model: align Laguna with upstream llama.cpp (#17335)

Update llama.cpp to pick up upstream Laguna implementation and remove Ollama's local Laguna implementation. Retain a narrow Metal-only scaling workaround for routed-MoE prompt overflow.

Translate older Ollama GGUF attention-gate and SWA metadata names so existing models continue to load.

LLAMA_CPP_VERSION

@@ -1 +1 @@
-b10069
+b10091

integration/thinking_test.go

@@ -11,8 +11,29 @@ import (
 	"github.com/ollama/ollama/api"
 )
 
-// runThinkingEnabled verifies that when thinking is requested, the model
+var rawThinkingProtocolTags = []string{
-// produces both thinking and content output without leaking raw channel tags.
+	"<|channel>",
+	"<channel|>",
+	"<think>",
+	"</think>",
+	"<assistant>",
+	"</assistant>",
+	"<tool_call>",
+	"</tool_call>",
+}
+
+func rejectRawThinkingProtocolTags(t *testing.T, field, value string) {
+	t.Helper()
+	for _, tag := range rawThinkingProtocolTags {
+		if strings.Contains(value, tag) {
+			t.Errorf("%s contains raw protocol tag %q: %s", field, tag, value)
+		}
+	}
+}
+
+// runThinkingEnabled verifies that thinking-capable models honor an explicit
+// thinking request, complete a reasoning trace, and return the final answer
+// without leaking raw channel tags.
 func runThinkingEnabled(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
 	defer cancel()
@@ -33,12 +54,15 @@ func runThinkingEnabled(t *testing.T) {
 				Stream: &stream,
 				Think:  &think,
 				Messages: []api.Message{
-					{Role: "user", Content: "What is 12 * 15? Think step by step."},
+					{Role: "user", Content: "What is 12 multiplied by 15? Give the final answer."},
 				},
 				Options: map[string]any{
 					"temperature": 0,
 					"seed":        42,
-					"num_predict": 512,
+					// Deep-thinking models can use several thousand tokens on
+					// simple problems before producing their final answer. Keep
+					// this high enough to verify a natural stop, not truncation.
+					"num_predict": 8192,
 				},
 			}
 
@@ -61,22 +85,17 @@ func runThinkingEnabled(t *testing.T) {
 			if thinking == "" {
 				t.Error("expected non-empty thinking output when thinking is enabled")
 			}
-
+			if content == "" {
-			// The answer (180) should appear in thinking, content, or both.
+				t.Error("expected non-empty final content after thinking")
-			// Some models put everything in thinking and leave content empty
+			} else if !strings.Contains(content, "180") {
-			// if they hit the token limit while still thinking.
+				t.Errorf("expected final answer 180, got content=%q", content)
-			combined := thinking + " " + content
+			}
-			if !strings.Contains(combined, "180") {
+			if response.DoneReason != "stop" {
-				t.Errorf("expected '180' in thinking or content, got thinking=%q content=%q", thinking, content)
+				t.Errorf("expected completed response, got done reason %q", response.DoneReason)
 			}
 
-			// Neither thinking nor content should contain raw channel tags
+			rejectRawThinkingProtocolTags(t, "content", content)
-			if strings.Contains(content, "<|channel>") || strings.Contains(content, "<channel|>") {
+			rejectRawThinkingProtocolTags(t, "thinking", thinking)
-				t.Errorf("content contains raw channel tags: %s", content)
-			}
-			if strings.Contains(thinking, "<|channel>") || strings.Contains(thinking, "<channel|>") {
-				t.Errorf("thinking contains raw channel tags: %s", thinking)
-			}
 
 			t.Logf("thinking (%d chars): %.100s...", len(thinking), thinking)
 			t.Logf("content (%d chars): %s", len(content), content)
@@ -84,7 +103,7 @@ func runThinkingEnabled(t *testing.T) {
 	}
 }
 
-// runThinkingSuppressed verifies that when thinking is NOT requested,
+// runThinkingSuppressed verifies that when thinking is explicitly disabled,
 // the model does not leak thinking/channel content into the response.
 func runThinkingSuppressed(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
@@ -100,10 +119,11 @@ func runThinkingSuppressed(t *testing.T) {
 			pullOrSkip(ctx, t, client, modelName)
 
 			stream := false
+			think := api.ThinkValue{Value: false}
 			req := api.ChatRequest{
 				Model:  modelName,
 				Stream: &stream,
-				// Think is nil — thinking not requested
+				Think:  &think,
 				Messages: []api.Message{
 					{Role: "user", Content: "What is the capital of Japan? Answer in one word."},
 				},
@@ -129,24 +149,16 @@ func runThinkingSuppressed(t *testing.T) {
 			content := response.Message.Content
 			thinking := response.Message.Thinking
 
-			// The answer should appear in content or thinking
+			// With thinking disabled, the answer must be returned as content.
-			combined := content + " " + thinking
+			if !strings.Contains(content, "Tokyo") {
-			if !strings.Contains(combined, "Tokyo") {
+				t.Errorf("expected 'Tokyo' in content, got content=%q thinking=%q", content, thinking)
-				t.Errorf("expected 'Tokyo' in content or thinking, got content=%q thinking=%q", content, thinking)
 			}
 
-			// Content must NOT contain channel/thinking tags
+			rejectRawThinkingProtocolTags(t, "content", content)
-			if strings.Contains(content, "<|channel>") || strings.Contains(content, "<channel|>") {
+			rejectRawThinkingProtocolTags(t, "thinking", thinking)
-				t.Errorf("content contains leaked channel tags when thinking not requested: %s", content)
-			}
-			if strings.Contains(content, "thought") && strings.Contains(content, "<channel|>") {
-				t.Errorf("content contains leaked thinking block: %s", content)
-			}
 
-			// Thinking field should ideally be empty when not requested.
-			// Some small models may still produce thinking output; log but don't fail.
 			if thinking != "" {
-				t.Logf("WARNING: model produced thinking output when not requested (%d chars): %.100s...", len(thinking), thinking)
+				t.Errorf("expected empty thinking when thinking is disabled, got %q", thinking)
 			}
 
 			t.Logf("content: %s", content)

llama/compat/README.md

@@ -88,6 +88,7 @@ This table tracks the dispatch surface. Keep it brief; the handler comments in
 | `deepseekocr` | Maps to `deepseek2-ocr`, injects missing OCR/MoE metadata, and hides embedded SAM/vision/projector tensors. | DeepSeek OCR projector translation. |
 | `glmocr` | Maps GLM OCR metadata/tensors to the llama.cpp-compatible view. | GLM OCR projector translation. |
 | `glm4moelite` | Maps GLM-4.7 Flash MLA metadata to the `deepseek2` path and fixes special-token metadata. | n/a |
+| `laguna` | Renames legacy attention-gate tensors and SWA RoPE metadata to current llama.cpp names. | n/a |
 | `nemotron_h_moe` | Fixes latent-FFN variants and hides MTP tensors. | n/a |
 | `nemotron_h_omni` | Selects the Nemotron text loader and hides audio/vision/projector tensors from the text loader. | Nemotron V2 VL projector translation; audio remains disabled. |
 | `llama` with Llama 3 markers | Fixes Llama 3 tokenizer metadata. | n/a |

llama/compat/llama-ollama-compat.cpp

@@ -118,6 +118,43 @@ void fix_glm4moelite_eog_token_ids(gguf_context * meta) {
     }
 }
 
+// =========================================================================
+// laguna (text side)
+// =========================================================================
+
+bool detect_ollama_laguna(const gguf_context * meta, const ggml_context * ctx) {
+    const int64_t arch_kid = gguf_find_key(meta, "general.architecture");
+    if (arch_kid < 0 || std::strcmp(gguf_get_val_str(meta, arch_kid), "laguna") != 0) return false;
+
+    return has_key(meta, "laguna.rope.swa.dimension_count")
+        || has_key(meta, "laguna.rope.swa.freq_base")
+        || ggml_get_tensor(const_cast<ggml_context *>(ctx), "blk.0.attn_g.weight") != nullptr;
+}
+
+void handle_laguna(gguf_context * meta, ggml_context * ctx) {
+    if (!detect_ollama_laguna(meta, ctx)) return;
+
+    OLLAMA_COMPAT_LOG_INFO("%s: detected Ollama-format laguna GGUF; applying compatibility fixes\n", __func__);
+
+    copy_u32_kv(meta, "laguna.rope.swa.dimension_count", "laguna.rope.dimension_count_swa");
+    copy_f32_kv(meta, "laguna.rope.swa.freq_base",       "laguna.rope.freq_base_swa");
+
+    std::vector<std::pair<std::string, std::string>> renames;
+    const int64_t n = gguf_get_n_tensors(meta);
+    constexpr const char * old_suffix = ".attn_g.weight";
+    constexpr const char * new_suffix = ".attn_gate.weight";
+    for (int64_t i = 0; i < n; ++i) {
+        const std::string name(gguf_get_tensor_name(meta, i));
+        const size_t pos = name.rfind(old_suffix);
+        if (pos != std::string::npos && pos + std::strlen(old_suffix) == name.size()) {
+            renames.emplace_back(name, name.substr(0, pos) + new_suffix);
+        }
+    }
+    for (const auto & [from, to] : renames) {
+        rename_tensor(meta, ctx, from.c_str(), to.c_str());
+    }
+}
+
 bool get_u32_kv(const gguf_context * meta, const char * key, uint32_t & out) {
     const int64_t kid = gguf_find_key(meta, key);
     if (kid < 0) return false;
@@ -3221,6 +3258,7 @@ bool translate_metadata(const llama_model_loader * ml,
     if (arch_name == "qwen35moe") handle_qwen35moe(ml, meta, ctx);
     if (arch_name == "qwen35")    handle_qwen35   (ml, meta, ctx);
     if (arch_name == "qwen3next") handle_qwen3next(meta, ctx);
+    if (arch_name == "laguna")   handle_laguna   (meta, ctx);
     if (arch_name == "gptoss")        handle_gptoss        (ml, meta, ctx, arch_name);
     if (arch_name == "lfm2")          handle_lfm2          (ml, meta, ctx);
     if (arch_name == "olmo3")         handle_olmo3         (meta, arch_name);

llama/compat/models/003-llama-cpp-laguna-metal.patch

@@ -0,0 +1,42 @@
+diff --git a/src/models/laguna.cpp b/src/models/laguna.cpp
+index 1d705440e..7e708b144 100644
+--- a/src/models/laguna.cpp
++++ b/src/models/laguna.cpp
+@@ -275,16 +275,35 @@ llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_para
+         if ((uint32_t)il >= hparams.n_layer_dense_lead) {
+             // MoE: sigmoid routing + score-correction bias + sum-norm +
+             // routed_scaling_factor (all handled by build_moe_ffn).
++            ggml_tensor * up_scale = nullptr;
++            float expert_weights_scale = hparams.expert_weights_scale;
++
++#if defined(GGML_USE_METAL)
++            const ggml_type down_type = model.layers[il].ffn_down_exps->type;
++            if (n_tokens >= 32 && (ggml_is_quantized(down_type) || down_type == GGML_TYPE_F16)) {
++                // At 32 prompt tokens, Metal switches MUL_MAT_ID from its
++                // range-safe matrix-vector kernel to FP16 matrix tiles.
++                // Laguna's routed SwiGLU activations can overflow those tiles.
++                // Generation uses one token and does not need this workaround.
++                constexpr float down_input_scale = 1.0f / 256.0f;
++                up_scale = ggml_fill(ctx0,
++                        model.layers[il].ffn_exp_probs_b, down_input_scale);
++                expert_weights_scale /= down_input_scale;
++            }
++#endif
++
+             ggml_tensor * moe_out = build_moe_ffn(cur,
+                     model.layers[il].ffn_gate_inp,
+                     model.layers[il].ffn_up_exps,
+                     model.layers[il].ffn_gate_exps,
+                     model.layers[il].ffn_down_exps,
+                     model.layers[il].ffn_exp_probs_b,
+                     n_expert, n_expert_used,
+                     LLM_FFN_SILU,
+                     hparams.expert_weights_norm,
+-                    hparams.expert_weights_scale,
++                    expert_weights_scale,
+                     (llama_expert_gating_func_type) hparams.expert_gating_func,
+-                    il);
++                    il,
++                    nullptr, nullptr,
++                    up_scale);
+             cb(moe_out, "ffn_moe_out", il);

llama/compat/models/003-llama-cpp-laguna.patch

@@ -1,93 +0,0 @@
-diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
---- a/src/llama-arch.cpp
-+++ b/src/llama-arch.cpp
-@@ -136,2 +136,3 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
-     { LLM_ARCH_MELLUM,           "mellum"           },
-+    { LLM_ARCH_LAGUNA,           "laguna"           },
-     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
-@@ -398,2 +399,3 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
-     { LLM_TENSOR_ATTN_GATE,                              "blk.%d.attn_gate" },
-+    { LLM_TENSOR_ATTN_GATE_LAGUNA,                       "blk.%d.attn_g" },
-     { LLM_TENSOR_FFN_POST_NORM,                          "blk.%d.post_ffw_norm" },
-@@ -596,2 +598,3 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
-     {LLM_TENSOR_ATTN_GATE,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-+    {LLM_TENSOR_ATTN_GATE_LAGUNA,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-     {LLM_TENSOR_FFN_GATE,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-diff --git a/src/llama-arch.h b/src/llama-arch.h
---- a/src/llama-arch.h
-+++ b/src/llama-arch.h
-@@ -145,2 +145,3 @@ enum llm_arch {
-     LLM_ARCH_DFLASH,
-+    LLM_ARCH_LAGUNA,
-     LLM_ARCH_UNKNOWN,
-@@ -382,2 +383,3 @@ enum llm_tensor {
-     LLM_TENSOR_ATTN_GATE,
-+    LLM_TENSOR_ATTN_GATE_LAGUNA,
-     LLM_TENSOR_FFN_GATE_INP,
-diff --git a/src/llama-model.cpp b/src/llama-model.cpp
---- a/src/llama-model.cpp
-+++ b/src/llama-model.cpp
-@@ -48,4 +48,6 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
-         case LLM_ARCH_TALKIE:
-             return new llama_model_talkie(params);
-+        case LLM_ARCH_LAGUNA:
-+            return new llama_model_laguna(params);
-         case LLM_ARCH_DECI:
-             return new llama_model_deci(params);
-@@ -2525,2 +2527,3 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
-         case LLM_ARCH_MELLUM:
-+        case LLM_ARCH_LAGUNA:
-         case LLM_ARCH_DFLASH:
-diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
---- a/src/llama-model-loader.cpp
-+++ b/src/llama-model-loader.cpp
-@@ -505,2 +505,3 @@ namespace GGUFMeta {
-     template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
-+    template bool llama_model_loader::get_key_or_arr<uint32_t, 512>(const std::string & key, std::array<uint32_t, 512> & result, uint32_t n, bool required);
-     template bool llama_model_loader::get_key_or_arr<std::array<float,    512>>(enum llm_kv kid, std::array<float,    512> & result, uint32_t n, bool required);
-diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
---- a/src/llama-vocab.cpp
-+++ b/src/llama-vocab.cpp
-@@ -359,2 +359,8 @@ struct llm_tokenizer_bpe : llm_tokenizer {
-                 break;
-+            case LLAMA_VOCAB_PRE_TYPE_LAGUNA:
-+                regex_exprs = {
-+                    "(?:\\r?\\n)+(?!\\r?\\n)",
-+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-+                };
-+                break;
-             case LLAMA_VOCAB_PRE_TYPE_GPT2:
-@@ -2100,2 +2106,4 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
-                 ignore_merges = true;
-+            } else if (tokenizer_pre == "laguna") {
-+                pre_type = LLAMA_VOCAB_PRE_TYPE_LAGUNA;
-             } else if (
-@@ -2773,2 +2781,3 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
-                     || t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr
-+                    || t.first == "</assistant>"     // poolside Laguna (eos_token_ids)
-                ) {
-diff --git a/src/llama-vocab.h b/src/llama-vocab.h
---- a/src/llama-vocab.h
-+++ b/src/llama-vocab.h
-@@ -65,2 +65,3 @@ enum llama_vocab_pre_type {
-     LLAMA_VOCAB_PRE_TYPE_MELLUM2           = 55,
-+    LLAMA_VOCAB_PRE_TYPE_LAGUNA            = 56,
- };
-diff --git a/src/models/models.h b/src/models/models.h
---- a/src/models/models.h
-+++ b/src/models/models.h
-@@ -1657,1 +1657,14 @@ struct llama_model_arcee : public llama_model_base {
-+struct llama_model_laguna : public llama_model_base {
-+    llama_model_laguna(const struct llama_model_params & params) : llama_model_base(params) {}
-+    void load_arch_hparams(llama_model_loader & ml) override;
-+    void load_arch_tensors(llama_model_loader & ml) override;
-+
-+    struct graph : public llm_graph_context {
-+        graph(const llama_model & model, const llm_graph_params & params);
-+    };
-+
-+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
-+};
-+
-+
- struct llama_model_arcee : public llama_model_base {

llama/compat/models/laguna.cpp

@@ -1,253 +0,0 @@
-#include "models/models.h"
-
-void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
-    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-
-    // MoE
-    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);
-    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,        false);
-    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared,   false);
-    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
-    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm,  false);
-    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func,   false);
-
-    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
-    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-    ml.get_key_or_arr("laguna.attention.layer_types", hparams.is_swa_impl, hparams.n_layer(), false);
-
-    ml.get_key("laguna.rope.swa.dimension_count", hparams.n_rot_swa,                false);
-    ml.get_key("laguna.rope.swa.freq_base",       hparams.rope_freq_base_train_swa, false);
-    ml.get_key("laguna.rope.scaling.beta_fast",   hparams.yarn_beta_fast,           false);
-    ml.get_key("laguna.rope.scaling.beta_slow",   hparams.yarn_beta_slow,           false);
-
-    type = LLM_TYPE_UNKNOWN;
-}
-
-void llama_model_laguna::load_arch_tensors(llama_model_loader &) {
-    LLAMA_LOAD_LOCALS;
-
-    const int64_t n_ff_exp        = hparams.n_ff_exp;
-    const int64_t n_ff_shexp      = hparams.n_ff_shexp;
-
-    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-
-    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
-    if (output == NULL) {
-        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
-    }
-
-    for (int i = 0; i < n_layer; ++i) {
-        auto & layer = layers[i];
-
-        const int64_t n_head_i    = hparams.n_head(i);
-        const int64_t n_head_kv_i = hparams.n_head_kv(i);
-        const int64_t n_embd_q    = n_embd_head_k * n_head_i;
-        const int64_t n_embd_kv   = n_embd_head_k * n_head_kv_i;
-
-        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
-
-        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_q},  0);
-        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_kv}, 0);
-        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_kv}, 0);
-        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q, n_embd},  0);
-
-        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE_LAGUNA, "weight", i), {n_embd, n_head_i}, 0);
-
-        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
-
-        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
-
-        if (i < (int) hparams.n_layer_dense_lead) {
-            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
-            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-        } else {
-            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, 0);
-            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, TENSOR_NOT_REQUIRED);
-
-            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
-
-            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
-            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
-            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
-        }
-    }
-}
-
-std::unique_ptr<llm_graph_context> llama_model_laguna::build_arch_graph(const llm_graph_params & params) const {
-    return std::make_unique<graph>(*this, params);
-}
-
-llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_params & params) :
-        llm_graph_context(params) {
-    const int64_t n_embd_head = hparams.n_embd_head_v();
-    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
-
-    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
-
-    ggml_tensor * cur;
-    ggml_tensor * inpL;
-
-    inpL = build_inp_embd(model.tok_embd);
-
-    ggml_tensor * inp_pos = build_inp_pos();
-
-    auto * inp_attn = build_attn_inp_kv_iswa();
-
-    ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-    for (int il = 0; il < n_layer; ++il) {
-        ggml_tensor * inpSA = inpL;
-
-        const int64_t n_head_il    = hparams.n_head(il);
-        const int64_t n_head_kv_il = hparams.n_head_kv(il);
-        const bool    is_swa       = hparams.is_swa(il);
-
-        const int   rope_n_dims = hparams.n_rot(il);
-        const float rope_base   = is_swa ? hparams.rope_freq_base_train_swa  : hparams.rope_freq_base_train;
-        const float rope_scale  = is_swa ? hparams.rope_freq_scale_train_swa : hparams.rope_freq_scale_train;
-        const float rope_ext    = is_swa ? 0.0f : 1.0f;
-        const float rope_bfast  = hparams.yarn_beta_fast;
-        const float rope_bslow  = hparams.yarn_beta_slow;
-
-        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-        cb(cur, "attn_norm", il);
-
-        // self-attention
-        {
-            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
-            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
-            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-            cb(Vcur, "Vcur", il);
-
-            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur);
-            cb(gate, "gate", il);
-
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head_il,    n_tokens);
-            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv_il, n_tokens);
-            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv_il, n_tokens);
-
-            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-            cb(Qcur, "Qcur_normed", il);
-            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-            cb(Kcur, "Kcur_normed", il);
-
-            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
-                    rope_n_dims, rope_type, hparams.n_ctx_orig_yarn, rope_base, rope_scale,
-                    rope_ext, hparams.rope_attn_factor, rope_bfast, rope_bslow);
-            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
-                    rope_n_dims, rope_type, hparams.n_ctx_orig_yarn, rope_base, rope_scale,
-                    rope_ext, hparams.rope_attn_factor, rope_bfast, rope_bslow);
-
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
-
-            cur = build_attn(inp_attn,
-                    nullptr, nullptr, nullptr,
-                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-            cb(cur, "attn_pregate", il);
-
-            gate = ggml_softplus(ctx0, gate);
-            cur  = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_il, n_tokens);
-            gate = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens);
-            cur  = ggml_mul(ctx0, cur, gate);
-            cur  = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens);
-            cb(cur, "attn_gated", il);
-
-            cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
-            cb(cur, "attn_out", il);
-        }
-
-        if (il == n_layer - 1 && inp_out_ids) {
-            cur   = ggml_get_rows(ctx0, cur,   inp_out_ids);
-            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-        }
-
-        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
-
-        // feed-forward
-        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-        cb(cur, "ffn_norm", il);
-
-        if ((uint32_t) il < hparams.n_layer_dense_lead) {
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-        } else {
-            ggml_tensor * up_scale = nullptr;
-            float expert_weights_scale = hparams.expert_weights_scale;
-
-#if defined(GGML_USE_METAL)
-            if (n_tokens >= 32 && ggml_is_quantized(model.layers[il].ffn_down_exps->type)) {
-                // ggml-metal switches MUL_MAT_ID from its range-safe
-                // matrix-vector kernel to FP16 matrix tiles at 32 tokens
-                // (ne21_mm_id_min in ggml_metal_op_mul_mat_id). Laguna's routed
-                // SwiGLU activations can overflow those tiles. Scale the linear
-                // up branch and fold the inverse power-of-two factor into the
-                // existing routing-weight scale. Revisit this guard if the
-                // Metal dispatch threshold changes.
-                constexpr float down_input_scale = 1.0f / 256.0f;
-                up_scale = ggml_fill(ctx0,
-                        model.layers[il].ffn_exp_probs_b, down_input_scale);
-                expert_weights_scale /= down_input_scale;
-            }
-#endif
-
-            ggml_tensor * moe_out = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    model.layers[il].ffn_exp_probs_b,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, hparams.expert_weights_norm,
-                    expert_weights_scale,
-                    (llama_expert_gating_func_type) hparams.expert_gating_func,
-                    il,
-                    nullptr, nullptr,
-                    up_scale);
-            cb(moe_out, "ffn_moe_out", il);
-
-            ggml_tensor * ffn_shexp = build_ffn(cur,
-                    model.layers[il].ffn_up_shexp,   NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(ffn_shexp, "ffn_shexp", il);
-
-            cur = ggml_add(ctx0, moe_out, ffn_shexp);
-            cb(cur, "ffn_out", il);
-        }
-
-        cur = ggml_add(ctx0, cur, ffn_inp);
-
-        cur = build_cvec(cur, il);
-        cb(cur, "l_out", il);
-
-        inpL = cur;
-    }
-
-    cur = inpL;
-
-    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-    cb(cur, "result_norm", -1);
-    res->t_embd = cur;
-
-    cur = build_lora_mm(model.output, cur, model.output_s);
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
-
-    ggml_build_forward_expand(gf, cur);
-}

model/parsers/laguna_test.go

@@ -535,12 +535,12 @@ func TestLagunaParserNonAssistantLastMessageStillPrimesThinking(t *testing.T) {
 func TestLagunaV8ParserAssistantHistoryStillPrimesThinking(t *testing.T) {
 	// Laguna v8 closes assistant history and emits a fresh generation prompt,
 	// so an assistant tail message must not switch the parser into prefill mode.
-	parser := ParserForName("laguna-v8")
+	parser := ParserForName("poolside-v1")
 	if parser == nil {
-		t.Fatal("expected laguna-v8 parser")
+		t.Fatal("expected poolside-v1 parser")
 	}
 	if !parser.HasToolSupport() || !parser.HasThinkingSupport() {
-		t.Fatal("laguna-v8 parser should advertise tools and thinking")
+		t.Fatal("poolside-v1 parser should advertise tools and thinking")
 	}
 
 	parser.Init(nil, &api.Message{Role: "assistant", Content: "Previous."}, &api.ThinkValue{Value: true})

model/parsers/parsers.go

@@ -94,7 +94,7 @@ func ParserForName(name string) Parser {
 		return &LFM2Parser{hasThinkingSupport: true}
 	case "laguna":
 		return &LagunaParser{}
-	case "laguna-v8":
+	case "poolside-v1":
 		return &LagunaV8Parser{}
 	case "cohere":
 		return &CohereParser{}

model/renderers/renderer.go

@@ -109,7 +109,7 @@ func rendererForName(name string) Renderer {
 		return &LFM2Renderer{IsThinking: true, useImgTags: RenderImgTags}
 	case "laguna":
 		return &LagunaRenderer{}
-	case "laguna-v8":
+	case "poolside-v1":
 		return &LagunaV8Renderer{}
 	case "cohere":
 		return &CohereRenderer{}

model/renderers/renderer_test.go

@@ -69,7 +69,7 @@ func TestLeadingBOSForRenderer(t *testing.T) {
 		{name: "lfm2", want: "<|startoftext|>"},
 		{name: "lfm2-thinking", want: "<|startoftext|>"},
 		{name: "laguna", want: "〈|EOS|〉"},
-		{name: "laguna-v8", want: "〈|EOS|〉"},
+		{name: "poolside-v1", want: "〈|EOS|〉"},
 		{name: "deepseek3.1", want: "<｜begin▁of▁sentence｜>"},
 		{name: "cogito", want: "<｜begin▁of▁sentence｜>"},
 		{name: "qwen3-coder", want: ""},