diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 4ce0b49e6..4a8aec8cd 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -2938,6 +2938,7 @@ class T5Model(Model):
 
         return [(self.map_tensor_name(name), data_torch)]
 
+
 @Model.register("JAISLMHeadModel")
 class JaisModel(Model):
     model_arch = gguf.MODEL_ARCH.JAIS
@@ -2954,7 +2955,7 @@ class JaisModel(Model):
         self.embeddings_scale = 1.0
         # note: For some JAIS flavors, output is tied to (same as) wte in original model
         self.output_is_wte = False
-        if  'mup_embeddings_scale' in self.hparams:
+        if 'mup_embeddings_scale' in self.hparams:
             self.output_is_wte = True   # Hack (?)
             self.embeddings_scale = self.hparams['mup_embeddings_scale']
         elif 'embeddings_scale' in self.hparams:
@@ -2963,7 +2964,7 @@ class JaisModel(Model):
             assert False
 
         self.width_scale = 1.0
-        if  'mup_output_alpha' in self.hparams:
+        if 'mup_output_alpha' in self.hparams:
             assert 'mup_width_scale' in self.hparams
             self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
         elif 'width_scale' in self.hparams:
@@ -2984,13 +2985,27 @@ class JaisModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
         self.gguf_writer.add_file_type(self.ftype)
 
+        # Hack to populate self.tensor_names
+        all(self.get_tensors())
+        if 'transformer.relative_pe.slopes' not in self.tensor_names:
+            self.gguf_writer.add_max_alibi_bias(8.0)
+        # else set later
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
         tensors: list[tuple[str, Tensor]] = []
 
         # we don't need these
-        if name.endswith((".attn.bias", "relative_pe.slopes")):
+        if name.endswith((".attn.bias")):
+            return tensors
+
+        if name.endswith(("relative_pe.slopes")):
+            # calculate ALiBi bias
+            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
+            first_val = float(data_torch._data[0])
+            alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
+            self.gguf_writer.add_max_alibi_bias(alibi_bias)
             return tensors
 
         if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
diff --git a/src/llama.cpp b/src/llama.cpp
index 307bcef84..8549388f1 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -4902,8 +4902,8 @@ static void llm_load_hparams(
         case LLM_ARCH_JAIS:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                // TODO: become GGUF KV parameter
-                hparams.f_max_alibi_bias = 8.0f;
+                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1_3B; break;
                     case 40: model.type = e_model::MODEL_13B; break;