diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index adf67cecc..1e344db6b 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -799,6 +799,7 @@ jobs:
           7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
           $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)
           cd build
+          $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1
           & $sde -future -- ctest -L main -C Release --verbose --timeout 900
 
       - name: Determine tag name
diff --git a/CMakeLists.txt b/CMakeLists.txt
index dba083089..e3a0cc369 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -79,8 +79,15 @@ set(GGML_SANITIZE_ADDRESS   ${LLAMA_SANITIZE_ADDRESS})
 set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED})
 set(GGML_ALL_WARNINGS       ${LLAMA_ALL_WARNINGS})
 set(GGML_FATAL_WARNINGS     ${LLAMA_FATAL_WARNINGS})
-set(GGML_LLAMAFILE          ON)
-set(GGML_CUDA_USE_GRAPHS    ON)
+
+# change the default for these ggml options
+if (NOT DEFINED GGML_LLAMAFILE)
+    set(GGML_LLAMAFILE ON)
+endif()
+
+if (NOT DEFINED GGML_CUDA_USE_GRAPHS)
+    set(GGML_CUDA_USE_GRAPHS ON)
+endif()
 
 # transition helpers
 function (llama_option_depr TYPE OLD NEW)
diff --git a/Makefile b/Makefile
index bbfe0f12b..8ae4f1dc4 100644
--- a/Makefile
+++ b/Makefile
@@ -45,6 +45,7 @@ BUILD_TARGETS = \
 TEST_TARGETS = \
 	tests/test-autorelease \
 	tests/test-backend-ops \
+	tests/test-chat-template \
 	tests/test-double-float \
 	tests/test-grad0 \
 	tests/test-grammar-integration \
@@ -1070,6 +1071,7 @@ clean:
 	rm -rvf src/*.o
 	rm -rvf tests/*.o
 	rm -rvf examples/*.o
+	rm -rvf common/*.o
 	rm -rvf *.a
 	rm -rvf *.dll
 	rm -rvf *.so
diff --git a/common/common.cpp b/common/common.cpp
index 57d03a578..6a00d25be 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1026,6 +1026,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.input_suffix = argv[i];
         return true;
     }
+    if (arg == "--spm-infill") {
+        params.spm_infill = true;
+        return true;
+    }
     if (arg == "--grammar") {
         CHECK_ARG
         sparams.grammar = argv[i];
@@ -1409,6 +1413,8 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "main infill", "       --in-prefix-bos",        "prefix BOS to user inputs, preceding the `--in-prefix` string" });
     options.push_back({ "main infill", "       --in-prefix STRING",     "string to prefix user inputs with (default: empty)" });
     options.push_back({ "main infill", "       --in-suffix STRING",     "string to suffix after user inputs with (default: empty)" });
+    options.push_back({ "server infill",
+                                       "       --spm-infill",           "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });
 
     options.push_back({ "sampling" });
     options.push_back({ "*",           "       --samplers SAMPLERS",    "samplers that will be used for generation in the order, separated by \';\'\n"
diff --git a/common/common.h b/common/common.h
index 0486ba380..d6cb814b9 100644
--- a/common/common.h
+++ b/common/common.h
@@ -250,6 +250,8 @@ struct gpt_params {
     std::string cvector_outfile       = "control_vector.gguf";
     std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
     std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
+
+    bool spm_infill = false; // suffix/prefix/middle pattern for infill
 };
 
 void gpt_params_handle_model_default(gpt_params & params);
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index 823db93a6..d149bf908 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -321,7 +321,7 @@ std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
 };
 
 std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
-std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
+std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
 
 template <typename Iterator>
 std::string join(Iterator begin, Iterator end, const std::string & separator) {
@@ -714,7 +714,7 @@ private:
             }
             prop_names.push_back(prop_name);
         }
-        if (!(additional_properties.is_boolean() && !additional_properties.get<bool>())) {
+        if ((additional_properties.is_boolean() && additional_properties.get<bool>()) || additional_properties.is_object()) {
             std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
             std::string value_rule =
                 additional_properties.is_object() ? visit(additional_properties, sub_name + "-value")
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 5bf69ef9f..5bcc849db 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -2340,6 +2340,46 @@ class GemmaModel(Model):
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("Gemma2ForCausalLM")
+class Gemma2Model(Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA2
+
+    def set_vocab(self):
+        self._set_vocab_llama_hf()
+        self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unusem
+
+        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
+        # To prevent errors, skip loading lm_head.weight.
+        if name == "lm_head.weight":
+            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+            return []
+
+        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @Model.register("Starcoder2ForCausalLM")
 class StarCoder2Model(Model):
     model_arch = gguf.MODEL_ARCH.STARCODER2
diff --git a/examples/infill/README.md b/examples/infill/README.md
index 74f42d2fc..810a0c5e7 100644
--- a/examples/infill/README.md
+++ b/examples/infill/README.md
@@ -15,6 +15,7 @@ In this section, we cover the most commonly used options for running the `infill
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
 -   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
 -   `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `--spm-infill`: Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this.
 
 ## Input Prompts
 
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 3e82e4a81..ca71dd687 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -210,6 +210,7 @@ int main(int argc, char ** argv) {
         suff_rm_leading_spc = false;
     }
     std::vector<llama_token> embd_inp;
+    std::vector<llama_token> embd_end;
     std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
     std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
     const int space_token = 29871;
@@ -217,12 +218,13 @@ int main(int argc, char ** argv) {
         inp_sfx.erase(inp_sfx.begin());
     }
     inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
-    if (add_bos) {
-        inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
-    }
     inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
-    embd_inp = inp_pfx;
-    embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
+    embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
+    embd_end = params.spm_infill ? inp_pfx : inp_sfx;
+    if (add_bos) {
+        embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+    }
+    embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
 
     const llama_token middle_token = llama_token_middle(model);
     if (middle_token >= 0) {
@@ -526,14 +528,14 @@ int main(int argc, char ** argv) {
                     inp_sfx.erase(inp_sfx.begin());
                 }
                 inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
-                if (add_bos) {
-                    inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
-                }
                 inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
-                embd_inp = inp_pfx;
-                embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
+                embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
+                embd_end = params.spm_infill ? inp_pfx : inp_sfx;
+                if (add_bos) {
+                    embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+                }
+                embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
 
-                const llama_token middle_token = llama_token_middle(model);
                 if (middle_token >= 0) {
                     embd_inp.push_back(middle_token);
                 }
diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py
index 02c91d650..5b97ba50f 100755
--- a/examples/json_schema_to_grammar.py
+++ b/examples/json_schema_to_grammar.py
@@ -231,7 +231,7 @@ GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]')
 GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'}
 
 NON_LITERAL_SET = set('|.()[]{}*+?')
-ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?')
+ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('^$.[]()|{}*+?')
 
 
 class SchemaConverter:
@@ -686,7 +686,7 @@ class SchemaConverter:
         required_props = [k for k in sorted_props if k in required]
         optional_props = [k for k in sorted_props if k not in required]
 
-        if additional_properties != False:
+        if additional_properties is not None and additional_properties != False:
             sub_name = f'{name}{"-" if name else ""}additional'
             value_rule = self.visit(additional_properties, f'{sub_name}-value') if isinstance(additional_properties, dict) else \
                 self._add_primitive('value', PRIMITIVE_RULES['value'])
diff --git a/examples/server/README.md b/examples/server/README.md
index e7fb0bf64..4fab006bb 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -73,6 +73,7 @@ The project is under active development, and we are [looking for feedback and co
 - `-fa`, `--flash-attn` : enable flash attention (default: disabled).
 - `-ctk TYPE`, `--cache-type-k TYPE` : KV cache data type for K (default: `f16`, options `f32`, `f16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, or `q5_1`)
 - `-ctv TYPE`, `--cache-type-v TYPE` : KV cache type for V (default `f16`, see `-ctk` for options)
+- `--spm-infill` : Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this.
 
 **If compiled with `LLAMA_SERVER_SSL=ON`**
 - `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs
index 8baf23c2c..b796f8dcd 100644
--- a/examples/server/public/json-schema-to-grammar.mjs
+++ b/examples/server/public/json-schema-to-grammar.mjs
@@ -259,7 +259,7 @@ const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g;
 const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' };
 
 const NON_LITERAL_SET = new Set('|.()[]{}*+?');
-const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('[]()|{}*+?');
+const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('^$.[]()|{}*+?');
 
 export class SchemaConverter {
   constructor(options) {
@@ -752,7 +752,7 @@ export class SchemaConverter {
     const requiredProps = sortedProps.filter(k => required.has(k));
     const optionalProps = sortedProps.filter(k => !required.has(k));
 
-    if (additionalProperties !== false) {
+    if (additionalProperties) {
       const subName = `${name ?? ''}${name ? '-' : ''}additional`;
       const valueRule =
         additionalProperties != null && typeof additionalProperties === 'object' ? this.visit(additionalProperties, `${subName}-value`)
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index ae768097b..d7fb61812 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -2020,6 +2020,7 @@ struct server_context {
                         slot.t_start_generation = 0;
 
                         if (slot.infill) {
+                            const bool add_bos = llama_should_add_bos_token(model);
                             bool suff_rm_leading_spc = true;
                             if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
                                 params.input_suffix.erase(0, 1);
@@ -2035,16 +2036,21 @@ struct server_context {
                             }
 
                             prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
-                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
-                            prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
-                            prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
+                            suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model));
+
+                            auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
+                            auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
+                            if (add_bos) {
+                                embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+                            }
+                            embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
 
                             const llama_token middle_token = llama_token_middle(model);
                             if (middle_token >= 0) {
-                                prefix_tokens.push_back(middle_token);
+                                embd_inp.push_back(middle_token);
                             }
 
-                            prompt_tokens = prefix_tokens;
+                            prompt_tokens = embd_inp;
                         } else {
                             prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
                         }
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 222a2d137..cf3d09e70 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -150,6 +150,7 @@ class MODEL_ARCH(IntEnum):
     INTERNLM2    = auto()
     MINICPM      = auto()
     GEMMA        = auto()
+    GEMMA2       = auto()
     STARCODER2   = auto()
     MAMBA        = auto()
     XVERSE       = auto()
@@ -180,10 +181,13 @@ class MODEL_TENSOR(IntEnum):
     ATTN_NORM            = auto()
     ATTN_NORM_2          = auto()
     ATTN_OUT_NORM        = auto()
+    ATTN_POST_NORM       = auto()
     ATTN_ROT_EMBD        = auto()
     FFN_GATE_INP         = auto()
     FFN_GATE_INP_SHEXP   = auto()
     FFN_NORM             = auto()
+    FFN_PRE_NORM         = auto()
+    FFN_POST_NORM        = auto()
     FFN_GATE             = auto()
     FFN_DOWN             = auto()
     FFN_UP               = auto()
@@ -270,6 +274,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.INTERNLM2:      "internlm2",
     MODEL_ARCH.MINICPM:        "minicpm",
     MODEL_ARCH.GEMMA:          "gemma",
+    MODEL_ARCH.GEMMA2:         "gemma2",
     MODEL_ARCH.STARCODER2:     "starcoder2",
     MODEL_ARCH.MAMBA:          "mamba",
     MODEL_ARCH.XVERSE:         "xverse",
@@ -303,9 +308,12 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_Q_NORM:          "blk.{bid}.attn_q_norm",
     MODEL_TENSOR.ATTN_K_NORM:          "blk.{bid}.attn_k_norm",
     MODEL_TENSOR.ATTN_OUT_NORM:        "blk.{bid}.attn_output_norm",
+    MODEL_TENSOR.ATTN_POST_NORM:       "blk.{bid}.post_attention_norm",
     MODEL_TENSOR.FFN_GATE_INP:         "blk.{bid}.ffn_gate_inp",
     MODEL_TENSOR.FFN_GATE_INP_SHEXP:   "blk.{bid}.ffn_gate_inp_shexp",
     MODEL_TENSOR.FFN_NORM:             "blk.{bid}.ffn_norm",
+    MODEL_TENSOR.FFN_PRE_NORM:         "blk.{bid}.ffn_norm",
+    MODEL_TENSOR.FFN_POST_NORM:        "blk.{bid}.post_ffw_norm",
     MODEL_TENSOR.FFN_GATE:             "blk.{bid}.ffn_gate",
     MODEL_TENSOR.FFN_DOWN:             "blk.{bid}.ffn_down",
     MODEL_TENSOR.FFN_UP:               "blk.{bid}.ffn_up",
@@ -751,6 +759,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FFN_NORM,
     ],
+    MODEL_ARCH.GEMMA2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_PRE_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
     MODEL_ARCH.STARCODER2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 7b047f241..0bed43939 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -187,6 +187,10 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
         ),
 
+        MODEL_TENSOR.ATTN_POST_NORM: (
+            "model.layers.{bid}.post_attention_layernorm",     # gemma2
+        ),
+
         # Rotary embeddings
         MODEL_TENSOR.ATTN_ROT_EMBD: (
             "model.layers.{bid}.self_attn.rotary_emb.inv_freq",        # llama-hf
@@ -210,6 +214,16 @@ class TensorNameMap:
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
         ),
 
+        # Post feed-forward norm
+        MODEL_TENSOR.FFN_PRE_NORM: (
+            "model.layers.{bid}.pre_feedforward_layernorm", # gemma2
+        ),
+
+        # Post feed-forward norm
+        MODEL_TENSOR.FFN_POST_NORM: (
+            "model.layers.{bid}.post_feedforward_layernorm", # gemma2
+        ),
+
         MODEL_TENSOR.FFN_GATE_INP: (
             "layers.{bid}.feed_forward.gate",             # mixtral
             "model.layers.{bid}.block_sparse_moe.gate",   # mixtral
diff --git a/grammars/README.md b/grammars/README.md
index 6920ca0bc..e91fe3e82 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -182,6 +182,8 @@ space ::= | " " | "\n" [ \t]{0,20}
 
 Here is also a list of known limitations (contributions welcome):
 
+- `additionalProperties` defaults to `false` (produces faster grammars + reduces hallucinations).
+- `"additionalProperties": true` may produce keys that contain unescaped newlines.
 - Unsupported features are skipped silently. It is currently advised to use the command-line Python converter (see above) to see any warnings, and to inspect the resulting grammar / test it w/ [llama-gbnf-validator](../examples/gbnf-validator/gbnf-validator.cpp).
 - Can't mix `properties` w/ `oneOf` in the same type (https://github.com/ggerganov/llama.cpp/issues/7703)
 - [prefixItems](https://json-schema.org/draft/2020-12/json-schema-core#name-prefixitems) is broken (but [items](https://json-schema.org/draft/2020-12/json-schema-core#name-items) works)
@@ -202,10 +204,11 @@ And a non-exhaustive list of other unsupported features that are unlikely to be
 ### A word about additionalProperties
 
 > [!WARNING]
-> By default, `object`s accept [additional properties](https://json-schema.org/understanding-json-schema/reference/object#additionalproperties), which you might not want / not expect, and which will make sampling slower (not just because of the extra tokens, but also generates a slower grammar).
-> You can set `"additionalProperties": false` on the schema of any object to ensure only properties listed in `properties` are generated (not needed for non-`object` types, e.g. `array` or `string`).
+> The JSON schemas spec states `object`s accept [additional properties](https://json-schema.org/understanding-json-schema/reference/object#additionalproperties) by default.
+> Since this is slow and seems prone to hallucinations, we default to no additional properties.
+> You can set `"additionalProperties": true` in the the schema of any object to explicitly allow additional properties.
 
-If you're using [Pydantic](https://pydantic.dev/) to generate schemas, you can disable additional properties with the `extra` config on each model class:
+If you're using [Pydantic](https://pydantic.dev/) to generate schemas, you can enable additional properties with the `extra` config on each model class:
 
 ```python
 # pip install pydantic
@@ -214,14 +217,14 @@ from typing import Annotated, List
 from pydantic import BaseModel, Extra, Field
 class QAPair(BaseModel):
     class Config:
-        extra = 'forbid'  # triggers additionalProperties: false in the JSON schema
+        extra = 'allow'  # triggers additionalProperties: true in the JSON schema
     question: str
     concise_answer: str
     justification: str
 
 class Summary(BaseModel):
     class Config:
-        extra = 'forbid'
+        extra = 'allow'
     key_facts: List[Annotated[str, Field(pattern='- .{5,}')]]
     question_answers: List[Annotated[List[QAPair], Field(min_items=5)]]
 
@@ -235,7 +238,7 @@ print(json.dumps(Summary.model_json_schema(), indent=2))
 {
   "$defs": {
     "QAPair": {
-      "additionalProperties": false,
+      "additionalProperties": true,
       "properties": {
         "question": {
           "title": "Question",
@@ -259,7 +262,7 @@ print(json.dumps(Summary.model_json_schema(), indent=2))
       "type": "object"
     }
   },
-  "additionalProperties": false,
+  "additionalProperties": true,
   "properties": {
     "key_facts": {
       "items": {
@@ -291,30 +294,40 @@ print(json.dumps(Summary.model_json_schema(), indent=2))
 ```
 
 ```
-QAPair ::= "{" space QAPair-question-kv "," space QAPair-concise-answer-kv "," space QAPair-justification-kv "}" space
+QAPair ::= "{" space QAPair-question-kv "," space QAPair-concise-answer-kv "," space QAPair-justification-kv ( "," space ( QAPair-additional-kv ( "," space QAPair-additional-kv )* ) )? "}" space
+QAPair-additional-k ::= ["] ( [c] ([o] ([n] ([c] ([i] ([s] ([e] ([_] ([a] ([n] ([s] ([w] ([e] ([r] char+ | [^"r] char*) | [^"e] char*) | [^"w] char*) | [^"s] char*) | [^"n] char*) | [^"a] char*) | [^"_] char*) | [^"e] char*) | [^"s] char*) | [^"i] char*) | [^"c] char*) | [^"n] char*) | [^"o] char*) | [j] ([u] ([s] ([t] ([i] ([f] ([i] ([c] ([a] ([t] ([i] ([o] ([n] char+ | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"a] char*) | [^"c] char*) | [^"i] char*) | [^"f] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"u] char*) | [q] ([u] ([e] ([s] ([t] ([i] ([o] ([n] char+ | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"e] char*) | [^"u] char*) | [^"cjq] char* )? ["] space
+QAPair-additional-kv ::= QAPair-additional-k ":" space value
 QAPair-concise-answer-kv ::= "\"concise_answer\"" space ":" space string
 QAPair-justification-kv ::= "\"justification\"" space ":" space string
 QAPair-question-kv ::= "\"question\"" space ":" space string
+additional-k ::= ["] ( [k] ([e] ([y] ([_] ([f] ([a] ([c] ([t] ([s] char+ | [^"s] char*) | [^"t] char*) | [^"c] char*) | [^"a] char*) | [^"f] char*) | [^"_] char*) | [^"y] char*) | [^"e] char*) | [q] ([u] ([e] ([s] ([t] ([i] ([o] ([n] ([_] ([a] ([n] ([s] ([w] ([e] ([r] ([s] char+ | [^"s] char*) | [^"r] char*) | [^"e] char*) | [^"w] char*) | [^"s] char*) | [^"n] char*) | [^"a] char*) | [^"_] char*) | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"e] char*) | [^"u] char*) | [^"kq] char* )? ["] space
+additional-kv ::= additional-k ":" space value
+array ::= "[" space ( value ("," space value)* )? "]" space
+boolean ::= ("true" | "false") space
 char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+decimal-part ::= [0-9]{1,16}
 dot ::= [^\x0A\x0D]
+integral-part ::= [0] | [1-9] [0-9]{0,15}
 key-facts ::= "[" space (key-facts-item ("," space key-facts-item)*)? "]" space
 key-facts-item ::= "\"" "- " key-facts-item-1{5,} "\"" space
 key-facts-item-1 ::= dot
 key-facts-kv ::= "\"key_facts\"" space ":" space key-facts
+null ::= "null" space
+number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
 question-answers ::= "[" space (question-answers-item ("," space question-answers-item)*)? "]" space
 question-answers-item ::= "[" space question-answers-item-item ("," space question-answers-item-item){4,} "]" space
 question-answers-item-item ::= QAPair
 question-answers-kv ::= "\"question_answers\"" space ":" space question-answers
-root ::= "{" space key-facts-kv "," space question-answers-kv "}" space
+root ::= "{" space key-facts-kv "," space question-answers-kv ( "," space ( additional-kv ( "," space additional-kv )* ) )? "}" space
 space ::= | " " | "\n" [ \t]{0,20}
 string ::= "\"" char* "\"" space
+value ::= object | array | string | number | boolean | null
 ```
 
 </details>
 
-If you're using [Zod](https://zod.dev/), you can make your objects explicitly strict w/ `z.object(...).strict()` or `z.strictObject(...)`.
-
-Note however that [zod-to-json-schema](https://github.com/StefanTerdell/zod-to-json-schema) currently always seems to set `"additionalProperties": false` anyway (even w/ zod schemas on which `nonstrict()` / `passthrough()` was called).
+If you're using [Zod](https://zod.dev/), you can make your objects to explicitly allow extra properties w/ `nonstrict()` / `passthrough()` (or explicitly no extra props w/ `z.object(...).strict()` or `z.strictObject(...)`) but note that [zod-to-json-schema](https://github.com/StefanTerdell/zod-to-json-schema) currently always sets `"additionalProperties": false` anyway.
 
 ```js
 import { z } from 'zod';
diff --git a/src/llama.cpp b/src/llama.cpp
index 3dc0f8535..3edaa98e8 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -217,6 +217,7 @@ enum llm_arch {
     LLM_ARCH_INTERNLM2,
     LLM_ARCH_MINICPM,
     LLM_ARCH_GEMMA,
+    LLM_ARCH_GEMMA2,
     LLM_ARCH_STARCODER2,
     LLM_ARCH_MAMBA,
     LLM_ARCH_XVERSE,
@@ -257,6 +258,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_INTERNLM2,       "internlm2"    },
     { LLM_ARCH_MINICPM,         "minicpm"      },
     { LLM_ARCH_GEMMA,           "gemma"        },
+    { LLM_ARCH_GEMMA2,          "gemma2"       },
     { LLM_ARCH_STARCODER2,      "starcoder2"   },
     { LLM_ARCH_MAMBA,           "mamba"        },
     { LLM_ARCH_XVERSE,          "xverse"       },
@@ -478,10 +480,12 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_NORM,
     LLM_TENSOR_ATTN_NORM_2,
     LLM_TENSOR_ATTN_OUT_NORM,
+    LLM_TENSOR_ATTN_POST_NORM,
     LLM_TENSOR_ATTN_ROT_EMBD,
     LLM_TENSOR_FFN_GATE_INP,
     LLM_TENSOR_FFN_GATE_INP_SHEXP,
     LLM_TENSOR_FFN_NORM,
+    LLM_TENSOR_FFN_POST_NORM,
     LLM_TENSOR_FFN_GATE,
     LLM_TENSOR_FFN_DOWN,
     LLM_TENSOR_FFN_UP,
@@ -1004,6 +1008,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_GEMMA2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
     {
         LLM_ARCH_STARCODER2,
         {
@@ -2039,6 +2061,8 @@ enum e_model {
     MODEL_16x12B,
     MODEL_10B_128x3_66B,
     MODEL_57B_A14B,
+    MODEL_9B,
+    MODEL_27B,
 };
 
 static const size_t kiB = 1024;
@@ -2215,6 +2239,7 @@ struct llama_layer {
     struct ggml_tensor * attn_q_a_norm;
     struct ggml_tensor * attn_kv_a_norm;
     struct ggml_tensor * attn_sub_norm;
+    struct ggml_tensor * attn_post_norm;
     struct ggml_tensor * ffn_sub_norm;
 
     // attention
@@ -2238,6 +2263,7 @@ struct llama_layer {
     // normalization
     struct ggml_tensor * ffn_norm;
     struct ggml_tensor * ffn_norm_b;
+    struct ggml_tensor * ffn_post_norm;
     struct ggml_tensor * layer_out_norm;
     struct ggml_tensor * layer_out_norm_b;
     struct ggml_tensor * ffn_norm_exps;
@@ -4269,6 +4295,8 @@ static const char * llama_model_type_name(e_model type) {
         case MODEL_16x12B:        return "16x12B";
         case MODEL_10B_128x3_66B: return "10B+128x3.66B";
         case MODEL_57B_A14B:      return "57B.A14B";
+        case MODEL_9B:            return "9B";
+        case MODEL_27B:           return "27B";
         default:                  return "?B";
     }
 }
@@ -4671,6 +4699,16 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                }
             } break;
+        case LLM_ARCH_GEMMA2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 42: model.type = e_model::MODEL_9B; break;
+                    case 46: model.type = e_model::MODEL_27B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
         case LLM_ARCH_STARCODER2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -6512,6 +6550,40 @@ static bool llm_load_tensors(
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                     }
                 } break;
+            case LLM_ARCH_GEMMA2:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+                    const int64_t n_ff          = hparams.n_ff;
+                    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+                    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+                    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
+                        layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
+                    }
+                } break;
             case LLM_ARCH_STARCODER2:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@@ -10923,6 +10995,125 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_gemma2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
+                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
+                cb(Qcur, "Qcur_scaled", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
+                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_post_norm", il);
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
+
+            cur = llm_build_norm(ctx0, sa_out, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, 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_GELU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                model.layers[il].ffn_post_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+            cb(cur, "ffn_post_norm", -1);
+
+            cur = ggml_add(ctx0, cur, sa_out);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+
     struct ggml_cgraph * build_starcoder2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -12303,6 +12494,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_gemma();
             } break;
+        case LLM_ARCH_GEMMA2:
+            {
+                result = llm.build_gemma2();
+            } break;
         case LLM_ARCH_STARCODER2:
             {
                 result = llm.build_starcoder2();
@@ -17597,6 +17792,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_PHI2:
         case LLM_ARCH_PHI3:
         case LLM_ARCH_GEMMA:
+        case LLM_ARCH_GEMMA2:
         case LLM_ARCH_STARCODER2:
         case LLM_ARCH_GPTNEOX:
             return LLAMA_ROPE_TYPE_NEOX;
@@ -19417,7 +19613,10 @@ static int32_t llama_chat_apply_template_internal(
     std::string & dest, bool add_ass) {
     // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
     std::stringstream ss;
-    if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
+    auto tmpl_contains = [&tmpl](std::string haystack) -> bool {
+        return tmpl.find(haystack) != std::string::npos;
+    };
+    if (tmpl == "chatml" || tmpl_contains("<|im_start|>")) {
         // chatml template
         for (auto message : chat) {
             ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
@@ -19425,16 +19624,16 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|im_start|>assistant\n";
         }
-    } else if (tmpl == "llama2" || tmpl == "mistral" || tmpl.find("[INST]") != std::string::npos) {
+    } else if (tmpl == "llama2" || tmpl == "mistral" || tmpl_contains("[INST]")) {
         // llama2 template and its variants
         // [variant] support system message
-        bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos || tmpl == "mistral";
+        bool support_system_message = tmpl_contains("<<SYS>>") || tmpl == "mistral";
         // [variant] space before + after response
-        bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
+        bool space_around_response = tmpl_contains("' ' + eos_token");
         // [variant] add BOS inside history
-        bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos;
+        bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
         // [variant] trim spaces from the input message
-        bool strip_message = tmpl.find("content.strip()") != std::string::npos;
+        bool strip_message = tmpl_contains("content.strip()");
         // construct the prompt
         bool is_inside_turn = true; // skip BOS at the beginning
         ss << "[INST] ";
@@ -19460,7 +19659,7 @@ static int32_t llama_chat_apply_template_internal(
             }
         }
         // llama2 templates seem to not care about "add_generation_prompt"
-    } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos)) {
+    } else if (tmpl == "phi3" || (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>"))) {
         // Phi 3
         for (auto message : chat) {
             std::string role(message->role);
@@ -19469,7 +19668,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
-    } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
+    } else if (tmpl == "zephyr" || tmpl_contains("<|user|>")) {
         // zephyr template
         for (auto message : chat) {
             ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
@@ -19477,7 +19676,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>\n";
         }
-    } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
+    } else if (tmpl == "monarch" || tmpl_contains("bos_token + message['role']")) {
         // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
         for (auto message : chat) {
             std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
@@ -19486,7 +19685,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<s>assistant\n";
         }
-    } else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
+    } else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl_contains("<start_of_turn>")) {
         // google/gemma-7b-it
         std::string system_prompt = "";
         for (auto message : chat) {
@@ -19508,7 +19707,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<start_of_turn>model\n";
         }
-    } else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) {
+    } else if (tmpl == "orion" || tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
         // OrionStarAI/Orion-14B-Chat
         std::string system_prompt = "";
         for (auto message : chat) {
@@ -19528,7 +19727,7 @@ static int32_t llama_chat_apply_template_internal(
                 ss << message->content << "</s>";
             }
         }
-    } else if (tmpl == "openchat" || tmpl.find("GPT4 Correct ") != std::string::npos) {
+    } else if (tmpl == "openchat" || tmpl_contains("GPT4 Correct ")) {
         // openchat/openchat-3.5-0106,
         for (auto message : chat) {
             std::string role(message->role);
@@ -19542,13 +19741,13 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "GPT4 Correct Assistant:";
         }
-    } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl.find("USER: ") != std::string::npos && tmpl.find("ASSISTANT: ") != std::string::npos)) {
+    } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: "))) {
         // eachadea/vicuna-13b-1.1 (and Orca variant)
         for (auto message : chat) {
             std::string role(message->role);
             if (role == "system") {
                 // Orca-Vicuna variant uses a system prefix
-                if (tmpl == "vicuna-orca" || tmpl.find("SYSTEM: ") != std::string::npos) {
+                if (tmpl == "vicuna-orca" || tmpl_contains("SYSTEM: ")) {
                     ss << "SYSTEM: " << message->content << "\n";
                 } else {
                     ss << message->content << "\n\n";
@@ -19562,7 +19761,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "ASSISTANT:";
         }
-    } else if (tmpl == "deepseek" || (tmpl.find("### Instruction:") != std::string::npos && tmpl.find("<|EOT|>") != std::string::npos)) {
+    } else if (tmpl == "deepseek" || (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>"))) {
         // deepseek-ai/deepseek-coder-33b-instruct
         for (auto message : chat) {
             std::string role(message->role);
@@ -19577,7 +19776,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "### Response:\n";
         }
-    } else if (tmpl == "command-r" || (tmpl.find("<|START_OF_TURN_TOKEN|>") != std::string::npos && tmpl.find("<|USER_TOKEN|>") != std::string::npos)) {
+    } else if (tmpl == "command-r" || (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>"))) {
         // CohereForAI/c4ai-command-r-plus
         for (auto message : chat) {
             std::string role(message->role);
@@ -19592,7 +19791,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
         }
-    } else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
+    } else if (tmpl == "llama3" || (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>"))) {
         // Llama 3
         for (auto message : chat) {
             std::string role(message->role);
@@ -19601,6 +19800,33 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
         }
+    } else if (tmpl == "minicpm" || tmpl_contains(u8"<用户>")) {
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "user") {
+                ss << u8"<用户>";
+                ss << trim(message->content);
+                ss << "<AI>";
+            } else {
+                ss << trim(message->content);
+            }
+        }
+    } else if (tmpl == "deepseek2" || tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
+        // DeepSeek-V2
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "\n\n";
+            } else if (role == "user") {
+                ss << "User: " << message->content << "\n\n";
+            } else if (role == "assistant") {
+                ss << "Assistant: " << message->content << u8"<｜end▁of▁sentence｜>";
+            }
+        }
+        if (add_ass) {
+            ss << "Assistant:";
+        }
     } else {
         // template not supported
         return -1;
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index d19ba8633..b154038b2 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -57,7 +57,11 @@ int main(void) {
         //Phi-3-medium
         "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
         //Phi-3-vision
-        "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}"
+        "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
+        // DeepSeek-V2
+        "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -94,6 +98,10 @@ int main(void) {
         "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
         //Phi-3-vision
         "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
+        // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+        u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
+        // DeepSeek-V2
+        u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant:    I am an assistant   <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index dd277e3be..c9836370b 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -993,6 +993,40 @@ static void test_json_schema() {
         }
     );
 
+    test_schema(
+        "simple pattern",
+        // Schema
+        R"""({
+            "pattern": "^[a-zA-Z0-9_-]*$"
+        })""",
+        // Passing strings
+        {
+            R"""("")""",
+            R"""("He_llo-12")""",
+        },
+        // Failing strings
+        {
+            R"""("!")""",
+            R"""("Hello World")""",
+        }
+    );
+
+    test_schema(
+        "pattern with escapes",
+        // Schema
+        R"""({
+            "pattern": "^a\\^\\$\\.\\[\\]\\(\\)\\|\\{\\}\\*\\+\\?b$"
+        })""",
+        // Passing strings
+        {
+            R"""("a^$.[]()|{}*+?b")""",
+        },
+        // Failing strings
+        {
+            R"""("ab")""",
+        }
+    );
+
     test_schema(
         "",
         // Schema
@@ -1062,8 +1096,6 @@ static void test_json_schema() {
             R"""({ "number": 1600, "street_name": "Pennsylvania" })""",
             // "By extension, even an empty object is valid"
             R"""({})""",
-            // "By default, providing additional properties is valid"
-            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue", "direction":"NW"})""",
             R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type": "Avenue" })""",
         },
         // Failing strings
@@ -1074,6 +1106,9 @@ static void test_json_schema() {
             R"""({ "street_name": "Pennsylvania", "number": 1600 })""",
             // Reorder properties
             R"""({ "number": "1600", "street_name": "Pennsylvania", "street_type":"Avenue"})""",
+            // "Additional properties default to false for generation, even though the spec says true.
+            R"""({ "number": 1600, "street_name": "Pennsylvania", "street_type":"Avenue", "direction":"NW"})""",
+
         }
     );
 
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
index ec57dfc83..7e547698b 100755
--- a/tests/test-json-schema-to-grammar.cpp
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -1281,26 +1281,30 @@ int main() {
         }
     });
 
-    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python -c \"import sys; exit(1) if sys.version_info < (3, 8) else print('Python version is sufficient')\"") == 0)) {
-        test_all("Python", [](const TestCase & tc) {
-            write("test-json-schema-input.tmp", tc.schema);
-            tc.verify_status(std::system(
-                "python ./examples/json_schema_to_grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
-            tc.verify(read("test-grammar-output.tmp"));
-        });
+    if (getenv("LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR")) {
+        fprintf(stderr, "\033[33mWARNING: Skipping slow tests on emulator.\n\033[0m");
     } else {
-        fprintf(stderr, "\033[33mWARNING: Python not found (min version required is 3.8), skipping Python JSON schema -> grammar tests.\n\033[0m");
-    }
+        if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python -c \"import sys; exit(1) if sys.version_info < (3, 8) else print('Python version is sufficient')\"") == 0)) {
+            test_all("Python", [](const TestCase & tc) {
+                write("test-json-schema-input.tmp", tc.schema);
+                tc.verify_status(std::system(
+                    "python ./examples/json_schema_to_grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+                tc.verify(read("test-grammar-output.tmp"));
+            });
+        } else {
+            fprintf(stderr, "\033[33mWARNING: Python not found (min version required is 3.8), skipping Python JSON schema -> grammar tests.\n\033[0m");
+        }
 
-    if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
-        test_all("JavaScript", [](const TestCase & tc) {
-            write("test-json-schema-input.tmp", tc.schema);
-            tc.verify_status(std::system(
-                "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
-            tc.verify(read("test-grammar-output.tmp"));
-        });
-    } else {
-        fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m");
+        if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
+            test_all("JavaScript", [](const TestCase & tc) {
+                write("test-json-schema-input.tmp", tc.schema);
+                tc.verify_status(std::system(
+                    "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+                tc.verify(read("test-grammar-output.tmp"));
+            });
+        } else {
+            fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m");
+        }
     }
 
     test_all("Check Expectations Validity", [](const TestCase & tc) {