diff --git a/flash_attn/models/llama.py b/flash_attn/models/llama.py
index 40d4073..993a282 100644
--- a/flash_attn/models/llama.py
+++ b/flash_attn/models/llama.py
@@ -10,6 +10,7 @@ from typing import Union
 
 import torch
 import torch.nn.functional as F
+from sentencepiece import SentencePieceProcessor
 from transformers import GPT2Config, LlamaConfig
 
 
@@ -308,7 +309,30 @@ def config_from_meta_checkpoint(
         num_attention_heads=params["n_heads"],
         num_hidden_layers=params["n_layers"],
         rms_norm_eps=params["norm_eps"],
+        num_key_value_heads=params.get("n_kv_heads", None),
     )
+    multiple_of = params.get("multiple_of", 1)
+    ffn_dim_multiplier = params.get("ffn_dim_multiplier", None)
+
+    # Compute the hidden dimension of the MLP
+    # https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/model.py#L224
+    intermediate_size = 4 * config.hidden_size
+    # https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/model.py#L195-L199
+    intermediate_size = int(2 * intermediate_size / 3)
+    # custom dim factor multiplier
+    if ffn_dim_multiplier is not None:
+        intermediate_size = int(ffn_dim_multiplier * intermediate_size)
+    intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of)
+
+    config.intermediate_size = intermediate_size
+    if "rope_theta" in params:
+        config.rotary_emb_base = params["rope_theta"]
+    config.vocab_size = 32000
+    # some CodeLLaMa have vocab_size 32000, some 32016
+    # Sadly it's not specified in the `params.json` file :(
+    tokenizer = Path(checkpoint_path) / model_name / "tokenizer.model"
+    if tokenizer.is_file():
+        config.vocab_size = SentencePieceProcessor(str(tokenizer)).vocab_size()
     return config
 
 
@@ -364,4 +388,6 @@ def llama_config_to_gpt2_config(llama_config: LlamaConfig) -> GPT2Config:
         out_proj_bias=False,
         mlp_fc1_bias=False,
         mlp_fc2_bias=False,
+        rotary_emb_base=getattr(llama_config, "rotary_emb_base", 10000.0),
+        n_head_kv=llama_config.num_key_value_heads,
     )