diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index d48bc28b..e377c874 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -632,6 +632,10 @@ def _optimize_pre(model):
                 module.rope_base = rope_base
                 del module.c_attn
         model.apply(split_qkv_proj_func)
+    if model.config.model_type == "stablelm":
+        from ipex_llm.transformers.models.stablelm import merge_qkv
+        model.apply(merge_qkv)
+
     return model
 
 
@@ -1336,5 +1340,16 @@ def _optimize_post(model, lightweight_bmm=False):
         convert_forward(model,
                         module.BertEncoder,
                         encoder_forward)
+    elif model.config.model_type == 'stablelm':
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from ipex_llm.transformers.models.stablelm import stablelm_attention_forward
+        convert_forward(model,
+                        module.StableLmAttention,
+                        stablelm_attention_forward
+                        )
+        convert_forward(model,
+                        module.StableLmMLP,
+                        llama_mlp_forward)
 
     return model
diff --git a/python/llm/src/ipex_llm/transformers/models/stablelm.py b/python/llm/src/ipex_llm/transformers/models/stablelm.py
new file mode 100644
index 00000000..b311c740
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/models/stablelm.py
@@ -0,0 +1,244 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Some parts of this file is adapted from
+# https://github.com/huggingface/transformers/blob/v4.38.0/src/transformers/models/stablelm/modeling_stablelm.py
+# which is licensed under Apache License 2.0:
+#
+# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import math
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from transformers.models.stablelm.modeling_stablelm import StableLmAttention
+
+from ipex_llm.utils.common import invalidInputError
+from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
+from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, \
+    apply_rotary_pos_emb_no_cache_xpu
+from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
+from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
+from ipex_llm.transformers.models.mistral import should_use_fuse_rope, repeat_kv
+try:
+    from transformers.cache_utils import Cache
+except ImportError:
+    Cache = Tuple[torch.Tensor]
+
+
+KV_CACHE_ALLOC_BLOCK_LENGTH = 256
+
+
+def merge_qkv(module: torch.nn.Module):
+    if isinstance(module, StableLmAttention):
+        new_weight = torch.cat([
+            module.q_proj.weight.data,
+            module.k_proj.weight.data,
+            module.v_proj.weight.data,
+        ], dim=0)
+
+        qkv_proj = torch.nn.Linear(0, 0, bias=False)
+        qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
+        qkv_proj.in_features = new_weight.size(1)
+        qkv_proj.out_features = new_weight.size(0)
+        module.qkv_proj = qkv_proj
+
+        del module.q_proj, module.k_proj, module.v_proj
+
+
+def stablelm_attention_forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor]=None,
+        position_ids: Optional[torch.LongTensor]=None,
+        past_key_value: Optional[Cache]=None,
+        output_attentions: bool=False,
+        use_cache: bool=False,
+        **kwargs
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+
+    bsz, q_len, _ = hidden_states.size()
+    device = hidden_states.device
+    # for flash attention
+    original_dtype = hidden_states.dtype
+
+    use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
+    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
+
+    qkv = self.qkv_proj(hidden_states)
+    qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+    qkv = qkv.transpose(1, 2)
+    query_states, key_states, value_states = qkv.split([self.num_heads,
+                                                        self.num_heads,
+                                                        self.num_heads], dim=1)
+
+    kv_seq_len = key_states.shape[-2]
+
+    if past_key_value is not None:
+        if self.layer_idx is None:
+            invalidInputError(False,
+                              "The cache structure has changed since version v4.36. "
+                              f"If you are using {self.__class__.__name__} for "
+                              "auto-regressive decodingwith k/v caching, please make sure "
+                              "to initialize the attention class with a layer index.")
+        kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+    # Partial rotary embedding
+    query_rot, query_pass = (
+        query_states[..., : self.rotary_emb.dim],
+        query_states[..., self.rotary_emb.dim:],
+    )
+    key_rot, key_pass = (
+        key_states[..., : self.rotary_emb.dim],
+        key_states[..., self.rotary_emb.dim:],
+    )
+    if use_fuse_rope:
+        query_rot, key_rot = apply_rotary_pos_emb_no_cache_xpu(query_rot,
+                                                               key_rot,
+                                                               position_ids,
+                                                               "stablelm")
+    else:
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(query_rot,
+                                                  key_rot,
+                                                  cos,
+                                                  sin,
+                                                  position_ids,
+                                                  "stablelm")
+
+    # [batch_size, seq_length, num_heads, head_dim]
+    query_states = torch.cat((query_rot, query_pass), dim=-1)
+    key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+    if past_key_value is not None:
+        # update the number of seen tokens
+        if self.layer_idx == 0:
+            past_key_value.seen_tokens += key_states.shape[-2]
+
+        # reuse k, v, self_attention
+        # update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
+        if len(past_key_value.key_cache) <= self.layer_idx:
+            past_key_value.key_cache.append(key_states)
+            past_key_value.value_cache.append(value_states)
+        else:
+            cache_k = past_key_value.key_cache[self.layer_idx]
+            cache_v = past_key_value.value_cache[self.layer_idx]
+
+            if not enough_kv_room:
+                # allocate new
+                new_c_k, new_c_v = extend_kv_cache(bsz,
+                                                   self.num_key_value_heads,  # Support GQA
+                                                   self.head_dim,
+                                                   cache_k.size(2),
+                                                   kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
+                                                   dtype=cache_k.dtype,
+                                                   device=device)
+
+                new_c_k[:] = cache_k
+                new_c_v[:] = cache_v
+                cache_k = new_c_k
+                cache_v = new_c_v
+
+            key_states, value_states = append_kv_cache(cache_k, cache_v,
+                                                       key_states, value_states)
+
+            # update past_key_value
+            past_key_value.key_cache[self.layer_idx] = key_states
+            past_key_value.value_cache[self.layer_idx] = value_states
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if not self.training and not hidden_states.requires_grad and \
+            use_flash_attention(query_states, key_states, attention_mask):
+        attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
+                                                     key_states.to(device, dtype=torch.float16),
+                                                     value_states.to(device, dtype=torch.float16),
+                                                     is_causal=True)
+        attn_weights = None
+    elif not self.training and not hidden_states.requires_grad and \
+            use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states, attention_mask):
+        import linear_fp16_esimd
+        attn_output = linear_fp16_esimd.sdp_forward(query_states,
+                                                    key_states,
+                                                    value_states)
+        attn_output = attn_output.view(query_states.shape)
+        attn_weights = None
+    else:
+        attn_weights = torch.matmul(
+            query_states,
+            key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            invalidInputError(
+                False,
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
+                f" but is {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                invalidInputError(
+                    False,
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
+                    f" but is {attention_mask.size()}"
+                )
+
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = \
+            nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query_states.dtype)
+        attn_weights = self.attention_dropout(attn_weights)
+
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            invalidInputError(
+                False,
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
+                f" but is {attn_output.size()}"
+            )
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output.to(original_dtype), attn_weights, past_key_value
diff --git a/python/llm/src/ipex_llm/transformers/models/utils.py b/python/llm/src/ipex_llm/transformers/models/utils.py
index 2241a9bc..3fa48489 100644
--- a/python/llm/src/ipex_llm/transformers/models/utils.py
+++ b/python/llm/src/ipex_llm/transformers/models/utils.py
@@ -168,7 +168,7 @@ def rotate_every_two(x):
 
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family):
     if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral",
-                        "mixtral", "qwen2", "yuan"]:
+                        "mixtral", "qwen2", "yuan", "stablelm"]:
         # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
         cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
         sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
@@ -207,7 +207,7 @@ def apply_rotary_pos_emb_no_cache_xpu(q, k, position_ids, model_family, rope_the
     q_embed = torch.empty(q.shape, dtype=q.dtype, device=q.device)
     k_embed = torch.empty(k.shape, dtype=k.dtype, device=k.device)
     if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral",
-                        "mixtral"]:
+                        "mixtral", "stablelm"]:
         linear_q4_0.apply_rotary_embedding_half_q_and_k(q, k, position_ids,
                                                         q_embed, k_embed, rope_theta)
         return q_embed, k_embed