diff --git a/.editorconfig b/.editorconfig
index 135a7e4bc..f8245b85c 100644
--- a/.editorconfig
+++ b/.editorconfig
@@ -17,3 +17,6 @@ indent_style = tab
 
 [prompts/*.txt]
 insert_final_newline = unset
+
+[examples/server/public/*]
+indent_size = 2
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 20fd8c2b5..9d0a6c222 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -18,7 +18,6 @@ on:
 env:
   BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
   GGML_NLOOP: 3
-  GGML_NITER: 1
   GGML_N_THREADS: 1
 
 jobs:
diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml
new file mode 100644
index 000000000..392db8a08
--- /dev/null
+++ b/.github/workflows/code-coverage.yml
@@ -0,0 +1,36 @@
+name: Code Coverage
+on: [push, pull_request]
+
+env:
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+
+jobs:
+  run:
+    runs-on: ubuntu-20.04
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential gcc-8 lcov
+
+      - name: Build
+        run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests
+
+      - name: Run tests
+        run: CC=gcc-8 make test
+
+      - name: Generate coverage report
+        run: |
+          make coverage
+          make lcov-report
+
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3
+        env:
+           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+        with:
+          files: lcov-report/coverage.info
diff --git a/.gitignore b/.gitignore
index 8b5f45a2d..e4157e804 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,10 @@
 *.exe
 *.dll
 *.log
+*.gcov
+*.gcno
+*.gcda
+*.dot
 .DS_Store
 .build/
 .cache/
@@ -17,6 +21,9 @@
 .vs/
 .vscode/
 
+lcov-report/
+gcovr-report/
+
 build*/
 out/
 tmp/
@@ -45,6 +52,7 @@ models-mnt
 /baby-llama
 /beam-search
 /save-load-state
+/speculative
 build-info.h
 arm_neon.h
 compile_commands.json
diff --git a/Makefile b/Makefile
index d5a6632e1..dea7cf74b 100644
--- a/Makefile
+++ b/Makefile
@@ -1,9 +1,12 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search tests/test-c.o
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1
 
+# Code coverage output files
+COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
+
 ifndef UNAME_S
 UNAME_S := $(shell uname -s)
 endif
@@ -56,6 +59,18 @@ test:
 
 all: $(BUILD_TARGETS) $(TEST_TARGETS)
 
+coverage: ## Run code coverage
+	gcov -pb tests/*.cpp
+
+lcov-report: coverage ## Generate lcov report
+	mkdir -p lcov-report
+	lcov --capture --directory . --output-file lcov-report/coverage.info
+	genhtml lcov-report/coverage.info --output-directory lcov-report
+
+gcovr-report: coverage ## Generate gcovr report
+	mkdir -p gcovr-report
+	gcovr --root . --html --html-details --output gcovr-report/coverage.html
+
 ifdef RISCV_CROSS_COMPILE
 CC	:= riscv64-unknown-linux-gnu-gcc
 CXX	:= riscv64-unknown-linux-gnu-g++
@@ -92,6 +107,11 @@ ifdef LLAMA_SERVER_VERBOSE
 	MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
 endif
 
+
+ifdef LLAMA_CODE_COVERAGE
+	CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase ''
+endif
+
 ifdef LLAMA_DISABLE_LOGS
 	CFLAGS   += -DLOG_DISABLE_LOGS
 	CXXFLAGS += -DLOG_DISABLE_LOGS
@@ -417,7 +437,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
 
 #
 # Examples
@@ -475,6 +495,16 @@ baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS)
 beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
+<<<<<<< HEAD
+=======
+speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
+BUILD_TARGETS += metal
+endif
+
+>>>>>>> master
 ifdef LLAMA_METAL
 metal: examples/metal/metal.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 000000000..a301c5b2c
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,14 @@
+comment: off
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 0
+        base: auto
+    patch:
+      default:
+        target: auto
+        threshold: 0
+        base: auto
diff --git a/common/common.cpp b/common/common.cpp
index b99b5db27..74e1b6fd2 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_keep = std::stoi(argv[i]);
+        } else if (arg == "--draft") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_draft = std::stoi(argv[i]);
         } else if (arg == "--chunks") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.model = argv[i];
+        } else if (arg == "-md" || arg == "--model-draft") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.model_draft = argv[i];
         } else if (arg == "-a" || arg == "--alias") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -638,6 +650,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
     fprintf(stdout, "  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
     fprintf(stdout, "  --keep N              number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
+    fprintf(stdout, "  --draft N             number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
     fprintf(stdout, "  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     if (llama_mlock_supported()) {
         fprintf(stdout, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
@@ -669,6 +682,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stdout, "  -m FNAME, --model FNAME\n");
     fprintf(stdout, "                        model path (default: %s)\n", params.model.c_str());
+    fprintf(stdout, "  -md FNAME, --model-draft FNAME\n");
+    fprintf(stdout, "                        draft model for speculative decoding (default: %s)\n", params.model.c_str());
     fprintf(stdout, "  -ld LOGDIR, --logdir LOGDIR\n");
     fprintf(stdout, "                        path under which to save YAML logs (no logging if unset)\n");
     fprintf(stdout, "\n");
@@ -754,6 +769,14 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
         params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
     }
 
+    {
+        LOG("warming up the model with an empty run\n");
+
+        const std::vector<llama_token> tmp = { llama_token_bos(lctx), };
+        llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads);
+        llama_reset_timings(lctx);
+    }
+
     return std::make_tuple(model, lctx);
 }
 
@@ -826,6 +849,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
     return result;
 }
 
+//
+// Sampling utils
+//
+
+llama_token llama_sample_token(
+                  struct llama_context * ctx,
+                  struct llama_context * ctx_guidance,
+                  struct llama_grammar * grammar,
+               const struct gpt_params & params,
+        const std::vector<llama_token> & last_tokens,
+         std::vector<llama_token_data> & candidates,
+                                   int   idx) {
+    const int n_ctx   = llama_n_ctx(ctx);
+    const int n_vocab = llama_n_vocab(ctx);
+
+    const float   temp            = params.temp;
+    const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
+    const float   top_p           = params.top_p;
+    const float   tfs_z           = params.tfs_z;
+    const float   typical_p       = params.typical_p;
+    const int32_t repeat_last_n   = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
+    const float   repeat_penalty  = params.repeat_penalty;
+    const float   alpha_presence  = params.presence_penalty;
+    const float   alpha_frequency = params.frequency_penalty;
+    const int     mirostat        = params.mirostat;
+    const float   mirostat_tau    = params.mirostat_tau;
+    const float   mirostat_eta    = params.mirostat_eta;
+    const bool    penalize_nl     = params.penalize_nl;
+
+    llama_token id = 0;
+
+    float * logits = llama_get_logits(ctx) + idx * n_vocab;
+
+    // Apply params.logit_bias map
+    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
+        logits[it->first] += it->second;
+    }
+
+    candidates.clear();
+    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+        candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+    }
+
+    llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
+
+    if (ctx_guidance) {
+        llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
+    }
+
+    // apply penalties
+    if (!last_tokens.empty()) {
+        const float nl_logit = logits[llama_token_nl(ctx)];
+        const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);
+
+        llama_sample_repetition_penalty(ctx, &cur_p,
+                last_tokens.data() + last_tokens.size() - last_n_repeat,
+                last_n_repeat, repeat_penalty);
+        llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
+                last_tokens.data() + last_tokens.size() - last_n_repeat,
+                last_n_repeat, alpha_frequency, alpha_presence);
+
+        if (!penalize_nl) {
+            for (size_t idx = 0; idx < cur_p.size; idx++) {
+                if (cur_p.data[idx].id == llama_token_nl(ctx)) {
+                    cur_p.data[idx].logit = nl_logit;
+                    break;
+                }
+            }
+        }
+    }
+
+    if (grammar != NULL) {
+        llama_sample_grammar(ctx, &cur_p, grammar);
+    }
+
+    if (temp <= 0) {
+        // Greedy sampling
+        id = llama_sample_token_greedy(ctx, &cur_p);
+    } else {
+        if (mirostat == 1) {
+            static float mirostat_mu = 2.0f * mirostat_tau;
+            const int mirostat_m = 100;
+            llama_sample_temperature(ctx, &cur_p, temp);
+            id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+        } else if (mirostat == 2) {
+            static float mirostat_mu = 2.0f * mirostat_tau;
+            llama_sample_temperature(ctx, &cur_p, temp);
+            id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+        } else {
+            // Temperature sampling
+            llama_sample_top_k      (ctx, &cur_p, top_k, 1);
+            llama_sample_tail_free  (ctx, &cur_p, tfs_z, 1);
+            llama_sample_typical    (ctx, &cur_p, typical_p, 1);
+            llama_sample_top_p      (ctx, &cur_p, top_p, 1);
+            llama_sample_temperature(ctx, &cur_p, temp);
+
+            {
+                const int n_top = 10;
+                LOG("top %d candidates:\n", n_top);
+
+                for (int i = 0; i < n_top; i++) {
+                    const llama_token id = cur_p.data[i].id;
+                    LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
+                }
+            }
+
+            id = llama_sample_token(ctx, &cur_p);
+
+            LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
+        }
+    }
+    // printf("`%d`", candidates_p.size);
+
+    if (grammar != NULL) {
+        llama_grammar_accept_token(ctx, grammar, id);
+    }
+
+    return id;
+}
+
+//
+// YAML utils
+//
+
 // returns true if successful, false otherwise
 bool create_directory_with_parents(const std::string & path) {
 #ifdef _WIN32
@@ -1064,6 +1211,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
     fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
     fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
+    fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
     fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
     fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
     fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
diff --git a/common/common.h b/common/common.h
index 28cc04772..85ac0df9b 100644
--- a/common/common.h
+++ b/common/common.h
@@ -32,6 +32,7 @@ struct gpt_params {
     int32_t n_ctx                           = 512;  // context size
     int32_t n_batch                         = 512;  // batch size for prompt processing (must be >=32 to use BLAS)
     int32_t n_keep                          = 0;    // number of tokens to keep from initial prompt
+    int32_t n_draft                         = 16;   // number of tokens to draft during speculative decoding
     int32_t n_chunks                        = -1;   // max number of chunks to process (-1 = unlimited)
     int32_t n_gpu_layers                    = -1;   // number of layers to store in VRAM (-1 - use default)
     int32_t main_gpu                        = 0;    // the GPU that is used for scratch and small tensors
@@ -63,6 +64,7 @@ struct gpt_params {
     float       cfg_scale         = 1.f;   // How strong is guidance
 
     std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
+    std::string model_draft       = "";                              // draft model for speculative decoding
     std::string model_alias       = "unknown"; // model alias
     std::string prompt            = "";
     std::string path_prompt_cache = "";  // path to file for saving/loading prompt eval state
@@ -156,6 +158,40 @@ std::string llama_detokenize_bpe(
                          llama_context * ctx,
         const std::vector<llama_token> & tokens);
 
+//
+// Sampling utils
+//
+
+// this is a common sampling function used across the examples for convenience
+// it can serve as a starting point for implementing your own sampling function
+//
+// required:
+//  - ctx:    context to use for sampling
+//  - params: sampling parameters
+//
+// optional:
+//  - ctx_guidance:  context to use for classifier-free guidance, ignore if NULL
+//  - grammar:       grammar to use for sampling, ignore if NULL
+//  - last_tokens:   needed for repetition penalty, ignore if empty
+//  - idx:           sample from llama_get_logits(ctx) + idx * n_vocab
+//
+// returns:
+//  - token:      sampled token
+//  - candidates: vector of candidate tokens
+//
+llama_token llama_sample_token(
+                  struct llama_context * ctx,
+                  struct llama_context * ctx_guidance,
+                  struct llama_grammar * grammar,
+               const struct gpt_params & params,
+        const std::vector<llama_token> & last_tokens,
+         std::vector<llama_token_data> & candidates,
+                                   int   idx = 0);
+
+//
+// YAML utils
+//
+
 bool create_directory_with_parents(const std::string & path);
 void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
 void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 6e65eb087..884c42764 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -23,6 +23,7 @@ else()
     add_subdirectory(train-text-from-scratch)
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(simple)
+    add_subdirectory(speculative)
     add_subdirectory(embd-input)
     add_subdirectory(llama-bench)
     add_subdirectory(beam-search)
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
old mode 100755
new mode 100644
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 38ad5df48..9201b53bd 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -116,7 +116,7 @@ int main(int argc, char ** argv) {
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("main", "log"));
     LOG_TEE("Log start\n");
-    log_dump_cmdline(argc,argv);
+    log_dump_cmdline(argc, argv);
 #endif // LOG_DISABLE_LOGS
 
     // TODO: Dump params ?
@@ -424,8 +424,9 @@ int main(int argc, char ** argv) {
     LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
     LOG_TEE("\n\n");
 
+    struct llama_grammar * grammar = NULL;
     grammar_parser::parse_state parsed_grammar;
-    llama_grammar *             grammar = NULL;
+
     if (!params.grammar.empty()) {
         parsed_grammar = grammar_parser::parse(params.grammar.c_str());
         // will be empty (default) if there are parse errors
@@ -449,8 +450,8 @@ int main(int argc, char ** argv) {
     }
 
     // TODO: replace with ring-buffer
-    std::vector<llama_token> last_n_tokens(n_ctx);
-    std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
+    std::vector<llama_token> last_tokens(n_ctx);
+    std::fill(last_tokens.begin(), last_tokens.end(), 0);
 
     if (params.interactive) {
         const char *control_message;
@@ -491,13 +492,10 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
-    {
-        LOG("warming up the model with an empty run\n");
+    const int n_vocab = llama_n_vocab(ctx);
 
-        const std::vector<llama_token> tmp = { llama_token_bos(ctx), };
-        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
-        llama_reset_timings(ctx);
-    }
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
 
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
@@ -536,8 +534,8 @@ int main(int argc, char ** argv) {
 
                 LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
 
-                // insert n_left/2 tokens at the start of embd from last_n_tokens
-                embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
+                // insert n_left/2 tokens at the start of embd from last_tokens
+                embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size());
 
                 LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
 
@@ -636,20 +634,6 @@ int main(int argc, char ** argv) {
         embd_guidance.clear();
 
         if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
-            const float   temp            = params.temp;
-            const int32_t top_k           = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
-            const float   top_p           = params.top_p;
-            const float   tfs_z           = params.tfs_z;
-            const float   typical_p       = params.typical_p;
-            const int32_t repeat_last_n   = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
-            const float   repeat_penalty  = params.repeat_penalty;
-            const float   alpha_presence  = params.presence_penalty;
-            const float   alpha_frequency = params.frequency_penalty;
-            const int     mirostat        = params.mirostat;
-            const float   mirostat_tau    = params.mirostat_tau;
-            const float   mirostat_eta    = params.mirostat_eta;
-            const bool    penalize_nl     = params.penalize_nl;
-
             // optionally save the session on first sample (for faster prompt loading next time)
             if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
                 need_to_save_session = false;
@@ -658,98 +642,12 @@ int main(int argc, char ** argv) {
                 LOG("saved session to %s\n", path_session.c_str());
             }
 
-            llama_token id = 0;
+            const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
 
-            {
-                auto logits  = llama_get_logits(ctx);
-                auto n_vocab = llama_n_vocab(ctx);
+            last_tokens.erase(last_tokens.begin());
+            last_tokens.push_back(id);
 
-                // Apply params.logit_bias map
-                for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
-                    logits[it->first] += it->second;
-                }
-
-                std::vector<llama_token_data> candidates;
-                candidates.reserve(n_vocab);
-                for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                    candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-                }
-
-                llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
-
-                if (ctx_guidance) {
-                    llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
-                }
-
-                // Apply penalties
-                float nl_logit = logits[llama_token_nl(ctx)];
-                auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
-                llama_sample_repetition_penalty(ctx, &cur_p,
-                    last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                    last_n_repeat, repeat_penalty);
-                llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
-                    last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                    last_n_repeat, alpha_frequency, alpha_presence);
-                if (!penalize_nl) {
-                    for (size_t idx = 0; idx < cur_p.size; idx++) {
-                        if (cur_p.data[idx].id == llama_token_nl(ctx)) {
-                            cur_p.data[idx].logit = nl_logit;
-                            break;
-                        }
-                    }
-                }
-
-                if (grammar != NULL) {
-                    llama_sample_grammar(ctx, &cur_p, grammar);
-                }
-
-                if (temp <= 0) {
-                    // Greedy sampling
-                    id = llama_sample_token_greedy(ctx, &cur_p);
-                } else {
-                    if (mirostat == 1) {
-                        static float mirostat_mu = 2.0f * mirostat_tau;
-                        const int mirostat_m = 100;
-                        llama_sample_temperature(ctx, &cur_p, temp);
-                        id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
-                    } else if (mirostat == 2) {
-                        static float mirostat_mu = 2.0f * mirostat_tau;
-                        llama_sample_temperature(ctx, &cur_p, temp);
-                        id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-                    } else {
-                        // Temperature sampling
-                        llama_sample_top_k      (ctx, &cur_p, top_k, 1);
-                        llama_sample_tail_free  (ctx, &cur_p, tfs_z, 1);
-                        llama_sample_typical    (ctx, &cur_p, typical_p, 1);
-                        llama_sample_top_p      (ctx, &cur_p, top_p, 1);
-                        llama_sample_temperature(ctx, &cur_p, temp);
-
-                        {
-                            const int n_top = 10;
-                            LOG("top %d candidates:\n", n_top);
-
-                            for (int i = 0; i < n_top; i++) {
-                                const llama_token id = cur_p.data[i].id;
-                                LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
-                            }
-                        }
-
-                        id = llama_sample_token(ctx, &cur_p);
-
-                        LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
-                    }
-                }
-                // printf("`%d`", candidates_p.size);
-
-                if (grammar != NULL) {
-                    llama_grammar_accept_token(ctx, grammar, id);
-                }
-
-                last_n_tokens.erase(last_n_tokens.begin());
-                last_n_tokens.push_back(id);
-
-                LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens));
-            }
+            LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens));
 
             embd.push_back(id);
 
@@ -765,8 +663,8 @@ int main(int argc, char ** argv) {
             LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
             while ((int) embd_inp.size() > n_consumed) {
                 embd.push_back(embd_inp[n_consumed]);
-                last_n_tokens.erase(last_n_tokens.begin());
-                last_n_tokens.push_back(embd_inp[n_consumed]);
+                last_tokens.erase(last_tokens.begin());
+                last_tokens.push_back(embd_inp[n_consumed]);
                 ++n_consumed;
                 if ((int) embd.size() >= params.n_batch) {
                     break;
@@ -799,7 +697,7 @@ int main(int argc, char ** argv) {
             // check for reverse prompt
             if (params.antiprompt.size()) {
                 std::string last_output;
-                for (auto id : last_n_tokens) {
+                for (auto id : last_tokens) {
                     last_output += llama_token_to_piece(ctx, id);
                 }
 
@@ -830,7 +728,7 @@ int main(int argc, char ** argv) {
             }
 
             // deal with end of text token in interactive mode
-            if (last_n_tokens.back() == llama_token_eos(ctx)) {
+            if (last_tokens.back() == llama_token_eos(ctx)) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -932,7 +830,7 @@ int main(int argc, char ** argv) {
                     if (grammar != NULL) {
                         llama_grammar_free(grammar);
 
-                        std::vector<const llama_grammar_element *> grammar_rules( parsed_grammar.c_rules());
+                        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
                         grammar = llama_grammar_init(
                             grammar_rules.data(), grammar_rules.size(),
                             parsed_grammar.symbol_ids.at("root"));
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index 84e6f97ba..f30232929 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -210,1024 +210,1120 @@ unsigned char index_html[] = {
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-  0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x3c,
-  0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20,
-  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f,
-  0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
-  0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20,
-  0x20, 0x20, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a,
-  0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68,
-  0x28, 0x41, 0x70, 0x70, 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d,
-  0x65, 0x6e, 0x74, 0x2e, 0x71, 0x75, 0x65, 0x72, 0x79, 0x53, 0x65, 0x6c,
-  0x65, 0x63, 0x74, 0x6f, 0x72, 0x28, 0x27, 0x23, 0x63, 0x6f, 0x6e, 0x74,
-  0x61, 0x69, 0x6e, 0x65, 0x72, 0x27, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20,
-  0x3c, 0x2f, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f,
-  0x68, 0x65, 0x61, 0x64, 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79,
-  0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d,
-  0x22, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e,
-  0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69,
-  0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c,
-  0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62,
-  0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c,
-  0x3e, 0x0a
+  0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e,
+  0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64,
+  0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b,
+  0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20,
+  0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70,
+  0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e,
+  0x71, 0x75, 0x65, 0x72, 0x79, 0x53, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x6f,
+  0x72, 0x28, 0x27, 0x23, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65,
+  0x72, 0x27, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x63,
+  0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64,
+  0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x20, 0x20,
+  0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e,
+  0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69,
+  0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64,
+  0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, 0x3c, 0x2f,
+  0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e,
+  0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a
 };
-unsigned int index_html_len = 27218;
+unsigned int index_html_len = 28018;
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 959a9b9a6..1bf2a8b3a 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -145,7 +145,29 @@
       color: #888;
     }
 
+
+    @keyframes loading-bg-wipe {
+      0% {
+        background-position: 0%;
+      }
+      100% {
+        background-position: 100%;
+      }
+    }
+
+    .loading {
+      --loading-color-1: #eeeeee00;
+      --loading-color-2: #eeeeeeff;
+      background-size: 50% 100%;
+      background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1));
+      animation: loading-bg-wipe 2s linear infinite;
+    }
+
     @media (prefers-color-scheme: dark) {
+      .loading {
+        --loading-color-1: #22222200;
+        --loading-color-2: #222222ff;
+      }
       .popover-content {
         background-color: black;
       }
@@ -321,7 +343,10 @@
     const llamaStats = signal(null)
     const controller = signal(null)
 
-    const generating = computed(() => controller.value == null )
+    // currently generating a completion?
+    const generating = computed(() => controller.value != null)
+
+    // has the user started a chat?
     const chatStarted = computed(() => session.value.transcript.length > 0)
 
     const transcriptUpdate = (transcript) => {
@@ -430,11 +455,19 @@
       return html`
         <form onsubmit=${submit}>
           <div>
-            <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
+            <textarea
+               className=${generating.value ? "loading" : null}
+               oninput=${(e) => message.value = e.target.value}
+               onkeypress=${enterSubmits}
+               placeholder="Say something..."
+               rows=2
+               type="text"
+               value="${message}"
+            />
           </div>
           <div class="right">
-            <button type="submit" disabled=${!generating.value} >Send</button>
-            <button onclick=${stop} disabled=${generating}>Stop</button>
+            <button type="submit" disabled=${generating.value}>Send</button>
+            <button onclick=${stop} disabled=${!generating.value}>Stop</button>
             <button onclick=${reset}>Reset</button>
           </div>
         </form>
diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt
new file mode 100644
index 000000000..6c5c9456e
--- /dev/null
+++ b/examples/speculative/CMakeLists.txt
@@ -0,0 +1,8 @@
+set(TARGET speculative)
+add_executable(${TARGET} speculative.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+  add_dependencies(${TARGET} BUILD_INFO)
+endif()
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
new file mode 100644
index 000000000..f0400c13f
--- /dev/null
+++ b/examples/speculative/speculative.cpp
@@ -0,0 +1,234 @@
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
+#include "build-info.h"
+
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+
+    if (gpt_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    if (params.model_draft.empty()) {
+        fprintf(stderr, "%s: error: --model-draft is required\n", __func__);
+        return 1;
+    }
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("speculative", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+#endif // LOG_DISABLE_LOGS
+
+    // init llama.cpp
+    llama_backend_init(params.numa);
+
+    llama_model * model_tgt = NULL;
+    llama_model * model_dft = NULL;
+
+    llama_context * ctx_tgt = NULL;
+    llama_context * ctx_dft = NULL;
+
+    // load the target model
+    params.perplexity = true; // HACK: enable logits_all = true
+    std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
+
+    // load the draft model
+    params.model = params.model_draft;
+    std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
+
+    // tokenize the prompt
+    std::vector<llama_token> inp;
+    inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
+
+    const int max_context_size     = llama_n_ctx(ctx_tgt);
+    const int max_tokens_list_size = max_context_size - 4;
+
+    if ((int) inp.size() > max_tokens_list_size) {
+        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
+        return 1;
+    }
+
+    fprintf(stderr, "\n\n");
+
+    for (auto id : inp) {
+        fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str());
+    }
+
+    fflush(stderr);
+
+    const int n_input = inp.size();
+
+    const auto t_enc_start = ggml_time_us();
+
+    // eval the prompt with both models
+    llama_eval(ctx_tgt,  inp.data(), int(inp.size() - 1), 0, params.n_threads);
+    llama_eval(ctx_tgt, &inp.back(),      1, inp.size() - 1, params.n_threads);
+    llama_eval(ctx_dft,  inp.data(),     int(inp.size()), 0, params.n_threads);
+
+    const auto t_enc_end = ggml_time_us();
+
+    // the 2 models should have the same vocab
+    const int n_ctx   = llama_n_ctx(ctx_tgt);
+    const int n_vocab = llama_n_vocab(ctx_tgt);
+    //GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft));
+
+    // how many tokens to draft each time
+    const int n_draft = params.n_draft;
+
+    int n_predict = 0;
+    int n_drafted = 0;
+    int n_accept  = 0;
+
+    int n_past_tgt = inp.size();
+    int n_past_dft = inp.size();
+
+    std::vector<llama_token> drafted;
+
+    std::vector<llama_token> last_tokens(n_ctx);
+    std::fill(last_tokens.begin(), last_tokens.end(), 0);
+
+    for (auto & id : inp) {
+        last_tokens.erase(last_tokens.begin());
+        last_tokens.push_back(id);
+    }
+
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+
+    // used to determine end of generation
+    bool has_eos = false;
+
+    const auto t_dec_start = ggml_time_us();
+
+    while (true) {
+        LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
+
+        // sample from the drafted tokens if any
+        int i_dft = 0;
+        while (true) {
+            const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft);
+
+            last_tokens.erase(last_tokens.begin());
+            last_tokens.push_back(id);
+
+            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens));
+
+            const std::string token_str = llama_token_to_piece(ctx_tgt, id);
+            printf("%s", token_str.c_str());
+            fflush(stdout);
+
+            if (id == llama_token_eos(ctx_tgt)) {
+                has_eos = true;
+            }
+
+            ++n_predict;
+
+            if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
+                LOG("drafted token %d accepted\n", id);
+                ++n_accept;
+                ++n_past_tgt;
+                ++n_past_dft;
+                ++i_dft;
+
+                continue;
+            }
+
+            // the drafted token was rejected or we are out of drafted tokens
+            llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
+            ++n_past_dft;
+
+            drafted.clear();
+            drafted.push_back(id);
+
+            break;
+        }
+
+        if (n_predict > params.n_predict || has_eos) {
+            break;
+        }
+
+        // sample n_draft tokens from the draft model picking the best token
+        int n_past_cur = n_past_dft;
+        for (int i = 0; i < n_draft; ++i) {
+            float * logits = llama_get_logits(ctx_dft);
+
+            candidates.clear();
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+            }
+
+            llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
+
+            // computes softmax and sorts the candidates
+            llama_sample_softmax(ctx_dft, &cur_p);
+
+            for (int i = 0; i < 3; ++i) {
+                LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
+            }
+
+            // too low probability, stop drafting
+            if (cur_p.data[0].p < 2*cur_p.data[1].p) {
+                break;
+            }
+
+            drafted.push_back(cur_p.data[0].id);
+            ++n_drafted;
+
+            if (i < n_draft - 1) {
+                // evaluate the drafted token on the draft model
+                llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
+                ++n_past_cur;
+            }
+        }
+
+        // evaluate the target model on the drafted tokens
+        llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
+        ++n_past_tgt;
+
+        drafted.erase(drafted.begin());
+    }
+
+    auto t_dec_end = ggml_time_us();
+
+    LOG_TEE("\n\n");
+
+    LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
+    LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
+
+    // TODO: make sure these numbers are computed correctly
+    LOG_TEE("\n");
+    LOG_TEE("n_draft   = %d\n", n_draft);
+    LOG_TEE("n_predict = %d\n", n_predict);
+    LOG_TEE("n_drafted = %d\n", n_drafted);
+    LOG_TEE("n_accept  = %d\n", n_accept);
+    LOG_TEE("accept    = %.3f%%\n", 100.0f * n_accept / n_drafted);
+
+    LOG_TEE("\ndraft:\n");
+    llama_print_timings(ctx_dft);
+
+    LOG_TEE("\ntarget:\n");
+    llama_print_timings(ctx_tgt);
+
+    llama_free(ctx_tgt);
+    llama_free_model(model_tgt);
+
+    llama_free(ctx_dft);
+    llama_free_model(model_dft);
+
+    llama_backend_free();
+
+    fprintf(stderr, "\n\n");
+
+    return 0;
+}
diff --git a/ggml-alloc.c b/ggml-alloc.c
index 459f121ca..c1939a4b7 100644
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@@ -1,3 +1,8 @@
+// defines MAP_ANONYMOUS
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
 #include "ggml-alloc.h"
 #include "ggml.h"
 #include <assert.h>
@@ -6,6 +11,26 @@
 #include <stdlib.h>
 #include <string.h>
 
+#ifdef __has_include
+    #if __has_include(<unistd.h>)
+        #include <unistd.h>
+        #if defined(_POSIX_MAPPED_FILES)
+            #include <sys/types.h>
+            #include <sys/mman.h>
+        #endif
+    #endif
+#endif
+
+#if defined(_WIN32)
+    #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
+        #define NOMINMAX
+    #endif
+    #include <windows.h>
+    #include <memoryapi.h>
+#endif
+
+
 #define UNUSED(x) (void)(x)
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
@@ -99,19 +124,24 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens
 }
 #endif
 
-
-static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+static size_t ggml_allocr_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
     return ggml_nbytes(tensor);
 
     UNUSED(alloc);
 }
 
+// check if a tensor is allocated by this buffer
+static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) {
+    void * ptr = tensor->data;
+    return ptr >= alloc->data && (char *)ptr < (char *)alloc->data + alloc->max_size;
+}
+
 void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
 #ifdef GGML_ALLOCATOR_DEBUG
     GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources
     GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
 #endif
-    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
+    size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
     size = aligned_offset(NULL, size, alloc->alignment);
 
     AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
@@ -177,17 +207,17 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
 }
 
 // this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
     void * ptr = tensor->data;
 
-    if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
+    if (ggml_allocr_is_own(alloc, tensor) == false) {
         // the tensor was not allocated in this buffer
         // this can happen because the graph allocator will try to free weights and other tensors from different buffers
         // the easiest way to deal with this is just to ignore it
         return;
     }
 
-    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
+    size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
     size = aligned_offset(NULL, size, alloc->alignment);
     AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
 
@@ -281,24 +311,64 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
     return alloc;
 }
 
-// address and size of the buffer when measuring
-// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
-static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
-#if defined(__ARM_NEON) && !defined(__aarch64__)
-// 32-bit
-// TODO: Use for 32-bit x86 as well
-static const size_t MEASURE_MAX_SIZE  = (1ULL<<32) - 1; // 4 GB
+// OS specific functions to allocate and free uncommitted virtual memory
+static void * alloc_vmem(size_t size) {
+#if defined(_WIN32)
+    return VirtualAlloc(NULL, size, MEM_RESERVE, PAGE_NOACCESS);
+#elif defined(_POSIX_MAPPED_FILES)
+    return mmap(NULL, size, PROT_NONE, MAP_PRIVATE | MAP_ANON, -1, 0);
 #else
-// 64-bit
-static const size_t MEASURE_MAX_SIZE  = 1ULL<<40; // 1 TB
+    // use a fixed address for other platforms
+    uintptr_t base_addr = (uintptr_t)-size - 0x100;
+    return (void *)base_addr;
 #endif
+}
+
+static void free_vmem(void * base_addr, size_t size) {
+#if defined(_WIN32)
+    VirtualFree(base_addr, 0, MEM_RELEASE);
+    UNUSED(size);
+#elif defined(_POSIX_MAPPED_FILES)
+    munmap(base_addr, size);
+#else
+    // nothing to do
+    UNUSED(base_addr);
+    UNUSED(size);
+#endif
+}
+
+// allocate uncommitted virtual memory to measure the size of the graph
+static void alloc_measure_vmem(void ** base_addr, size_t * size) {
+    // 1TB for 64-bit, 1GB for 32-bit
+    *size = sizeof(void *) == 4 ? 1ULL<<30 : 1ULL<<40;
+    do {
+        *base_addr = alloc_vmem(*size);
+        if (*base_addr != NULL) {
+            AT_PRINTF("allocated %.2f GB of virtual memory for measure buffer at %p\n", *size / 1024.0 / 1024.0 / 1024.0, *base_addr);
+            return;
+        }
+        // try again with half the size
+        *size /= 2;
+    } while (*size > 0);
+
+    GGML_ASSERT(!"failed to allocate virtual memory for measure buffer");
+}
+
+static void free_measure_vmem(void * base_addr, size_t size) {
+    free_vmem(base_addr, size);
+}
 
 struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
     struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
 
+    void * base_addr;
+    size_t size;
+
+    alloc_measure_vmem(&base_addr, &size);
+
     *alloc = (struct ggml_allocr){
-        /*.data          = */ MEASURE_BASE_ADDR,
-        /*.size          = */ MEASURE_MAX_SIZE,
+        /*.data          = */ base_addr,
+        /*.size          = */ size,
         /*.alignment     = */ alignment,
         /*.n_free_blocks = */ 0,
         /*.free_blocks   = */ {{0}},
@@ -318,6 +388,9 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
 }
 
 void ggml_allocr_free(struct ggml_allocr * alloc) {
+    if (alloc->measure) {
+        free_measure_vmem(alloc->data, alloc->size);
+    }
     free(alloc);
 }
 
@@ -387,8 +460,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                     }
 
                     // if the node's data is external, then we cannot re-use it
-                    if ((char *) parent->data < (char *) alloc->data ||
-                        (char *) parent->data >= ((char *) alloc->data + alloc->size)) {
+                    if (ggml_allocr_is_own(alloc, parent) == false) {
                         AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
                         continue;
                     }
@@ -422,7 +494,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
     }
 }
 
-static size_t ggml_allocator_alloc_graph_tensors_n(
+static size_t ggml_allocr_alloc_graph_tensors_n(
     struct ggml_allocr * alloc,
     struct ggml_cgraph ** graphs, int n_graphs,
     struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
@@ -500,11 +572,10 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
                 AT_PRINTF("\n");
             }
 
-
             // update parents
             // update immediately if there is no parse_seq
             // update only at barriers if there is parse_seq
-            if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) {
+            if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) {
                 int update_start = alloc->parse_seq_len ? last_barrier_pos : ind;
                 int update_end   = alloc->parse_seq_len ? ind              : ind + 1;
                 for (int i = update_start; i < update_end; i++) {
@@ -528,12 +599,12 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
                                 view_src_hn->n_views -= 1;
                                 AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
                                 if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
-                                    ggml_allocator_free_tensor(alloc, view_src);
+                                    ggml_allocr_free_tensor(alloc, view_src);
                                 }
                             }
                             else {
                                 if (parent->data != node->data) {
-                                    ggml_allocator_free_tensor(alloc, parent);
+                                    ggml_allocr_free_tensor(alloc, parent);
                                 }
                             }
                         }
@@ -550,7 +621,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
             for (int i = 0; outputs[g][i] != NULL; i++) {
                 struct ggml_tensor * output = outputs[g][i];
                 AT_PRINTF("output: %s\n", output->name);
-                ggml_allocator_free_tensor(alloc, output);
+                ggml_allocr_free_tensor(alloc, output);
             }
         }
     }
@@ -559,5 +630,5 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
 }
 
 size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
-    return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
+    return ggml_allocr_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
 }
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 8357f32f7..d2dbf824e 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -464,58 +464,91 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) {
     dst[i] = x[i] / (1.0f + expf(-x[i]));
 }
 
+static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
+        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
+    }
+    return a;
+}
+
+template <int block_size>
 static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
     const int tid = threadIdx.x;
 
     const float eps = 1e-5f;
 
-    float mean = 0.0f;
-    float var = 0.0f;
+    float2 mean_var = make_float2(0.f, 0.f);
 
-    for (int col = tid; col < ncols; col += WARP_SIZE) {
+    for (int col = tid; col < ncols; col += block_size) {
         const float xi = x[row*ncols + col];
-        mean += xi;
-        var += xi * xi;
+        mean_var.x += xi;
+        mean_var.y += xi * xi;
     }
 
     // sum up partial sums
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        mean += __shfl_xor_sync(0xffffffff, mean, mask, 32);
-        var += __shfl_xor_sync(0xffffffff, var, mask, 32);
+    mean_var = warp_reduce_sum(mean_var);
+    if (block_size > WARP_SIZE) {
+        __shared__ float2 s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        __syncthreads();
+        mean_var = s_sum[lane_id];
+        mean_var = warp_reduce_sum(mean_var);
     }
 
-    mean /= ncols;
-    var = var / ncols - mean * mean;
-    const float inv_var = rsqrtf(var + eps);
+    const float mean = mean_var.x / ncols;
+    const float var = mean_var.y / ncols - mean * mean;
+    const float inv_std = rsqrtf(var + eps);
 
-    for (int col = tid; col < ncols; col += WARP_SIZE) {
-        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var;
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
     }
 }
 
+static __device__ __forceinline__ float warp_reduce_sum(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
+    }
+    return x;
+}
+
+template <int block_size>
 static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
     const int tid = threadIdx.x;
 
     float tmp = 0.0f; // partial sum for thread in warp
 
-    for (int col = tid; col < ncols; col += WARP_SIZE) {
+    for (int col = tid; col < ncols; col += block_size) {
         const float xi = x[row*ncols + col];
         tmp += xi * xi;
     }
 
     // sum up partial sums
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
     }
 
     const float mean = tmp / ncols;
     const float scale = rsqrtf(mean + eps);
 
-    for (int col = tid; col < ncols; col += WARP_SIZE) {
+    for (int col = tid; col < ncols; col += block_size) {
         dst[row*ncols + col] = scale * x[row*ncols + col];
     }
 }
@@ -4203,14 +4236,24 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_
 
 static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % WARP_SIZE == 0);
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
+    }
 }
 
 static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
     GGML_ASSERT(ncols % WARP_SIZE == 0);
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    rms_norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    }
 }
 
 static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
diff --git a/ggml-metal.m b/ggml-metal.m
index 88e7e1356..d0d23442e 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -76,6 +76,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(rms_norm);
     GGML_METAL_DECL_KERNEL(norm);
     GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
+    GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_1row);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32);
@@ -219,6 +220,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(rms_norm);
         GGML_METAL_ADD_KERNEL(norm);
         GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
+        GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_1row);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32);
@@ -284,6 +286,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(rms_norm);
     GGML_METAL_DEL_KERNEL(norm);
     GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_1row);
     GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
     GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
@@ -868,7 +871,11 @@ void ggml_metal_graph_compute(
                                         {
                                             nth0 = 32;
                                             nth1 = 1;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
+                                            if (ne11 * ne12 < 4) {
+                                                [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row];
+                                            } else {
+                                                [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
+                                            }
                                         } break;
                                     case GGML_TYPE_Q4_0:
                                         {
@@ -920,8 +927,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 2;
-                                            nth1 = 32;
+                                            nth0 = 4; //1;
+                                            nth1 = 8; //32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32];
                                         } break;
                                     case GGML_TYPE_Q5_K:
@@ -969,9 +976,12 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&gqa  length:sizeof(gqa)  atIndex:17];
 
                                 if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 ||
-                                    src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
+                                    src0t == GGML_TYPE_Q2_K) {// || src0t == GGML_TYPE_Q4_K) {
                                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
+                                else if (src0t == GGML_TYPE_Q4_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
                                 else if (src0t == GGML_TYPE_Q3_K) {
 #ifdef GGML_QKK_64
                                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -985,8 +995,8 @@ void ggml_metal_graph_compute(
                                 else if (src0t == GGML_TYPE_Q6_K) {
                                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 } else {
-                                    [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    int64_t ny = (ne11 + 3)/4;
+                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
                             }
                         } break;
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 8cdf0b9d2..119fcbeb6 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -133,19 +133,24 @@ kernel void kernel_soft_max(
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-    // broadcast
-    if (tpitg[0] == 0) {
-        buf[0] = buf[0];
-    }
+    //// broadcast - not needed. There is a threadgroup barrier above in the last iteration of
+    //               the loop, and when that is done, buf[0] has the correct (synchronized) value
+    //if (tpitg[0] == 0) {
+    //    buf[0] = buf[0];
+    //}
 
-    threadgroup_barrier(mem_flags::mem_threadgroup);
+    //threadgroup_barrier(mem_flags::mem_threadgroup);
 
     const float max = buf[0];
 
     // parallel sum
     buf[tpitg[0]] = 0.0f;
     for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
-        buf[tpitg[0]] += exp(psrc0[i00] - max);
+        const float exp_psrc0 = exp(psrc0[i00] - max);
+        buf[tpitg[0]] += exp_psrc0;
+        // Remember the result of exp here. exp is expensive, so we really do not
+        // whish to compute it twice.
+        pdst[i00] = exp_psrc0;
     }
 
     // reduce
@@ -157,17 +162,18 @@ kernel void kernel_soft_max(
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-    // broadcast
-    if (tpitg[0] == 0) {
-        buf[0] = buf[0];
-    }
+    // broadcast - not needed, see above
+    //// broadcast
+    //if (tpitg[0] == 0) {
+    //    buf[0] = buf[0];
+    //}
 
-    threadgroup_barrier(mem_flags::mem_threadgroup);
+    //threadgroup_barrier(mem_flags::mem_threadgroup);
 
     const float sum = buf[0];
 
     for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
-        pdst[i00] = exp(psrc0[i00] - max) / sum;
+        pdst[i00] /= sum;
     }
 }
 
@@ -214,25 +220,27 @@ kernel void kernel_norm(
         }
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
-    // broadcast
-    if (tpitg == 0) {
-        sum[0] /= ne00;
-    }
-    threadgroup_barrier(mem_flags::mem_threadgroup);
+    //// broadcast
+    //if (tpitg == 0) {
+    //    sum[0] /= ne00;
+    //}
+    //threadgroup_barrier(mem_flags::mem_threadgroup);
     const float mean  = sum[0];
 
-    // recenter
+    // recenter and VARIANCE
     device float * y = dst + tgpig*ne00;
-    for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        y[i00] = x[i00] - mean;
-    }
-
-    // VARIANCE
-    // parallel sum
     sum[tpitg] = 0.0f;
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
+        y[i00] = x[i00] - mean;
         sum[tpitg] += y[i00] * y[i00];
     }
+
+    //// VARIANCE
+    //// parallel sum
+    //sum[tpitg] = 0.0f;
+    //for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
+    //    sum[tpitg] += y[i00] * y[i00];
+    //}
     // reduce
     threadgroup_barrier(mem_flags::mem_threadgroup);
     for (uint i = ntg/2; i > 0; i /= 2) {
@@ -241,11 +249,11 @@ kernel void kernel_norm(
         }
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
-    // broadcast
-    if (tpitg == 0) {
-        sum[0] /= ne00;
-    }
-    threadgroup_barrier(mem_flags::mem_threadgroup);
+    //// broadcast
+    //if (tpitg == 0) {
+    //    sum[0] /= ne00;
+    //}
+    //threadgroup_barrier(mem_flags::mem_threadgroup);
     const float variance = sum[0];
 
     const float scale = 1.0f/sqrt(variance + eps);
@@ -435,6 +443,8 @@ kernel void kernel_mul_mat_q4_1_f32(
      mul_vec_q_n_f32<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg);
 }
 
+#define NB_Q8_0 8
+
 kernel void kernel_mul_mat_q8_0_f32(
         device const  void * src0,
         device const float * src1,
@@ -463,30 +473,30 @@ kernel void kernel_mul_mat_q8_0_f32(
     device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0;
     device const float      * y = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
 
-    float yl[16];
+    float yl[NB_Q8_0];
     float sumf[nr]={0.f};
 
-    const int ix = tiisg/2;
-    const int il = tiisg%2;
+    const int ix = tiisg/4;
+    const int il = tiisg%4;
 
-    device const float * yb = y + ix * QK8_0 + 16*il;
+    device const float * yb = y + ix * QK8_0 + NB_Q8_0*il;
 
-    // each thread in a SIMD group deals with half a block.
-    for (int ib = ix; ib < nb; ib += nw/2) {
-        for (int i = 0; i < 16; ++i) {
+    // each thread in a SIMD group deals with NB_Q8_0 quants at a time
+    for (int ib = ix; ib < nb; ib += nw/4) {
+        for (int i = 0; i < NB_Q8_0; ++i) {
             yl[i] = yb[i];
         }
 
         for (int row = 0; row < nr; row++) {
-            device const int8_t * qs = x[ib+row*nb].qs + 16*il;
+            device const int8_t * qs = x[ib+row*nb].qs + NB_Q8_0*il;
             float sumq = 0.f;
-            for (int iq = 0; iq < 16; ++iq) {
+            for (int iq = 0; iq < NB_Q8_0; ++iq) {
                 sumq += qs[iq] * yl[iq];
             }
             sumf[row] += sumq*x[ib+row*nb].d;
         }
 
-        yb += QK8_0 * 16;
+        yb += NB_Q8_0 * nw;
     }
 
     for (int row = 0; row < nr; ++row) {
@@ -497,6 +507,60 @@ kernel void kernel_mul_mat_q8_0_f32(
     }
 }
 
+kernel void kernel_mul_mat_f16_f32_1row(
+        device const  char * src0,
+        device const  char * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]]) {
+
+    const int64_t r0 = tgpig.x;
+    const int64_t r1 = tgpig.y;
+    const int64_t im = tgpig.z;
+
+    device const half  * x = (device const half  *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
+    device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
+
+    float sumf = 0;
+    if (ne00 < 128) {
+        for (int i = tiisg; i < ne00; i += 32) {
+            sumf += (float) x[i] * (float) y[i];
+        }
+        float all_sum = simd_sum(sumf);
+        if (tiisg == 0) {
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    } else {
+        device const half4  * x4 = (device const half4  *) x;
+        device const float4 * y4 = (device const float4 *) y;
+        for (int i = tiisg; i < ne00/4; i += 32) {
+            for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k];
+        }
+        float all_sum = simd_sum(sumf);
+        if (tiisg == 0) {
+            for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    }
+
+}
+
+#define N_F16_F32 4
+
 kernel void kernel_mul_mat_f16_f32(
         device const  char * src0,
         device const  char * src1,
@@ -515,55 +579,58 @@ kernel void kernel_mul_mat_f16_f32(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        threadgroup float  * sum [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3  tpig[[thread_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3  tptg[[threads_per_threadgroup]]) {
+        uint tiisg[[thread_index_in_simdgroup]]) {
 
     const int64_t r0 = tgpig.x;
-    const int64_t r1 = tgpig.y;
+    const int64_t rb = tgpig.y*N_F16_F32;
     const int64_t im = tgpig.z;
 
-    device const half  * x = (device const half  *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
-    device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
+    device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
 
-    uint ith = tpitg.x;
-    uint nth = tptg.x;
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F16_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
 
-    sum[ith] = 0.0f;
+            device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
 
-    for (int i = ith; i < ne00; i += nth) {
-        sum[ith] += (float) x[i] * (float) y[i];
+            float sumf = 0;
+            for (int i = tiisg; i < ne00; i += 32) {
+                sumf += (float) x[i] * (float) y[i];
+            }
+
+            float all_sum = simd_sum(sumf);
+            if (tiisg == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        device const half4 * x4 = (device const half4 *)x;
+        for (int row = 0; row < N_F16_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            device const float  * y  = (device const float  *) (src1 + r1*nb11 + im*nb12);
+            device const float4 * y4 = (device const float4 *) y;
+
+            float sumf = 0;
+            for (int i = tiisg; i < ne00/4; i += 32) {
+                for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+            }
+
+            float all_sum = simd_sum(sumf);
+            if (tiisg == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
     }
 
-    // accumulate the sum from all threads in the threadgroup
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-    if (ith%4 == 0) {
-        for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
-    }
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-    if (ith%16 == 0) {
-        for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
-    }
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-    if (ith == 0) {
-        for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
-        dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0];
-    }
-
-    // Original implementation. Left behind commented out for now
-    //threadgroup_barrier(mem_flags::mem_threadgroup);
-    //for (uint i = tptg.x/2; i > 0; i /= 2) {
-    //    if (tpitg.x < i) {
-    //        sum[tpitg.x] += sum[tpitg.x + i];
-    //    }
-    //    threadgroup_barrier(mem_flags::mem_threadgroup);
-    //}
-    //
-    //if (tpitg.x == 0) {
-    //    dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0];
-    //}
 }
 
 kernel void kernel_alibi_f32(
@@ -1262,7 +1329,8 @@ kernel void kernel_mul_mat_q4_K_f32(
     const int r0 = tgpig.x;
     const int r1 = tgpig.y;
     const int r2 = tgpig.z;
-    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int first_row = r0 * N_DST;
     const int ib_row = first_row * nb;
     const uint offset0 = r2/gqa*(nb*ne0);
     device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0;
diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp
index eb214a836..777048d01 100644
--- a/ggml-opencl.cpp
+++ b/ggml-opencl.cpp
@@ -1334,7 +1334,7 @@ void ggml_cl_free_data(const struct ggml_tensor* tensor) {
         return;
     }
 
-    cl_mem mem = (cl_mem)tensor->data;
+    cl_mem mem = (cl_mem)tensor->extra;
     clReleaseMemObject(mem);
 }
 
@@ -1393,7 +1393,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
     size_t d_size;
 
     cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size); // src0
-    cl_mem d_Y = (cl_mem) src1->data; // src1 is already on device, broadcasted.
+    cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
     cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size); // dst
 
 
@@ -1491,9 +1491,9 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
     size_t d_size;
     cl_mem d_X;
     if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
-        d_X = (cl_mem) src0->data;
+        d_X = (cl_mem) src0->extra;
     } else {
-        d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
+        d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
     }
     cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
     cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
@@ -1567,7 +1567,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
     size_t d_size;
     cl_mem d_X;
     if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
-        d_X = (cl_mem) src0->data;
+        d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
     }
@@ -1697,7 +1697,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
                 events.emplace_back();
                 CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
             } else if (src0->backend == GGML_BACKEND_GPU) {
-                d_Q = (cl_mem) src0->data;
+                d_Q = (cl_mem) src0->extra;
             } else {
                 GGML_ASSERT(false);
             }
@@ -1860,6 +1860,6 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
 
     CL_CHECK(clFinish(queue));
 
-    tensor->data = dst;
+    tensor->extra = dst;
     GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
 }
diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py
index b1bc4205b..d377cd56d 100644
--- a/gguf-py/gguf/gguf.py
+++ b/gguf-py/gguf/gguf.py
@@ -801,7 +801,7 @@ class SpecialVocab:
             else:
                 continue
             for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content):
-                if isinstance(maybe_token_id, int):
+                if isinstance(maybe_token_id, int) and maybe_token_id >= 0:
                     self.special_token_ids[typ] = maybe_token_id
                 break
         return True
@@ -814,7 +814,7 @@ class SpecialVocab:
             config = json.load(f)
         for typ in self.special_token_types:
             maybe_token_id = config.get(f'{typ}_token_id')
-            if isinstance(maybe_token_id, int):
+            if isinstance(maybe_token_id, int) and maybe_token_id >= 0:
                 self.special_token_ids[typ] = maybe_token_id
         return True
 
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 26f792b14..8da60de1b 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.3.1"
+version = "0.3.2"
 description = "Write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
diff --git a/llama.cpp b/llama.cpp
index 56e605df5..b9485df0c 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3366,9 +3366,15 @@ struct llm_tokenizer_bpe {
                         std::string byte_str(1, *j);
                         auto token_multibyte = vocab.token_to_id.find(byte_str);
                         if (token_multibyte == vocab.token_to_id.end()) {
-                            fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str());
+                            try {
+                                llama_token token_byte = llama_byte_to_token(vocab, *j);
+                                output.push_back(token_byte);
+                            } catch (const std::out_of_range & err) {
+                                fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str());
+                            }
+                        } else {
+                            output.push_back((*token_multibyte).second);
                         }
-                        output.push_back((*token_multibyte).second);
                     }
                 } else {
                     output.push_back((*token).second);