diff --git a/lmdeploy/lite/apis/calibrate.py b/lmdeploy/lite/apis/calibrate.py
index 555d0c297..372480f6f 100644
--- a/lmdeploy/lite/apis/calibrate.py
+++ b/lmdeploy/lite/apis/calibrate.py
@@ -25,7 +25,11 @@
     'MGMLlamaForCausalLM': 'LlamaDecoderLayer',  # mini gemini
     'InternLMXComposer2ForCausalLM': 'InternLM2DecoderLayer',
     'Phi3ForCausalLM': 'Phi3DecoderLayer',
-    'ChatGLMForConditionalGeneration': 'GLMBlock'
+    'ChatGLMForConditionalGeneration': 'GLMBlock',
+    'MixtralForCausalLM': 'MixtralDecoderLayer',
+    'Qwen2VLForConditionalGeneration': 'Qwen2VLDecoderLayer',
+    'MistralForCausalLM': 'MistralDecoderLayer',
+    'InternLM3MoEForCausalLM': 'InternLM3MoEDecoderLayer',
 }
 
 NORM_TYPE_MAP = {
@@ -41,7 +45,11 @@
     'MGMLlamaForCausalLM': 'LlamaRMSNorm',  # mini gemini
     'InternLMXComposer2ForCausalLM': 'InternLM2RMSNorm',
     'Phi3ForCausalLM': 'Phi3RMSNorm',
-    'ChatGLMForConditionalGeneration': 'RMSNorm'
+    'ChatGLMForConditionalGeneration': 'RMSNorm',
+    'MixtralForCausalLM': 'MixtralRMSNorm',
+    'Qwen2VLForConditionalGeneration': 'Qwen2RMSNorm',
+    'MistralForCausalLM': 'MistralRMSNorm',
+    'InternLM3MoEForCausalLM': 'InternLM3MoERMSNorm',
 }
 
 HEAD_NAME_MAP = {
@@ -57,7 +65,11 @@
     'MGMLlamaForCausalLM': 'lm_head',  # mini gemini
     'InternLMXComposer2ForCausalLM': 'output',
     'Phi3ForCausalLM': 'lm_head',
-    'ChatGLMForConditionalGeneration': 'output_layer'
+    'ChatGLMForConditionalGeneration': 'output_layer',
+    'MixtralForCausalLM': 'lm_head',
+    'Qwen2VLForConditionalGeneration': 'lm_head',
+    'MistralForCausalLM': 'lm_head',
+    'InternLM3MoEForCausalLM': 'output',
 }
 
 
@@ -185,7 +197,7 @@ def calibrate(model: str,
                                                   trust_remote_code=True)
 
         model = load_hf_from_pretrained(model,
-                                        torch_dtype=torch.float16,
+                                        torch_dtype=torch.bfloat16,
                                         trust_remote_code=True)
         vl_model = None
     elif model_type == 'vlm':
diff --git a/lmdeploy/lite/apis/gptq.py b/lmdeploy/lite/apis/gptq.py
index 12b88a52c..3d8049bde 100644
--- a/lmdeploy/lite/apis/gptq.py
+++ b/lmdeploy/lite/apis/gptq.py
@@ -54,6 +54,10 @@ def auto_gptq(model: str,
     SUPPORTED_MODELS.append('internlm2')
     GPTQ_CAUSAL_LM_MODEL_MAP.update(dict(internlm2=InternLM2GPTQForCausalLM))
 
+    from ..modeling.internlm3_moe_gptq import InternLM3MoEGPTQForCausalLM
+    SUPPORTED_MODELS.append('InternLM3_MoE')
+    GPTQ_CAUSAL_LM_MODEL_MAP.update(dict(InternLM3_MoE=InternLM3MoEGPTQForCausalLM))
+
     pretrained_model_dir = model
     quantized_model_dir = work_dir
 
@@ -85,6 +89,7 @@ def auto_gptq(model: str,
     # the model will always be loaded into CPU memory
     model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir,
                                                 quantize_config,
+                                                torch_dtype=torch.bfloat16,
                                                 revision=revision,
                                                 trust_remote_code=True)
 
diff --git a/lmdeploy/lite/quantization/awq.py b/lmdeploy/lite/quantization/awq.py
index eca7d46d7..fb701329a 100644
--- a/lmdeploy/lite/quantization/awq.py
+++ b/lmdeploy/lite/quantization/awq.py
@@ -43,7 +43,29 @@
     'GLMBlock': {
         'input_layernorm': ['self_attention.query_key_value'],
         'post_attention_layernorm': ['mlp.dense_h_to_4h']
-    }
+    },
+    'MixtralDecoderLayer': {
+        'input_layernorm':
+        ['self_attn.k_proj', 'self_attn.q_proj', 'self_attn.v_proj'],
+        'post_attention_layernorm':
+        ['block_sparse_moe.experts.{i}.w1', 'block_sparse_moe.experts.{i}.w3']
+    },
+    'InternLM3MoEDecoderLayer': {
+        'attention_norm':
+        ['attention.wqkv'],
+        'ffn_norm':
+        ['feed_forward.experts.fused_w1w3']
+    },
+    'Qwen2VLDecoderLayer': {
+        'input_layernorm':
+        ['self_attn.k_proj', 'self_attn.q_proj', 'self_attn.v_proj'],
+        'post_attention_layernorm': ['mlp.gate_proj', 'mlp.up_proj']
+    },
+    'MistralDecoderLayer': {
+        'input_layernorm':
+        ['self_attn.k_proj', 'self_attn.q_proj', 'self_attn.v_proj'],
+        'post_attention_layernorm': ['mlp.gate_proj', 'mlp.up_proj']
+    },
 }
 
 FC_FCS_MAP = {
@@ -80,6 +102,22 @@
     'GLMBlock': {
         # 'self_attention.query_key_value': ['self_attention.dense']
         # 'mlp.dense_h_to_4h': ['mlp.dense_4h_to_h']
+    },
+    'MixtralDecoderLayer': {
+        'self_attn.v_proj': ['self_attn.o_proj'],
+        'block_sparse_moe.experts.{i}.w3': ['block_sparse_moe.experts.{i}.w2']
+    },
+    'InternLM3MoEDecoderLayer': {
+        'feed_forward.experts.fused_w1w3':
+        ['feed_forward.experts.w2']
+    },
+    'Qwen2VLDecoderLayer': {
+        'self_attn.v_proj': ['self_attn.o_proj'],
+        'mlp.up_proj': ['mlp.down_proj']
+    },
+    'MistralDecoderLayer': {
+        'self_attn.v_proj': ['self_attn.o_proj'],
+        'mlp.up_proj': ['mlp.down_proj']
     }
 }