mistral(
     rope: bool = True,
     cross_entropy: bool = False,
@@ -3120,6 +3184,7 @@ MODEL_TYPE_TO_APPLY_LIGER_FN = {
     "granite": apply_liger_kernel_to_granite,
     "mllama": apply_liger_kernel_to_mllama,
     "mllama_text_model": apply_liger_kernel_to_mllama,
+    "ministral": apply_liger_kernel_to_ministral,
     "mistral": apply_liger_kernel_to_mistral,
     "mixtral": apply_liger_kernel_to_mixtral,
     "nemotron": apply_liger_kernel_to_nemotron,
diff --git a/test/convergence/bf16/test_mini_models.py b/test/convergence/bf16/test_mini_models.py
index 9395e28c..24a8e967 100644
--- a/test/convergence/bf16/test_mini_models.py
+++ b/test/convergence/bf16/test_mini_models.py
@@ -40,6 +40,7 @@ from liger_kernel.transformers import apply_liger_kernel_to_internvl
 from liger_kernel.transformers import apply_liger_kernel_to_llama
 from liger_kernel.transformers import apply_liger_kernel_to_llama4
 from liger_kernel.transformers import apply_liger_kernel_to_llava
+from liger_kernel.transformers import apply_liger_kernel_to_ministral
 from liger_kernel.transformers import apply_liger_kernel_to_mistral
 from liger_kernel.transformers import apply_liger_kernel_to_mixtral
 from liger_kernel.transformers import apply_liger_kernel_to_mllama
@@ -81,6 +82,7 @@ from test.utils import revert_liger_kernel_to_internvl
 from test.utils import revert_liger_kernel_to_llama
 from test.utils import revert_liger_kernel_to_llama4
 from test.utils import revert_liger_kernel_to_llava
+from test.utils import revert_liger_kernel_to_ministral
 from test.utils import revert_liger_kernel_to_mistral
 from test.utils import revert_liger_kernel_to_mixtral
 from test.utils import revert_liger_kernel_to_mllama
@@ -113,6 +115,14 @@ try:
 except ImportError:
     LLAMA4_AVAILABLE = False
 
+try:
+    from transformers.models.ministral.configuration_ministral import MinistralConfig
+    from transformers.models.ministral.modeling_ministral import MinistralForCausalLM
+
+    MINISTRAL_AVAILABLE = True
+except ImportError:
+    MINISTRAL_AVAILABLE = False
+
 try:
     # Mllama is only available in transformers>=4.45.0
     from transformers.models.mllama.configuration_mllama import MllamaTextConfig
@@ -565,6 +575,33 @@ MINI_MODEL_SETUPS = {
     ),
 }
 
+if MINISTRAL_AVAILABLE:
+    MINI_MODEL_SETUPS["mini_ministral"] = MiniModelConfig(
+        liger_kernel_patch_func=apply_liger_kernel_to_ministral,
+        liger_kernel_patch_revert_func=revert_liger_kernel_to_ministral,
+        model_class=MinistralForCausalLM,
+        mini_model_config=MinistralConfig(
+            attention_dropout=0.0,
+            bos_token_id=1,
+            eos_token_id=2,
+            head_dim=128,
+            hidden_act="silu",
+            hidden_size=1024,
+            initializer_range=0.02,
+            intermediate_size=2048,
+            max_position_embeddings=32768,
+            num_attention_heads=8,
+            num_hidden_layers=4,
+            num_key_value_heads=2,
+            rms_norm_eps=1e-5,
+            sliding_window=4096,
+            tie_word_embeddings=False,
+            use_cache=True,
+            vocab_size=32000,
+            attn_implementation="sdpa",
+        ),
+    )
+
 if LLAMA4_AVAILABLE:
     MINI_MODEL_SETUPS["mini_llama4"] = MiniModelConfig(
         liger_kernel_patch_func=apply_liger_kernel_to_llama4,
@@ -1963,6 +2000,24 @@ def run_mini_model(
                 pytest.mark.skipif(not supports_bfloat16(), reason="bfloat16 not supported on this GPU"),
             ],
         ),
+        pytest.param(
+            "mini_ministral",
+            32,
+            1e-5,
+            torch.bfloat16,
+            5e-2,
+            5e-2,
+            1e-1,
+            1e-2,
+            1e-2,
+            1e-2,
+            marks=[
+                pytest.mark.skipif(not supports_bfloat16(), reason="bfloat16 not supported on this GPU"),
+                pytest.mark.skipif(
+                    not MINISTRAL_AVAILABLE, reason="Ministral not available in this version of transformers"
+                ),
+            ],
+        ),
         pytest.param(
             "mini_olmo2",
             32,
diff --git a/test/convergence/bf16/test_mini_models_with_logits.py b/test/convergence/bf16/test_mini_models_with_logits.py
index b6deed0e..e9457f96 100644
--- a/test/convergence/bf16/test_mini_models_with_logits.py
+++ b/test/convergence/bf16/test_mini_models_with_logits.py
@@ -39,6 +39,7 @@ from liger_kernel.transformers import apply_liger_kernel_to_internvl
 from liger_kernel.transformers import apply_liger_kernel_to_llama
 from liger_kernel.transformers import apply_liger_kernel_to_llama4
 from liger_kernel.transformers import apply_liger_kernel_to_llava
+from liger_kernel.transformers import apply_liger_kernel_to_ministral
 from liger_kernel.transformers import apply_liger_kernel_to_mistral
 from liger_kernel.transformers import apply_liger_kernel_to_mixtral
 from liger_kernel.transformers import apply_liger_kernel_to_mllama
@@ -78,6 +79,7 @@ from test.utils import revert_liger_kernel_to_internvl
 from test.utils import revert_liger_kernel_to_llama
 from test.utils import revert_liger_kernel_to_llama4
 from test.utils import revert_liger_kernel_to_llava
+from test.utils import revert_liger_kernel_to_ministral
 from test.utils import revert_liger_kernel_to_mistral
 from test.utils import revert_liger_kernel_to_mixtral
 from test.utils import revert_liger_kernel_to_mllama
@@ -101,6 +103,14 @@ from test.utils import supports_bfloat16
 
 IS_TRANSFORMERS_V5_OR_LATER = version.parse(transformers.__version__) >= version.parse("5.0.0")
 
+try:
+    from transformers.models.ministral.configuration_ministral import MinistralConfig
+    from transformers.models.ministral.modeling_ministral import MinistralForCausalLM
+
+    MINISTRAL_AVAILABLE = True
+except ImportError:
+    MINISTRAL_AVAILABLE = False
+
 try:
     from transformers.models.llama4.configuration_llama4 import Llama4TextConfig
     from transformers.models.llama4.modeling_llama4 import Llama4ForCausalLM
@@ -424,22 +434,26 @@ MINI_MODEL_SETUPS = {
             attn_implementation="sdpa",
         ),
     ),
-    "mini_mixtral": MiniModelConfig(
-        liger_kernel_patch_func=apply_liger_kernel_to_mixtral,
-        liger_kernel_patch_revert_func=revert_liger_kernel_to_mixtral,
-        model_class=MixtralForCausalLM,
-        mini_model_config=MixtralConfig(
+}
+
+if MINISTRAL_AVAILABLE:
+    MINI_MODEL_SETUPS["mini_ministral"] = MiniModelConfig(
+        liger_kernel_patch_func=apply_liger_kernel_to_ministral,
+        liger_kernel_patch_revert_func=revert_liger_kernel_to_ministral,
+        model_class=MinistralForCausalLM,
+        mini_model_config=MinistralConfig(
             attention_dropout=0.0,
             bos_token_id=1,
             eos_token_id=2,
+            head_dim=128,
             hidden_act="silu",
-            hidden_size=512,  # 4096
+            hidden_size=1024,
             initializer_range=0.02,
-            intermediate_size=2048,  # 14336
-            max_position_embeddings=32768,  # 32768
-            num_attention_heads=8,  # 32
-            num_hidden_layers=4,  # 32
-            num_key_value_heads=2,  # 8
+            intermediate_size=2048,
+            max_position_embeddings=32768,
+            num_attention_heads=8,
+            num_hidden_layers=4,
+            num_key_value_heads=2,
             rms_norm_eps=1e-5,
             sliding_window=4096,
             tie_word_embeddings=False,
@@ -447,94 +461,122 @@ MINI_MODEL_SETUPS = {
             vocab_size=32000,
             attn_implementation="sdpa",
         ),
-    ),
-    "mini_gemma1": MiniModelConfig(
-        liger_kernel_patch_func=apply_liger_kernel_to_gemma,
-        liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma,
-        model_class=GemmaForCausalLM,
-        mini_model_config=GemmaConfig(
-            vocab_size=32000,  # 256000
-            hidden_size=1024,  # 3072
-            intermediate_size=2048,  # 24576
-            num_hidden_layers=4,  # 28
-            num_attention_heads=4,  # 16
-            num_key_value_heads=4,  # 16
-            head_dim=256,
-            # gemma1 model config uses `hidden_act` and point it to gelu,
-            # https://huggingface.co/google/gemma-7b/blob/main/config.json#L10
-            # but in reality it's ignored and HuggingFace will use tanh approximation:
-            # https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/gemma/modeling_gemma.py#L175
-            hidden_act="gelu",
-            max_position_embeddings=8192,
-            initializer_range=0.02,
-            rms_norm_eps=1e-06,
-            use_cache=True,
-            pad_token_id=0,
-            # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset
-            # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
-            bos_token_id=1,  # 128000
-            eos_token_id=2,  # 128001
-            tie_word_embeddings=True,
-            attention_bias=False,
-            attention_dropout=0.0,
+    )
+
+MINI_MODEL_SETUPS.update(
+    {
+        "mini_mixtral": MiniModelConfig(
+            liger_kernel_patch_func=apply_liger_kernel_to_mixtral,
+            liger_kernel_patch_revert_func=revert_liger_kernel_to_mixtral,
+            model_class=MixtralForCausalLM,
+            mini_model_config=MixtralConfig(
+                attention_dropout=0.0,
+                bos_token_id=1,
+                eos_token_id=2,
+                hidden_act="silu",
+                hidden_size=512,  # 4096
+                initializer_range=0.02,
+                intermediate_size=2048,  # 14336
+                max_position_embeddings=32768,  # 32768
+                num_attention_heads=8,  # 32
+                num_hidden_layers=4,  # 32
+                num_key_value_heads=2,  # 8
+                rms_norm_eps=1e-5,
+                sliding_window=4096,
+                tie_word_embeddings=False,
+                use_cache=True,
+                vocab_size=32000,
+                attn_implementation="sdpa",
+            ),
         ),
-    ),
-    "mini_gemma1.1": MiniModelConfig(
-        liger_kernel_patch_func=apply_liger_kernel_to_gemma,
-        liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma,
-        model_class=GemmaForCausalLM,
-        mini_model_config=GemmaConfig(
-            vocab_size=32000,  # 256000
-            hidden_size=1024,  # 3072
-            intermediate_size=2048,  # 24576
-            num_hidden_layers=4,  # 28
-            num_attention_heads=4,  # 16
-            num_key_value_heads=4,  # 16
-            head_dim=256,
-            hidden_activation="gelu_pytorch_tanh",
-            max_position_embeddings=8192,
-            initializer_range=0.02,
-            rms_norm_eps=1e-06,
-            use_cache=True,
-            pad_token_id=0,
-            # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset
-            # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
-            bos_token_id=1,  # 128000
-            eos_token_id=2,  # 128001
-            tie_word_embeddings=True,
-            attention_bias=False,
-            attention_dropout=0.0,
+        "mini_gemma1": MiniModelConfig(
+            liger_kernel_patch_func=apply_liger_kernel_to_gemma,
+            liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma,
+            model_class=GemmaForCausalLM,
+            mini_model_config=GemmaConfig(
+                vocab_size=32000,  # 256000
+                hidden_size=1024,  # 3072
+                intermediate_size=2048,  # 24576
+                num_hidden_layers=4,  # 28
+                num_attention_heads=4,  # 16
+                num_key_value_heads=4,  # 16
+                head_dim=256,
+                # gemma1 model config uses `hidden_act` and point it to gelu,
+                # https://huggingface.co/google/gemma-7b/blob/main/config.json#L10
+                # but in reality it's ignored and HuggingFace will use tanh approximation:
+                # https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/gemma/modeling_gemma.py#L175
+                hidden_act="gelu",
+                max_position_embeddings=8192,
+                initializer_range=0.02,
+                rms_norm_eps=1e-06,
+                use_cache=True,
+                pad_token_id=0,
+                # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset
+                # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
+                bos_token_id=1,  # 128000
+                eos_token_id=2,  # 128001
+                tie_word_embeddings=True,
+                attention_bias=False,
+                attention_dropout=0.0,
+            ),
         ),
-    ),
-    "mini_gemma2": MiniModelConfig(
-        liger_kernel_patch_func=apply_liger_kernel_to_gemma2,
-        liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma2,
-        model_class=Gemma2ForCausalLM,
-        mini_model_config=Gemma2Config(
-            vocab_size=32000,  # 256000
-            hidden_size=1024,  # 3072
-            intermediate_size=2048,  # 24576
-            num_hidden_layers=4,  # 28
-            num_attention_heads=4,  # 16
-            num_key_value_heads=4,  # 16
-            head_dim=256,
-            hidden_activation="gelu_pytorch_tanh",
-            max_position_embeddings=8192,
-            initializer_range=0.02,
-            rms_norm_eps=1e-06,
-            use_cache=True,
-            pad_token_id=0,
-            # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset
-            # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
-            bos_token_id=1,  # 128000
-            eos_token_id=2,  # 128001
-            tie_word_embeddings=True,
-            attention_bias=False,
-            attention_dropout=0.0,
-            attn_implementation="eager",
+        "mini_gemma1.1": MiniModelConfig(
+            liger_kernel_patch_func=apply_liger_kernel_to_gemma,
+            liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma,
+            model_class=GemmaForCausalLM,
+            mini_model_config=GemmaConfig(
+                vocab_size=32000,  # 256000
+                hidden_size=1024,  # 3072
+                intermediate_size=2048,  # 24576
+                num_hidden_layers=4,  # 28
+                num_attention_heads=4,  # 16
+                num_key_value_heads=4,  # 16
+                head_dim=256,
+                hidden_activation="gelu_pytorch_tanh",
+                max_position_embeddings=8192,
+                initializer_range=0.02,
+                rms_norm_eps=1e-06,
+                use_cache=True,
+                pad_token_id=0,
+                # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset
+                # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
+                bos_token_id=1,  # 128000
+                eos_token_id=2,  # 128001
+                tie_word_embeddings=True,
+                attention_bias=False,
+                attention_dropout=0.0,
+            ),
         ),
-    ),
-}
+        "mini_gemma2": MiniModelConfig(
+            liger_kernel_patch_func=apply_liger_kernel_to_gemma2,
+            liger_kernel_patch_revert_func=revert_liger_kernel_to_gemma2,
+            model_class=Gemma2ForCausalLM,
+            mini_model_config=Gemma2Config(
+                vocab_size=32000,  # 256000
+                hidden_size=1024,  # 3072
+                intermediate_size=2048,  # 24576
+                num_hidden_layers=4,  # 28
+                num_attention_heads=4,  # 16
+                num_key_value_heads=4,  # 16
+                head_dim=256,
+                hidden_activation="gelu_pytorch_tanh",
+                max_position_embeddings=8192,
+                initializer_range=0.02,
+                rms_norm_eps=1e-06,
+                use_cache=True,
+                pad_token_id=0,
+                # Special token ids/vocab size to match Mistral-7B tokenizer used to create the tokenized dataset
+                # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
+                bos_token_id=1,  # 128000
+                eos_token_id=2,  # 128001
+                tie_word_embeddings=True,
+                attention_bias=False,
+                attention_dropout=0.0,
+                attn_implementation="eager",
+            ),
+        ),
+    }
+)
 
 if LLAMA4_AVAILABLE:
     MINI_MODEL_SETUPS["mini_llama4"] = MiniModelConfig(
@@ -1822,6 +1864,24 @@ def run_mini_model(
             1e-2,
             marks=pytest.mark.skipif(not supports_bfloat16(), reason="bfloat16 not supported on this GPU"),
         ),
+        pytest.param(
+            "mini_ministral",
+            32,
+            1e-5,
+            torch.bfloat16,
+            1e-2,
+            5e-2,
+            1e-1,
+            1e-2,
+            1e-2,
+            1e-2,
+            marks=[
+                pytest.mark.skipif(not supports_bfloat16(), reason="bfloat16 not supported on this GPU"),
+                pytest.mark.skipif(
+                    not MINISTRAL_AVAILABLE, reason="Ministral not available in this version of transformers"
+                ),
+            ],
+        ),
         # TODO: mixtral is flaky so disable the test for now
         # pytest.param(
         #     "mini_mixtral",
diff --git a/test/convergence/fp32/test_mini_models.py b/test/convergence/fp32/test_mini_models.py
index 16107be1..c714b230 100644
--- a/test/convergence/fp32/test_mini_models.py
+++ b/test/convergence/fp32/test_mini_models.py
@@ -40,6 +40,7 @@ from liger_kernel.transformers import apply_liger_kernel_to_internvl
 from liger_kernel.transformers import apply_liger_kernel_to_llama
 from liger_kernel.transformers import apply_liger_kernel_to_llama4
 from liger_kernel.transformers import apply_liger_kernel_to_llava
+from liger_kernel.transformers import apply_liger_kernel_to_ministral
 from liger_kernel.transformers import apply_liger_kernel_to_mistral
 from liger_kernel.transformers import apply_liger_kernel_to_mixtral
 from liger_kernel.transformers import apply_liger_kernel_to_mllama
@@ -81,6 +82,7 @@ from test.utils import revert_liger_kernel_to_internvl
 from test.utils import revert_liger_kernel_to_llama
 from test.utils import revert_liger_kernel_to_llama4
 from test.utils import revert_liger_kernel_to_llava
+from test.utils import revert_liger_kernel_to_ministral
 from test.utils import revert_liger_kernel_to_mistral
 from test.utils import revert_liger_kernel_to_mixtral
 from test.utils import revert_liger_kernel_to_mllama
@@ -121,6 +123,14 @@ try:
 except ImportError:
     MLLAMA_AVAILABLE = False
 
+try:
+    from transformers.models.ministral.configuration_ministral import MinistralConfig
+    from transformers.models.ministral.modeling_ministral import MinistralForCausalLM
+
+    MINISTRAL_AVAILABLE = True
+except ImportError:
+    MINISTRAL_AVAILABLE = False
+
 try:
     # Qwen2-VL is only available in transformers>4.52.4
     import transformers
@@ -562,6 +572,32 @@ MINI_MODEL_SETUPS = {
         ),
     ),
 }
+if MINISTRAL_AVAILABLE:
+    MINI_MODEL_SETUPS["mini_ministral"] = MiniModelConfig(
+        liger_kernel_patch_func=apply_liger_kernel_to_ministral,
+        liger_kernel_patch_revert_func=revert_liger_kernel_to_ministral,
+        model_class=MinistralForCausalLM,
+        mini_model_config=MinistralConfig(
+            attention_dropout=0.0,
+            bos_token_id=1,
+            eos_token_id=2,
+            head_dim=128,
+            hidden_act="silu",
+            hidden_size=1024,
+            initializer_range=0.02,
+            intermediate_size=2048,
+            max_position_embeddings=32768,
+            num_attention_heads=8,
+            num_hidden_layers=4,
+            num_key_value_heads=2,
+            rms_norm_eps=1e-5,
+            sliding_window=4096,
+            tie_word_embeddings=False,
+            use_cache=True,
+            vocab_size=32000,
+            attn_implementation="sdpa",
+        ),
+    )
 if LLAMA4_AVAILABLE:
     MINI_MODEL_SETUPS["mini_llama4"] = MiniModelConfig(
         liger_kernel_patch_func=apply_liger_kernel_to_llama4,
@@ -1965,6 +2001,23 @@ def run_mini_model(
             1e-5,
             marks=[],
         ),
+        pytest.param(
+            "mini_ministral",
+            32,
+            1e-4,
+            torch.float32,
+            1e-8,
+            1e-5,
+            5e-3,
+            1e-5,
+            5e-3,
+            1e-5,
+            marks=[
+                pytest.mark.skipif(
+                    not MINISTRAL_AVAILABLE, reason="Ministral not available in this version of transformers"
+                ),
+            ],
+        ),
         # TODO: mixtral is flaky so disable the test for now
         # ("mini_mixtral", 32, 1e-4, torch.float32, 5e-4, 1e-4, 5e-3, 1e-5, 1e-2, 1e-5),
         # Gemma 1.1 and 2 has more tolerance because currently, the kernel is not a perfect match (casts are not done the same way)
diff --git a/test/convergence/fp32/test_mini_models_with_logits.py b/test/convergence/fp32/test_mini_models_with_logits.py
index d14d76b8..335ac130 100644
--- a/test/convergence/fp32/test_mini_models_with_logits.py
+++ b/test/convergence/fp32/test_mini_models_with_logits.py
@@ -39,6 +39,7 @@ from liger_kernel.transformers import apply_liger_kernel_to_internvl
 from liger_kernel.transformers import apply_liger_kernel_to_llama
 from liger_kernel.transformers import apply_liger_kernel_to_llama4
 from liger_kernel.transformers import apply_liger_kernel_to_llava
+from liger_kernel.transformers import apply_liger_kernel_to_ministral
 from liger_kernel.transformers import apply_liger_kernel_to_mistral
 from liger_kernel.transformers import apply_liger_kernel_to_mixtral
 from liger_kernel.transformers import apply_liger_kernel_to_mllama
@@ -78,6 +79,7 @@ from test.utils import revert_liger_kernel_to_internvl
 from test.utils import revert_liger_kernel_to_llama
 from test.utils import revert_liger_kernel_to_llama4
 from test.utils import revert_liger_kernel_to_llava
+from test.utils import revert_liger_kernel_to_ministral
 from test.utils import revert_liger_kernel_to_mistral
 from test.utils import revert_liger_kernel_to_mixtral
 from test.utils import revert_liger_kernel_to_mllama
@@ -108,6 +110,14 @@ try:
 except ImportError:
     LLAMA4_AVAILABLE = False
 
+try:
+    from transformers.models.ministral.configuration_ministral import MinistralConfig
+    from transformers.models.ministral.modeling_ministral import MinistralForCausalLM
+
+    MINISTRAL_AVAILABLE = True
+except ImportError:
+    MINISTRAL_AVAILABLE = False
+
 try:
     # Mllama is only available in transformers>=4.45.0
     from transformers.models.mllama.configuration_mllama import MllamaTextConfig
@@ -555,6 +565,34 @@ MINI_MODEL_SETUPS = {
         ),
     ),
 }
+
+if MINISTRAL_AVAILABLE:
+    MINI_MODEL_SETUPS["mini_ministral"] = MiniModelConfig(
+        liger_kernel_patch_func=apply_liger_kernel_to_ministral,
+        liger_kernel_patch_revert_func=revert_liger_kernel_to_ministral,
+        model_class=MinistralForCausalLM,
+        mini_model_config=MinistralConfig(
+            attention_dropout=0.0,
+            bos_token_id=1,
+            eos_token_id=2,
+            head_dim=128,
+            hidden_act="silu",
+            hidden_size=1024,
+            initializer_range=0.02,
+            intermediate_size=2048,
+            max_position_embeddings=32768,
+            num_attention_heads=8,
+            num_hidden_layers=4,
+            num_key_value_heads=2,
+            rms_norm_eps=1e-5,
+            sliding_window=4096,
+            tie_word_embeddings=False,
+            use_cache=True,
+            vocab_size=32000,
+            attn_implementation="sdpa",
+        ),
+    )
+
 if LLAMA4_AVAILABLE:
     MINI_MODEL_SETUPS["mini_llama4"] = MiniModelConfig(
         liger_kernel_patch_func=apply_liger_kernel_to_llama4,
@@ -1851,6 +1889,21 @@ def run_mini_model(
         ),
         ("mini_phi3", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5),
         ("mini_mistral", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5),
+        pytest.param(
+            "mini_ministral",
+            32,
+            1e-4,
+            torch.float32,
+            1e-8,
+            1e-5,
+            5e-3,
+            1e-5,
+            5e-3,
+            1e-5,
+            marks=pytest.mark.skipif(
+                not MINISTRAL_AVAILABLE, reason="Ministral not available in this version of transformers"
+            ),
+        ),
         # TODO: mixtral is flaky so disable the test for now
         # ("mini_mixtral", 32, 1e-4, torch.float32, 5e-4, 1e-4, 5e-3, 1e-5, 1e-2, 1e-5),
         # Gemma 1.1 and 2 has more tolerance because currently, the kernel is not a perfect match
diff --git a/test/transformers/test_monkey_patch.py b/test/transformers/test_monkey_patch.py
index fa7ee12c..542b45c7 100755
--- a/test/transformers/test_monkey_patch.py
+++ b/test/transformers/test_monkey_patch.py
@@ -29,6 +29,7 @@ from liger_kernel.transformers.model.falcon_h1 import lce_forward as falcon_h1_l
 from liger_kernel.transformers.model.gemma import lce_forward as gemma_lce_forward
 from liger_kernel.transformers.model.gemma2 import lce_forward as gemma2_lce_forward
 from liger_kernel.transformers.model.llama import lce_forward as llama_lce_forward
+from liger_kernel.transformers.model.ministral import lce_forward as ministral_lce_forward
 from liger_kernel.transformers.model.mistral import lce_forward as mistral_lce_forward
 from liger_kernel.transformers.model.mixtral import lce_forward as mixtral_lce_forward
 from liger_kernel.transformers.model.mllama import lce_forward as mllama_lce_forward
@@ -92,6 +93,15 @@ def is_llama4_available():
         return False
 
 
+def is_ministral_available():
+    try:
+        import transformers.models.ministral  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
 def is_qwen3_available():
     try:
         import transformers.models.qwen3  # noqa: F401
@@ -266,6 +276,7 @@ def test_import_from_root():
         from liger_kernel.transformers import apply_liger_kernel_to_glm4v_moe  # noqa: F401
         from liger_kernel.transformers import apply_liger_kernel_to_internvl  # noqa: F401
         from liger_kernel.transformers import apply_liger_kernel_to_llama  # noqa: F401
+        from liger_kernel.transformers import apply_liger_kernel_to_ministral  # noqa: F401
         from liger_kernel.transformers import apply_liger_kernel_to_mistral  # noqa: F401
         from liger_kernel.transformers import apply_liger_kernel_to_mixtral  # noqa: F401
         from liger_kernel.transformers import apply_liger_kernel_to_mllama  # noqa: F401
@@ -1470,6 +1481,49 @@ def test_apply_liger_kernel_to_instance_for_mistral():
             pytest.fail(f"An exception occured in extra_expr: {type(e).__name__} - {e}")
 
 
+@pytest.mark.skipif(not is_ministral_available(), reason="ministral module not available")
+def test_apply_liger_kernel_to_instance_for_ministral():
+    # Ensure any monkey patching is cleaned up for subsequent tests
+    with patch("transformers.models.ministral.modeling_ministral"):
+        # Instantiate a dummy model
+        config = transformers.models.ministral.configuration_ministral.MinistralConfig(
+            dtype=torch.bfloat16,
+            rms_norm_eps=1e-5,
+            hidden_size=32,
+            intermediate_size=64,
+            hidden_act="silu",
+            num_hidden_layers=2,
+            head_dim=16,
+            num_attention_heads=2,
+            num_key_value_heads=2,
+        )
+        dummy_model_instance = AutoModelForCausalLM.from_config(config)
+
+        # Check that model instance variables are not yet patched with Liger modules
+        assert inspect.getsource(dummy_model_instance.forward) != inspect.getsource(ministral_lce_forward)
+        assert inspect.getsource(dummy_model_instance.model.norm.forward) != inspect.getsource(LigerRMSNorm.forward)
+        for layer in dummy_model_instance.model.layers:
+            assert inspect.getsource(layer.mlp.forward) != inspect.getsource(LigerSwiGLUMLP.forward)
+            assert inspect.getsource(layer.input_layernorm.forward) != inspect.getsource(LigerRMSNorm.forward)
+            assert inspect.getsource(layer.post_attention_layernorm.forward) != inspect.getsource(LigerRMSNorm.forward)
+
+        # Test applying kernels to the model instance
+        _apply_liger_kernel_to_instance(model=dummy_model_instance)
+
+        # Check that the model's instance variables were correctly patched with Liger modules
+        assert inspect.getsource(dummy_model_instance.forward) == inspect.getsource(ministral_lce_forward)
+        assert inspect.getsource(dummy_model_instance.model.norm.forward) == inspect.getsource(LigerRMSNorm.forward)
+        for layer in dummy_model_instance.model.layers:
+            assert inspect.getsource(layer.mlp.forward) == inspect.getsource(LigerSwiGLUMLP.forward)
+            assert inspect.getsource(layer.input_layernorm.forward) == inspect.getsource(LigerRMSNorm.forward)
+            assert inspect.getsource(layer.post_attention_layernorm.forward) == inspect.getsource(LigerRMSNorm.forward)
+
+        try:
+            print(dummy_model_instance)
+        except Exception as e:
+            pytest.fail(f"An exception occured in extra_expr: {type(e).__name__} - {e}")
+
+
 def test_apply_liger_kernel_to_instance_for_mixtral():
     # Ensure any monkey patching is cleaned up for subsequent tests
     with patch("transformers.models.mixtral.modeling_mixtral"):
diff --git a/test/utils.py b/test/utils.py
index ba0f7d48..ae286fd0 100644
--- a/test/utils.py
+++ b/test/utils.py
@@ -414,6 +414,18 @@ def revert_liger_kernel_to_llama4(model_config: MiniModelConfig, model_type: str
     print("Liger kernel patches have been reverted.")
 
 
+def revert_liger_kernel_to_ministral(model_config: MiniModelConfig):
+    """
+    Revert all Liger kernel patches applied to Ministral.
+    """
+
+    from transformers.models.ministral import modeling_ministral
+
+    importlib.reload(modeling_ministral)
+    model_config.model_class = modeling_ministral.MinistralForCausalLM
+    print("Liger kernel patches have been reverted.")
+
+
 def revert_liger_kernel_to_mistral(model_config: MiniModelConfig):
     """
     Revert all Liger kernel patches applied to Mistral.