diff --git a/koboldcpp.py b/koboldcpp.py
index 1f94fdbb0..6398115c8 100755
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -5155,7 +5155,7 @@ def show_gui():
 
     tabs = ctk.CTkFrame(root, corner_radius = 0, width=windowwidth, height=windowheight-50)
     tabs.grid(row=0, stick="nsew")
-    tabnames= ["Quick Launch", "Hardware", "Tokens", "Loaded Files", "Network", "Horde Worker","Image Gen","Audio","Admin","Extra"]
+    tabnames= ["Quick Launch", "Hardware", "Context", "Loaded Files", "Network", "Horde Worker","Image Gen","Audio","Admin","Extra"]
     navbuttons = {}
     navbuttonframe = ctk.CTkFrame(tabs, width=int(104), height=int(tabs.cget("height")))
     navbuttonframe.grid(row=0, column=0, padx=2,pady=2)
@@ -5894,28 +5894,30 @@ def show_gui():
     makeslider(hardware_tab, "Batch Size:", batchsize_text, blas_size_var, 0, len(batchsize_values)-1, 16,width=200, set=6,tooltip="How many tokens to process at once per batch.\nLarger values use more memory.")
     blas_size_var.trace_add("write", changed_gpulayers_estimate)
 
-    makecheckbox(hardware_tab, "AutoFit (llama.cpp mode)", autofit_var, 100,0, tooltiptxt="Automatically attempt to fit the model in the best possible way. Overrides everything else. Not recommended for multi model setups. Experimental.")
+    makecheckbox(hardware_tab, "Use FlashAttention", flashattention_var, 100, command=toggleflashattn,  tooltiptxt="Enable flash attention for GGUF models.")
+
+    makecheckbox(hardware_tab, "AutoFit (llama.cpp mode)", autofit_var, 100,0,padx=160, tooltiptxt="Automatically attempt to fit the model in the best possible way. Overrides everything else. Not recommended for multi model setups. Experimental.")
     ctk.CTkButton(hardware_tab , text = "Run Benchmark", command = guibench ).grid(row=110,column=0, stick="nw", padx= 8, pady=2)
 
 
-    # Tokens Tab
-    tokens_tab = tabcontent["Tokens"]
-    # tokens checkboxes
-    smartcontextbox = makecheckbox(tokens_tab, "Use SmartContext", smartcontext_var, 1,tooltiptxt="Uses SmartContext. Now considered outdated and not recommended.\nCheck the wiki for more info.")
-    makecheckbox(tokens_tab, "Use ContextShift", contextshift_var, 2,tooltiptxt="Uses Context Shifting to reduce reprocessing.\nRecommended. Check the wiki for more info.", command=togglectxshift)
-    makecheckbox(tokens_tab, "Use FastForwarding", fastforward_var, 3,tooltiptxt="Use fast forwarding to recycle previous context (always reprocess if disabled).\nRecommended.", command=togglefastforward)
-    makecheckbox(tokens_tab, "Use Sliding Window Attention (SWA)", swa_var, 4,tooltiptxt="Allows Sliding Window Attention (SWA) KV Cache, which saves memory but cannot be used with context shifting.", command=toggleswa)
-    makecheckbox(tokens_tab, "Use SmartCache", smartcache_var, 5,tooltiptxt="Enables intelligent context switching by saving KV cache snapshots to RAM. Requires fast forwarding.", command=togglesmartcache)
+    # Context Tab
+    context_tab = tabcontent["Context"]
+    # Context checkboxes
+    smartcontextbox = makecheckbox(context_tab, "Use SmartContext", smartcontext_var, 1,tooltiptxt="Uses SmartContext. Now considered outdated and not recommended.\nCheck the wiki for more info.")
+    makecheckbox(context_tab, "Use ContextShift", contextshift_var, 2,tooltiptxt="Uses Context Shifting to reduce reprocessing.\nRecommended. Check the wiki for more info.", command=togglectxshift)
+    makecheckbox(context_tab, "Use FastForwarding", fastforward_var, 3,tooltiptxt="Use fast forwarding to recycle previous context (always reprocess if disabled).\nRecommended.", command=togglefastforward)
+    makecheckbox(context_tab, "Use Sliding Window Attention (SWA)", swa_var, 4,tooltiptxt="Allows Sliding Window Attention (SWA) KV Cache, which saves memory but cannot be used with context shifting.", command=toggleswa)
+    makecheckbox(context_tab, "Use SmartCache", smartcache_var, 5,tooltiptxt="Enables intelligent context switching by saving KV cache snapshots to RAM. Requires fast forwarding.", command=togglesmartcache)
 
     # context size
-    makeslider(tokens_tab, "Context Size:",contextsize_text, context_var, 0, len(contextsize_text)-1, 18, width=280, set=7,tooltip="What is the maximum context size to support. Model specific. You cannot exceed it.\nLarger contexts require more memory, and not all models support it.")
+    makeslider(context_tab, "Context Size:",contextsize_text, context_var, 0, len(contextsize_text)-1, 18, width=280, set=7,tooltip="What is the maximum context size to support. Model specific. You cannot exceed it.\nLarger contexts require more memory, and not all models support it.")
     context_var.trace_add("write", changed_gpulayers_estimate)
-    makelabelentry(tokens_tab, "Default Gen Amt:", defaultgenamt_var, row=20, padx=(120), singleline=True, tooltip="How many tokens to generate by default, if not specified. Must be smaller than context size. Usually, your frontend GUI will override this.")
-    makelabelentry(tokens_tab, "Prompt Limit:", genlimit_var, row=20, padx=(300), singleline=True, tooltip="If set, restricts max output tokens to this limit regardless of API request. Set to 0 to disable.",labelpadx=(210))
+    makelabelentry(context_tab, "Default Gen Amt:", defaultgenamt_var, row=20, padx=(120), singleline=True, tooltip="How many tokens to generate by default, if not specified. Must be smaller than context size. Usually, your frontend GUI will override this.")
+    makelabelentry(context_tab, "Prompt Limit:", genlimit_var, row=20, padx=(300), singleline=True, tooltip="If set, restricts max output tokens to this limit regardless of API request. Set to 0 to disable.",labelpadx=(210))
 
-    nativectx_entry, nativectx_label = makelabelentry(tokens_tab, "Override Native Context:", customrope_nativectx, row=23, padx=(146), singleline=True, tooltip="Overrides the native trained context of the loaded model with a custom value to be used for Rope scaling.")
-    customrope_scale_entry, customrope_scale_label = makelabelentry(tokens_tab, "RoPE Scale:", customrope_scale, row=23, padx=(100), singleline=True, tooltip="For Linear RoPE scaling. RoPE frequency scale.")
-    customrope_base_entry, customrope_base_label = makelabelentry(tokens_tab, "Base:", customrope_base, row=23, padx=(220), singleline=True, tooltip="For NTK Aware Scaling. RoPE frequency base.",labelpadx=(180))
+    nativectx_entry, nativectx_label = makelabelentry(context_tab, "Override Native Context:", customrope_nativectx, row=23, padx=(146), singleline=True, tooltip="Overrides the native trained context of the loaded model with a custom value to be used for Rope scaling.")
+    customrope_scale_entry, customrope_scale_label = makelabelentry(context_tab, "RoPE Scale:", customrope_scale, row=23, padx=(100), singleline=True, tooltip="For Linear RoPE scaling. RoPE frequency scale.")
+    customrope_base_entry, customrope_base_label = makelabelentry(context_tab, "Base:", customrope_base, row=23, padx=(220), singleline=True, tooltip="For NTK Aware Scaling. RoPE frequency base.",labelpadx=(180))
     def togglerope(a,b,c):
         manualropebox.grid_remove()
         nativectx_label.grid_remove()
@@ -5935,16 +5937,15 @@ def show_gui():
                 nativectx_label.grid(row=23, column=0, padx=8, pady=1, stick="nw")
                 nativectx_entry.grid(row=23, column=0, padx=(146), pady=1, stick="nw")
 
-    manualropebox = makecheckbox(tokens_tab, "Manual Rope Scale", variable=manualrope_var, row=22, command=togglerope, padx=(200), tooltiptxt="Set RoPE base and scale manually.")
+    manualropebox = makecheckbox(context_tab, "Manual Rope Scale", variable=manualrope_var, row=22, command=togglerope, padx=(200), tooltiptxt="Set RoPE base and scale manually.")
 
-    makecheckbox(tokens_tab, "Custom RoPE Config", variable=customrope_var, row=22, command=togglerope,tooltiptxt="Override the default RoPE configuration with custom RoPE scaling.")
-    makecheckbox(tokens_tab, "Use FlashAttention", flashattention_var, 28, command=toggleflashattn,  tooltiptxt="Enable flash attention for GGUF models.")
-    noqkvlabel = makelabel(tokens_tab,"(Note: QuantKV works best with flash attention)",28,0,"Only K cache can be quantized, and performance can suffer.\nIn some cases, it might even use more VRAM when doing a full offload.",padx=160)
+    makecheckbox(context_tab, "Custom RoPE Config", variable=customrope_var, row=22, command=togglerope,tooltiptxt="Override the default RoPE configuration with custom RoPE scaling.")
+    noqkvlabel = makelabel(context_tab,"(Note: QuantKV works best with flash attention)",30,0,"Only K cache can be quantized, and performance can suffer.\nIn some cases, it might even use more VRAM when doing a full offload.",padx=160)
     noqkvlabel.configure(text_color="#ff5555")
-    qkvslider,qkvlabel,qkvtitle = makeslider(tokens_tab, "Quantize KV Cache:", quantkv_text, quantkv_var, 0, 2, 30, set=0,tooltip="Enable quantization of KV cache.\nRequires FlashAttention for full effect, otherwise only K cache is quantized.")
+    qkvslider,qkvlabel,qkvtitle = makeslider(context_tab, "Quantize KV Cache:", quantkv_text, quantkv_var, 0, 2, 30, set=0,tooltip="Enable quantization of KV cache.\nRequires FlashAttention for full effect, otherwise only K cache is quantized.")
     quantkv_var.trace_add("write", toggleflashattn)
-    makecheckbox(tokens_tab, "No BOS Token", nobostoken_var, 43, tooltiptxt="Prevents BOS token from being added at the start of any prompt. Usually NOT recommended for most models.")
-    makecheckbox(tokens_tab, "Enable Guidance", enableguidance_var, 43,padx=(140), tooltiptxt="Enables the use of Classifier-Free-Guidance, which allows the use of negative prompts. Has performance and memory impact.")
+    makecheckbox(context_tab, "No BOS Token", nobostoken_var, 43, tooltiptxt="Prevents BOS token from being added at the start of any prompt. Usually NOT recommended for most models.")
+    makecheckbox(context_tab, "Enable Guidance", enableguidance_var, 43,padx=(140), tooltiptxt="Enables the use of Classifier-Free-Guidance, which allows the use of negative prompts. Has performance and memory impact.")
     def togglejinja(a,b,c):
         if jinja_var.get()==1:
             jinjatoolsbox.grid()
@@ -5952,13 +5953,13 @@ def show_gui():
             jinja_tools_var.set(0)
             jinjatoolsbox.grid_remove()
         changed_gpulayers_estimate()
-    makecheckbox(tokens_tab, "Use Jinja", jinja_var, row=45, command=togglejinja, tooltiptxt="Enables using jinja chat template formatting for chat completions endpoint. Other endpoints are unaffected.")
-    jinjatoolsbox = makecheckbox(tokens_tab, "Jinja for Tools", jinja_tools_var, row=45 ,padx=(140), tooltiptxt="Allows jinja even with tool calls. If unchecked, jinja will be disabled when tools are used.")
+    makecheckbox(context_tab, "Use Jinja", jinja_var, row=45, command=togglejinja, tooltiptxt="Enables using jinja chat template formatting for chat completions endpoint. Other endpoints are unaffected.")
+    jinjatoolsbox = makecheckbox(context_tab, "Jinja for Tools", jinja_tools_var, row=45 ,padx=(140), tooltiptxt="Allows jinja even with tool calls. If unchecked, jinja will be disabled when tools are used.")
     jinja_var.trace_add("write", togglejinja)
-    makelabelentry(tokens_tab, "MoE Experts:", moeexperts_var, row=55, padx=(120), singleline=True, tooltip="Override number of MoE experts.")
-    makelabelentry(tokens_tab, "MoE CPU Layers:", moecpu_var, row=55, padx=(320), singleline=True, tooltip="Force Mixture of Experts (MoE) weights of the first N layers to the CPU.\nSetting it higher than GPU layers has no effect.", labelpadx=(210))
-    makelabelentry(tokens_tab, "Override KV:", override_kv_var, row=57, padx=(120), singleline=True, width=150, tooltip="Override metadata value by key. Separate multiple values with commas. Format is name=type:value. Types: int, float, bool, str")
-    makelabelentry(tokens_tab, "Override Tensors:", override_tensors_var, row=59, padx=(120), singleline=True, width=150, tooltip="Override selected backend for specific tensors matching tensor_name_regex_pattern=buffer_type, same as in llama.cpp.")
+    makelabelentry(context_tab, "MoE Experts:", moeexperts_var, row=55, padx=(120), singleline=True, tooltip="Override number of MoE experts.")
+    makelabelentry(context_tab, "MoE CPU Layers:", moecpu_var, row=55, padx=(320), singleline=True, tooltip="Force Mixture of Experts (MoE) weights of the first N layers to the CPU.\nSetting it higher than GPU layers has no effect.", labelpadx=(210))
+    makelabelentry(context_tab, "Override KV:", override_kv_var, row=57, padx=(120), singleline=True, width=150, tooltip="Override metadata value by key. Separate multiple values with commas. Format is name=type:value. Types: int, float, bool, str")
+    makelabelentry(context_tab, "Override Tensors:", override_tensors_var, row=59, padx=(120), singleline=True, width=150, tooltip="Override selected backend for specific tensors matching tensor_name_regex_pattern=buffer_type, same as in llama.cpp.")
 
     # Model Tab
     model_tab = tabcontent["Loaded Files"]