diff --git a/common/chat-template.hpp b/common/chat-template.hpp
index 0e88fb361..882ba41bd 100644
--- a/common/chat-template.hpp
+++ b/common/chat-template.hpp
@@ -249,16 +249,30 @@ class chat_template {
                     inputs.add_generation_prompt = false;
                     full = apply(inputs);
                 }
-
-                if (full.find(prefix) != 0) {
-                    if (prefix.rfind(eos_token_) == prefix.size() - eos_token_.size()) {
-                        prefix = prefix.substr(0, prefix.size() - eos_token_.size());
+                auto eos_pos_last = full.rfind(eos_token_);
+                if (eos_pos_last == prefix.size() - eos_token_.size() ||
+                      (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) {
+                    full = full.substr(0, eos_pos_last);
+                }
+                size_t common_prefix_length = 0;
+                for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) {
+                    if (prefix[i] != full[i]) {
+                        break;
                     }
+                    if (prefix[i] == '<') {
+                        // DeepSeek R1's template (as of 20250209) adds a trailing <think> if add_generation_prompt,
+                        // but it removes thinking tags for past messages.
+                        // The prefix and full strings diverge at <think> vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <.
+                        continue;
+                    }
+                    common_prefix_length = i + 1;
                 }
-                if (full.find(prefix) != 0) {
+                auto example = full.substr(common_prefix_length);
+                if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) {
                     fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
+                } else {
+                    tool_call_example_ = example;
                 }
-                tool_call_example_ = full.substr(prefix.size());
             }
         } catch (const std::exception & e) {
             fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
@@ -363,7 +377,7 @@ class chat_template {
             if (polyfill_tools) {
                 adjusted_messages = add_system(inputs.messages,
                     "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
-                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_));
+                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n"));
             } else {
                 adjusted_messages = inputs.messages;
             }
diff --git a/common/common.h b/common/common.h
index 4eac8db5a..b208d0c7e 100644
--- a/common/common.h
+++ b/common/common.h
@@ -383,7 +383,7 @@ struct common_params {
     int32_t i_pos  = -1;  // position of the passkey in the junk text
 
     // imatrix params
-    std::string out_file = "imatrix.gguf"; // save the resulting imatrix to this file
+    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
 
     int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
     int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
diff --git a/common/minja.hpp b/common/minja.hpp
index c304b5c66..c58dd66e0 100644
--- a/common/minja.hpp
+++ b/common/minja.hpp
@@ -1385,6 +1385,13 @@ static std::string strip(const std::string & s) {
   return s.substr(start, end - start + 1);
 }
 
+static std::string capitalize(const std::string & s) {
+  if (s.empty()) return s;
+  auto result = s;
+  result[0] = std::toupper(result[0]);
+  return result;
+}
+
 static std::string html_escape(const std::string & s) {
   std::string result;
   result.reserve(s.size());
@@ -1462,6 +1469,9 @@ public:
           if (method->get_name() == "strip") {
             vargs.expectArgs("strip method", {0, 0}, {0, 0});
             return Value(strip(str));
+          } else if (method->get_name() == "capitalize") {
+            vargs.expectArgs("capitalize method", {0, 0}, {0, 0});
+            return Value(capitalize(str));
           } else if (method->get_name() == "endswith") {
             vargs.expectArgs("endswith method", {1, 1}, {0, 0});
             auto suffix = vargs.args[0].get<std::string>();
@@ -1792,7 +1802,7 @@ private:
         auto left = parseStringConcat();
         if (!left) throw std::runtime_error("Expected left side of 'logical compare' expression");
 
-        static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not[\r\n\s]+in\b)");
+        static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not\s+in\b)");
         static std::regex not_tok(R"(not\b)");
         std::string op_str;
         while (!(op_str = consumeToken(compare_tok)).empty()) {
@@ -2171,7 +2181,7 @@ private:
     using TemplateTokenIterator = TemplateTokenVector::const_iterator;
 
     std::vector<std::string> parseVarNames() {
-      static std::regex varnames_regex(R"(((?:\w+)(?:[\r\n\s]*,[\r\n\s]*(?:\w+))*)[\r\n\s]*)");
+      static std::regex varnames_regex(R"(((?:\w+)(?:\s*,\s*(?:\w+))*)\s*)");
 
       std::vector<std::string> group;
       if ((group = consumeTokenGroups(varnames_regex)).empty()) throw std::runtime_error("Expected variable names");
@@ -2194,13 +2204,13 @@ private:
     }
 
     TemplateTokenVector tokenize() {
-      static std::regex comment_tok(R"(\{#([-~]?)([\s\S\r\n]*?)([-~]?)#\})");
+      static std::regex comment_tok(R"(\{#([-~]?)([\s\S]*?)([-~]?)#\})");
       static std::regex expr_open_regex(R"(\{\{([-~])?)");
-      static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
+      static std::regex block_open_regex(R"(^\{%([-~])?\s*)");
       static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)");
       static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)");
-      static std::regex expr_close_regex(R"([\s\n\r]*([-~])?\}\})");
-      static std::regex block_close_regex(R"([\s\n\r]*([-~])?%\})");
+      static std::regex expr_close_regex(R"(\s*([-~])?\}\})");
+      static std::regex block_close_regex(R"(\s*([-~])?%\})");
 
       TemplateTokenVector tokens;
       std::vector<std::string> group;
@@ -2284,7 +2294,7 @@ private:
               auto post_space = parseBlockClose();
               tokens.push_back(std::make_unique<EndGenerationTemplateToken>(location, pre_space, post_space));
             } else if (keyword == "set") {
-              static std::regex namespaced_var_regex(R"((\w+)[\s\n\r]*\.[\s\n\r]*(\w+))");
+              static std::regex namespaced_var_regex(R"((\w+)\s*\.\s*(\w+))");
 
               std::string ns;
               std::vector<std::string> var_names;
@@ -2336,6 +2346,11 @@ private:
               throw std::runtime_error("Unexpected block: " + keyword);
             }
           } else if (std::regex_search(it, end, match, non_text_open_regex)) {
+            if (!match.position()) {
+                if (match[0] != "{#")
+                    throw std::runtime_error("Internal error: Expected a comment");
+                throw std::runtime_error("Missing end of comment tag");
+            }
             auto text_end = it + match.position();
             text = std::string(it, text_end);
             it = text_end;
@@ -2400,7 +2415,7 @@ private:
 
               auto text = text_token->text;
               if (post_space == SpaceHandling::Strip) {
-                static std::regex trailing_space_regex(R"((\s|\r|\n)+$)");
+                static std::regex trailing_space_regex(R"(\s+$)");
                 text = std::regex_replace(text, trailing_space_regex, "");
               } else if (options.lstrip_blocks && it != end) {
                 auto i = text.size();
@@ -2410,7 +2425,7 @@ private:
                 }
               }
               if (pre_space == SpaceHandling::Strip) {
-                static std::regex leading_space_regex(R"(^(\s|\r|\n)+)");
+                static std::regex leading_space_regex(R"(^\s+)");
                 text = std::regex_replace(text, leading_space_regex, "");
               } else if (options.trim_blocks && (it - 1) != begin && !dynamic_cast<ExpressionTemplateToken*>((*(it - 2)).get())) {
                 if (text.length() > 0 && text[0] == '\n') {
diff --git a/convert_legacy_imatrix_to_gguf.py b/convert_legacy_imatrix_to_gguf.py
deleted file mode 100644
index bd72655bf..000000000
--- a/convert_legacy_imatrix_to_gguf.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python3
-
-from __future__ import annotations
-
-import os
-import sys
-import logging
-import argparse
-
-from typing import Any
-from pathlib import Path
-from dataclasses import dataclass
-
-import numpy as np
-import numpy.typing as npt
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
-import gguf
-
-
-logger = logging.getLogger("imatrix-to-gguf")
-
-
-class IMatrixWriter(gguf.GGUFWriter):
-    def add_architecture(self) -> None:
-        # no arch is stored in imatrix files
-        pass
-
-
-@dataclass
-class IMatrixEntry:
-    values: np.ndarray[Any, np.dtype[np.float32]]
-    counts: np.ndarray[Any, np.dtype[np.float32]]
-
-
-class IMatrixReader:
-    chunk_size: int = 512  # guess
-    offset: int = 0
-    data: np.ndarray[Any, np.dtype[np.uint8]]
-    n_enties: int
-    entries: dict[str, IMatrixEntry]
-    chunk_count: int
-    dataset: str
-
-    def _get(self, dtype: npt.DTypeLike, count: int = 1) -> npt.NDArray[Any]:
-        count = int(count)
-        itemsize = int(np.empty([], dtype=dtype).itemsize)
-        offset = self.offset
-        self.offset = offset + itemsize * count
-        return self.data[offset:self.offset].view(dtype=dtype)[:count]
-
-    def __init__(self, imatrix: Path):
-        self.offset = 0
-        self.entries = {}
-        self.data = np.memmap(imatrix)
-        n_entries = self._get(np.int32).item()
-        assert n_entries >= 0
-        for _ in range(n_entries):
-            len = self._get(np.int32).item()
-            name = self._get(np.uint8, len).tobytes().decode("utf-8")
-            ncall = self._get(np.int32).item()
-            nval = self._get(np.int32).item()
-            data = self._get(np.float32, nval)
-            assert name not in self.entries, f"duplicated name: {name!r}"
-
-            self.entries[name] = IMatrixEntry(data * np.float32(self.chunk_size), np.array([ncall * self.chunk_size], dtype=np.float32))
-
-        self.chunk_count = self._get(np.int32).item()
-        dataset_len = self._get(np.int32).item()
-        self.dataset = self._get(np.uint8, dataset_len).tobytes().decode("utf-8")
-
-    def to_writer(self, outfile: Path) -> IMatrixWriter:
-        writer = IMatrixWriter(path=outfile, arch="")
-
-        writer.add_type(gguf.GGUFType.IMATRIX)
-        writer.add_key_value(gguf.Keys.IMatrix.CHUNK_COUNT, self.chunk_count, gguf.GGUFValueType.UINT32)
-        writer.add_key_value(gguf.Keys.IMatrix.CHUNK_SIZE, self.chunk_size, gguf.GGUFValueType.UINT32)
-        writer.add_key_value(gguf.Keys.IMatrix.DATASET, self.dataset, gguf.GGUFValueType.STRING)
-
-        for name, entry in self.entries.items():
-            writer.add_tensor(name + ".sums", entry.values)
-            writer.add_tensor(name + ".counts", entry.counts)
-
-        return writer
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Convert an old imatrix.dat file to a GGUF compatible file")
-    parser.add_argument(
-        "--outfile", type=Path,
-        help="path to write to; default: based on input.",
-    )
-    parser.add_argument(
-        "--verbose", action="store_true",
-        help="increase output verbosity",
-    )
-    parser.add_argument(
-        "imatrix", type=Path,
-        help="path to an imatrix file",
-    )
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-
-    if args.outfile is None:
-        input_file: Path = args.imatrix
-        if input_file.suffix != ".gguf":
-            args.outfile = input_file.with_suffix(".gguf")
-            if args.outfile.exists():
-                logger.error(f"default file exists, specify with --outfile to overwrite: {args.outfile}")
-                exit(1)
-
-    writer = IMatrixReader(args.imatrix).to_writer(args.outfile)
-
-    writer.write_header_to_file(args.outfile)
-    writer.write_kv_data_to_file()
-    writer.write_tensors_to_file()
diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp
index 99056e74c..b5f3feb9f 100644
--- a/examples/imatrix/imatrix.cpp
+++ b/examples/imatrix/imatrix.cpp
@@ -2,7 +2,6 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
-#include "gguf.h"
 
 #include <cmath>
 #include <cstdio>
@@ -11,8 +10,8 @@
 #include <thread>
 #include <mutex>
 #include <vector>
+#include <fstream>
 #include <unordered_map>
-#include <map>
 #include <algorithm>
 
 #if defined(_MSC_VER)
@@ -22,27 +21,16 @@
 static void print_usage(int, char ** argv) {
     LOG("\nexample usage:\n");
     LOG("\n    %s \\\n"
-            "       -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] \\\n"
+            "       -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n"
             "       [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
-            "       [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
+            "       [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
     LOG("\n");
 }
 
-static bool str_remove_suffix(std::string & str, const std::string & suffix) {
-    bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
-    if (has_suffix) {
-        str = str.substr(0, str.size() - suffix.size());
-    }
-    return has_suffix;
-}
-
-static const char * const LLM_KV_IMATRIX_DATASET     = "imatrix.dataset";
-static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
-static const char * const LLM_KV_IMATRIX_CHUNK_SIZE  = "imatrix.chunk_size";
-
 struct Stats {
-    std::vector<float>   values;
-    std::vector<int64_t> counts;
+    std::vector<float> values;
+    std::vector<int> counts;
+    int ncall = 0;
 };
 
 class IMatrixCollector {
@@ -50,13 +38,13 @@ public:
     IMatrixCollector() = default;
     void set_params(common_params params) { m_params = std::move(params); }
     bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
-    void save_imatrix(int32_t n_chunk = -1) const;
-    bool load_imatrix(const char * file_name);
+    void save_imatrix(int ncall = -1) const;
+    bool load_imatrix(const char * fname);
 private:
     std::unordered_map<std::string, Stats> m_stats;
     common_params                          m_params;
     std::mutex                             m_mutex;
-    int32_t                                m_last_chunk = 0;
+    int                                    m_last_call = 0;
     std::vector<float>                     m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
 };
@@ -130,23 +118,17 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 
         auto & e = m_stats[wname];
 
-        if (e.counts.size() == 1 && n_as > 1) {
-            // broadcast, when loading an old imatrix
-            e.counts.resize(n_as, e.counts[0]);
-        }
+        ++e.ncall;
+
         if (e.values.empty()) {
             e.values.resize(src1->ne[0]*n_as, 0);
-            e.counts.resize(n_as, 0);
+            e.counts.resize(src1->ne[0]*n_as, 0);
         }
         else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
             LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
             exit(1); //GGML_ABORT("fatal error");
         }
-        else if (e.counts.size() != (size_t)n_as) {
-            LOG_ERR("Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)n_as);
-            exit(1); //GGML_ABORT("fatal error");
-        }
-        LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
+        LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
         // loop over all possible experts, regardless if they are used or not in the batch
         for (int ex = 0; ex < n_as; ++ex) {
             size_t e_start = ex*src1->ne[0];
@@ -163,26 +145,24 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
                     const int64_t i12 = row;
                     const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
 
-                    e.counts[ex]++;
-
                     for (int j = 0; j < (int)src1->ne[0]; ++j) {
-                        e.values[e_start + j] = std::fma(x[j], x[j], e.values[e_start + j]);
-                        if (!std::isfinite((float)e.values[e_start + j])) {
-                            LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
+                        e.values[e_start + j] += x[j]*x[j];
+                        e.counts[e_start + j]++;
+                        if (!std::isfinite(e.values[e_start + j])) {
+                            LOG("\n");
+                            LOG_ERR("%f detected in %s\n", e.values[e_start + j], wname.c_str());
                             exit(1);
                         }
                     }
                 }
             }
-            const int32_t n_chunk = e.counts[ex] / (m_params.n_ctx / m_params.n_parallel);
-            if (n_chunk > m_last_chunk) {
-                const int32_t chunk_step = n_chunk - m_last_chunk;
-                m_last_chunk = n_chunk;
-                if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
+            if (e.ncall > m_last_call) {
+                m_last_call = e.ncall;
+                if (m_last_call % m_params.n_out_freq == 0) {
                     save_imatrix();
                 }
-                if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
-                    save_imatrix(m_last_chunk);
+                if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
+                    save_imatrix(m_last_call);
                 }
             }
         }
@@ -190,38 +170,32 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         auto & e = m_stats[wname];
         if (e.values.empty()) {
             e.values.resize(src1->ne[0], 0);
-            e.counts.resize(1, 0);
+            e.counts.resize(src1->ne[0], 0);
         }
         else if (e.values.size() != (size_t)src1->ne[0]) {
             LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
             exit(1); //GGML_ABORT("fatal error");
         }
-        else if (e.counts.size() != 1) {
-            LOG_ERR("Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), 1);
-            exit(1); //GGML_ABORT("fatal error");
-        }
-        LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
-        // TODO: higher dimensions
+        ++e.ncall;
+        LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
         for (int row = 0; row < (int)src1->ne[1]; ++row) {
             const float * x = data + row * src1->ne[0];
-            e.counts[0]++;
             for (int j = 0; j < (int)src1->ne[0]; ++j) {
-                e.values[j] = std::fma(x[j], x[j], e.values[j]);
-                if (!std::isfinite((float)e.values[j])) {
-                    LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str());
+                e.values[j] += x[j]*x[j];
+                e.counts[j]++;
+                if (!std::isfinite(e.values[j])) {
+                    LOG_ERR("%f detected in %s\n", e.values[j], wname.c_str());
                     exit(1);
                 }
             }
         }
-        const int32_t n_chunk = e.counts[0] / (m_params.n_ctx / m_params.n_parallel);
-        if (n_chunk > m_last_chunk) {
-            const int32_t chunk_step = n_chunk - m_last_chunk;
-            m_last_chunk = n_chunk;
-            if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
+        if (e.ncall > m_last_call) {
+            m_last_call = e.ncall;
+            if (m_last_call % m_params.n_out_freq == 0) {
                 save_imatrix();
             }
-            if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
-                save_imatrix(m_last_chunk);
+            if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
+                save_imatrix(m_last_call);
             }
         }
     }
@@ -229,22 +203,22 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     return true;
 }
 
-void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
+void IMatrixCollector::save_imatrix(int ncall) const {
     auto fname = m_params.out_file;
     if (fname.empty()) {
-        fname = "imatrix.gguf";
+        fname = "imatrix.dat";
     }
 
-    if (n_chunk > 0) {
+    if (ncall > 0) {
         fname += ".at_";
-        fname += std::to_string(n_chunk);
+        fname += std::to_string(ncall);
     }
 
     // avoid writing imatrix entries that do not have full data
     // this can happen with MoE models where some of the experts end up not being exercised by the provided training data
 
+    int n_entries = 0;
     std::vector<std::string> to_store;
-    size_t data_size = 0;
 
     bool is_first = true; // for printing
     for (const auto & kv : m_stats) {
@@ -276,157 +250,101 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
             continue;
         }
 
+        n_entries++;
         to_store.push_back(kv.first);
-        data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN);
-        data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
     }
 
     if (to_store.size() < m_stats.size()) {
         LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
     }
 
-    // deterministic tensor name order
-    std::sort(to_store.begin(), to_store.end());
-
-    struct ggml_init_params params = {
-        /* .mem_size   = */ data_size,
-        /* .mem_buffer = */ NULL,
-        /* .no_alloc   = */ false,
-    };
-    struct ggml_context * ctx = ggml_init(params);
-    struct gguf_context * ctx_gguf = gguf_init_empty();
-
-    gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
-    // Write the input filename to later on specify it in quantize
-    gguf_set_val_str(ctx_gguf, LLM_KV_IMATRIX_DATASET, m_params.prompt_file.c_str());
-    // Write the number of chunks the matrix was computed with
-    gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk);
-    gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel);
-
+    std::ofstream out(fname, std::ios::binary);
+    out.write((const char *) &n_entries, sizeof(n_entries));
     for (const auto & name : to_store) {
         const auto & stat = m_stats.at(name);
-        const int32_t nval = (int32_t) stat.values.size();
-        const int32_t nmat = (int32_t) stat.counts.size();
+        int len = name.size();
+        out.write((const char *) &len, sizeof(len));
+        out.write(name.c_str(), len);
+        out.write((const char *) &stat.ncall, sizeof(stat.ncall));
+        int nval = stat.values.size();
+        out.write((const char *) &nval, sizeof(nval));
         if (nval > 0) {
-            struct ggml_tensor * sums = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat);
-            struct ggml_tensor * counts = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat);
-            ggml_format_name(sums, "%s.sums", name.c_str());
-            ggml_format_name(counts, "%s.counts", name.c_str());
-
-            for (int32_t j = 0; j < nval; ++j) {
-                ((float *) sums->data)[j] = (float) stat.values[j];
+            std::vector<float> tmp(nval);
+            for (int i = 0; i < nval; i++) {
+                tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
             }
-            for (int32_t j = 0; j < nmat; ++j) {
-                ((float *) counts->data)[j] = (float) stat.counts[j];
-            }
-
-            gguf_add_tensor(ctx_gguf, sums);
-            gguf_add_tensor(ctx_gguf, counts);
+            out.write((const char*)tmp.data(), nval*sizeof(float));
         }
     }
 
-    gguf_write_to_file(ctx_gguf, fname.c_str(), false);
+    // Write the number of call the matrix was computed with
+    out.write((const char *) &m_last_call, sizeof(m_last_call));
+
+    // Write the input filename at the end of the file to later on specify it in quantize
+    {
+        int len = m_params.prompt_file.size();
+        out.write((const char *) &len, sizeof(len));
+        out.write(m_params.prompt_file.c_str(), len);
+    }
 
     LOGV(1, "\n");
-    LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
-
-    gguf_free(ctx_gguf);
-    ggml_free(ctx);
+    LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
 }
 
-bool IMatrixCollector::load_imatrix(const char * file_name) {
-    struct ggml_context * ctx = nullptr;
-    struct gguf_init_params meta_gguf_params = {
-        /* .no_alloc = */ false, // the data is needed
-        /* .ctx      = */ &ctx,
-    };
-    struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params);
-    if (!ctx_gguf) {
+bool IMatrixCollector::load_imatrix(const char * fname) {
+    std::ifstream in(fname, std::ios::binary);
+    if (!in) {
+        LOG_ERR("%s: failed to open %s\n",__func__, fname);
         return false;
     }
-    const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
-    if (n_entries < 1) {
-        LOG_ERR("%s: no data in file %s\n", __func__, file_name);
-        gguf_free(ctx_gguf);
-        ggml_free(ctx);
+    int n_entries;
+    in.read((char*)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        LOG_ERR("%s: no data in file %s\n", __func__, fname);
         return false;
     }
-
-    const std::string sums_suffix{".sums"};
-    const std::string counts_suffix{".counts"};
-
-    // Could re-use m_stats instead, but this allows
-    // checking for completeness of *each* loaded imatrix file
-    // and also makes it easier to re-use a similar implementation in quantize.cpp
-    // Using an ordered map to get a deterministic iteration order.
-    std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
-
-    for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
-        std::string name = cur->name;
-
-        if (name.empty()) { continue; }
-
-        if (str_remove_suffix(name, sums_suffix)) {
-            // sums
-            sums_counts_for[name].first = cur;
-        } else if (str_remove_suffix(name, counts_suffix)) {
-            // counts
-            sums_counts_for[name].second = cur;
-        } else {
-            LOG_ERR("%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
-            gguf_free(ctx_gguf);
-            ggml_free(ctx);
+    for (int i = 0; i < n_entries; ++i) {
+        int len; in.read((char *)&len, sizeof(len));
+        std::vector<char> name_as_vec(len+1);
+        in.read((char *)name_as_vec.data(), len);
+        if (in.fail()) {
+            LOG_ERR("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
             return false;
         }
-    }
-
-    for (const auto & sc : sums_counts_for) {
-        const        std::string & name   = sc.first;
-        const struct ggml_tensor * sums   = sc.second.first;
-        const struct ggml_tensor * counts = sc.second.second;
-
-        if (!sums || !counts) {
-            LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
-            gguf_free(ctx_gguf);
-            ggml_free(ctx);
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
+        auto & e = m_stats[std::move(name)];
+        int ncall;
+        in.read((char*)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char *)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            LOG_ERR("%s: failed reading number of values for entry %d\n",__func__,i);
+            m_stats = {};
             return false;
         }
 
-        auto & e = m_stats[name];
-
-        int64_t nval = ggml_nelements(sums);
         if (e.values.empty()) {
             e.values.resize(nval, 0);
-        } else if ((size_t) nval != e.values.size()) {
-            LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size());
-            gguf_free(ctx_gguf);
-            ggml_free(ctx);
+            e.counts.resize(nval, 0);
+        }
+
+        std::vector<float> tmp(nval);
+        in.read((char*)tmp.data(), nval*sizeof(float));
+        if (in.fail()) {
+            LOG_ERR("%s: failed reading data for entry %d\n",__func__,i);
+            m_stats = {};
             return false;
         }
 
-        int64_t ncounts = ggml_nelements(counts);
-        if (e.counts.empty()) {
-            e.counts.resize(ncounts, 0);
-        } else if (e.counts.size() == 1 && ncounts > 1) {
-            // broadcast, when loading an old imatrix
-            e.counts.resize(ncounts, e.counts[0]);
-        } else if ((size_t) ncounts != e.counts.size()) {
-            LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size());
-            gguf_free(ctx_gguf);
-            ggml_free(ctx);
-            return false;
+        // Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
+        for (int i = 0; i < nval; i++) {
+            e.values[i] += tmp[i];
+            e.counts[i] += ncall;
         }
+        e.ncall += ncall;
 
-        // Recreate the state as expected by save_imatrix()
-        for (int64_t j = 0; j < nval; j++) {
-            e.values[j] += ((const float *) sums->data)[j];
-        }
-        for (int64_t j = 0; j < ncounts; j++) {
-            e.counts[j] += std::lround(((const float *) counts->data)[j]);
-        }
     }
-    gguf_free(ctx_gguf);
-    ggml_free(ctx);
     return true;
 }
 
@@ -509,11 +427,12 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
+static bool compute_imatrix(llama_context * ctx, const common_params & params) {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     const bool add_bos = llama_vocab_get_add_bos(vocab);
+    const int n_ctx = llama_n_ctx(ctx);
 
     GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
@@ -558,61 +477,45 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c
     double nll = 0.0;
     double nll2 = 0.0;
 
+    LOG_INF("%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
+
+    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
     const int num_batches = (n_ctx + n_batch - 1) / n_batch;
-    const int n_seq = std::max(1, n_batch / n_ctx);
-
-    GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
-    GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
-
-    llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
 
     std::vector<float> logits;
     if (params.compute_ppl && num_batches > 1) {
         logits.reserve((size_t)n_ctx * n_vocab);
     }
 
-    LOG_INF("%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
-
-    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
-
-    for (int i = 0; i < n_chunk; i += n_seq) {
+    for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * n_ctx;
         const int end   = start + n_ctx;
 
-        const int n_seq_batch = std::min(n_seq, n_chunk - i);
+        std::vector<float> logits;
 
         const auto t_start = std::chrono::high_resolution_clock::now();
 
         // clear the KV cache
         llama_kv_cache_clear(ctx);
 
+        llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
         for (int j = 0; j < num_batches; ++j) {
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
 
-            // clear the batch
+            // save original token and restore it after eval
+            const auto token_org = tokens[batch_start];
+
+            // add BOS token for the first batch of each chunk
+            if (add_bos && j == 0) {
+                tokens[batch_start] = llama_vocab_bos(vocab);
+            }
+
             common_batch_clear(batch);
-
-            for (int seq = 0; seq < n_seq_batch; seq++) {
-                int seq_start = batch_start + seq*n_ctx;
-
-                // save original token and restore it after eval
-                const auto token_org = tokens[seq_start];
-
-                // add BOS token for the first batch of each chunk
-                if (add_bos && j == 0) {
-                    tokens[seq_start] = llama_vocab_bos(vocab);
-                }
-                for (int k = 0; k < batch_size; ++k) {
-                    // NOTE: specifying all logits to get activations for the output.weight tensor
-                    //       and also for the perplexity calculation.
-                    // TODO: only get outputs when (params.process_output || params.compute_ppl)
-                    //       (not possible when this skips FFN computation of the last layer)
-                    common_batch_add(batch, tokens[seq_start + k], j*n_batch + k, { seq }, true);
-                }
-  
-                // restore the original token in case it was set to BOS
-                tokens[seq_start] = token_org;
+            for (int i = 0; i < batch_size; i++) {
+                common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
             }
 
             if (llama_decode(ctx, batch)) {
@@ -621,19 +524,23 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c
                 return false;
             }
 
+            // restore the original token in case it was set to BOS
+            tokens[batch_start] = token_org;
+
             if (params.compute_ppl && num_batches > 1) {
                 const auto * batch_logits = llama_get_logits(ctx);
                 logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
             }
         }
 
+        llama_batch_free(batch);
+
+        const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {
-            llama_synchronize(ctx);
-            const auto t_end = std::chrono::high_resolution_clock::now();
             const float t_total = std::chrono::duration<float>(t_end - t_start).count();
             LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
-            int total_seconds = (int)(t_total * n_chunk / n_seq);
+            int total_seconds = (int)(t_total * n_chunk);
             if (total_seconds >= 60*60) {
                 LOG("%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
@@ -643,27 +550,17 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c
 
         if (params.compute_ppl) {
             const int first = n_ctx/2;
-            for (int seq = 0; seq < n_seq_batch; seq++) {
-                const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
+            const auto * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
+            process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+                    workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+            count += n_ctx - first - 1;
 
-                llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
-
-                process_logits(n_vocab, all_logits + first*n_vocab,
-                        tokens_data, n_ctx - 1 - first,
-                        workers, nll, nll2,
-                        logit_history.data() + start + seq*n_ctx + first,
-                        prob_history.data()  + start + seq*n_ctx + first);
-
-                count += n_ctx - first - 1;
-
-                LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
-            }
+            LOG("[%d]%.4lf,", i + 1, std::exp(nll / count));
             fflush(stdout);
 
             logits.clear();
         }
     }
-
     LOG("\n");
 
     if (params.compute_ppl) {
@@ -679,8 +576,6 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c
         }
     }
 
-    llama_batch_free(batch);
-
     return true;
 }
 
@@ -697,22 +592,7 @@ int main(int argc, char ** argv) {
 
     common_init();
 
-    const int32_t n_ctx = params.n_ctx;
-
-    if (n_ctx <= 0) {
-        LOG_ERR("%s: imatrix tool requires '--ctx-size' > 0\n", __func__);
-        return 1;
-    }
-
-    {
-        const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
-        const int32_t n_kv = n_seq * n_ctx;
-
-        params.n_parallel = n_seq;
-        params.n_ctx      = n_kv;
-
-        params.n_batch = std::min(params.n_batch, n_kv);
-    }
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
 
     g_collector.set_params(params);
 
@@ -768,7 +648,7 @@ int main(int argc, char ** argv) {
         }
         LOG_INF("No prompt provided; combining precomputed matrices only.\n");
     } else {
-        if (!compute_imatrix(ctx, params, n_ctx)) {
+        if (!compute_imatrix(ctx, params)) {
             return 1;
         }
     }
diff --git a/examples/main/README.md b/examples/main/README.md
index 46f92eb7a..ceaed42f6 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -37,7 +37,7 @@ Once downloaded, place your model in the models folder in llama.cpp.
 
 ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 ```bash
-./llama-cli -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
+./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```
 
 ### Windows:
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 355aef4a6..8d47b17b6 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -1,13 +1,13 @@
 #include "common.h"
 #include "llama.h"
-#include "gguf.h"
 
 #include <cstdio>
 #include <cstring>
 #include <vector>
 #include <string>
 #include <unordered_map>
-#include <map>
+#include <fstream>
+#include <cmath>
 
 struct quant_option {
     std::string name;
@@ -60,11 +60,6 @@ static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET    = "quantize.imatrix
 static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES  = "quantize.imatrix.entries_count";
 static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS   = "quantize.imatrix.chunks_count";
 
-// TODO: share with imatrix.cpp
-static const char * const LLM_KV_IMATRIX_DATASET     = "imatrix.dataset";
-static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
-static const char * const LLM_KV_IMATRIX_CHUNK_SIZE  = "imatrix.chunk_size";
-
 static bool striequals(const char * a, const char * b) {
     while (*a && *b) {
         if (std::tolower(*a) != std::tolower(*b)) {
@@ -134,114 +129,67 @@ static void usage(const char * executable) {
     exit(1);
 }
 
-// TODO: share with implementation in imatrix.cpp
-static bool str_remove_suffix(std::string & str, const std::string & suffix) {
-    bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
-    if (has_suffix) {
-        str = str.substr(0, str.size() - suffix.size());
-    }
-    return has_suffix;
-}
-
 static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
-
-    struct ggml_context * ctx = nullptr;
-    struct gguf_init_params meta_gguf_params = {
-        /* .no_alloc = */ false, // the data is needed
-        /* .ctx      = */ &ctx,
-    };
-    struct gguf_context * ctx_gguf = gguf_init_from_file(imatrix_file.c_str(), meta_gguf_params);
-    if (!ctx_gguf) {
-        fprintf(stderr, "%s: if this is an older imatrix file, make sure to convert it to the GGUF-based imatrix format\n", __func__);
+    std::ifstream in(imatrix_file.c_str(), std::ios::binary);
+    if (!in) {
+        printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
         exit(1);
     }
-    const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
-    if (n_entries < 1) {
-        fprintf(stderr, "%s: no data in file %s\n", __func__, imatrix_file.c_str());
-        gguf_free(ctx_gguf);
-        ggml_free(ctx);
+    int n_entries;
+    in.read((char *)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
         exit(1);
     }
-
-    const int dataset_idx     = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASET);
-    const int chunk_count_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT);
-    const int chunk_size_idx  = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE);
-    if (dataset_idx < 0 || chunk_count_idx < 0 || chunk_size_idx < 0) {
-        fprintf(stderr, "%s: missing imatrix metadata in file %s\n", __func__, imatrix_file.c_str());
-        gguf_free(ctx_gguf);
-        ggml_free(ctx);
-        exit(1);
-    }
-
-    const uint32_t chunk_size = gguf_get_val_u32(ctx_gguf, chunk_size_idx);
-
-    const std::string sums_suffix{".sums"};
-    const std::string counts_suffix{".counts"};
-
-    // Using an ordered map to get a deterministic iteration order.
-    std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
-
-    for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
-        std::string name = cur->name;
-
-        if (name.empty()) { continue; }
-
-        if (str_remove_suffix(name, sums_suffix)) {
-            // sums
-            sums_counts_for[name].first = cur;
-        } else if (str_remove_suffix(name, counts_suffix)) {
-            // counts
-            sums_counts_for[name].second = cur;
-        } else {
-            fprintf(stderr, "%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
-            gguf_free(ctx_gguf);
-            ggml_free(ctx);
+    for (int i = 0; i < n_entries; ++i) {
+        int len; in.read((char *)&len, sizeof(len));
+        std::vector<char> name_as_vec(len+1);
+        in.read((char *)name_as_vec.data(), len);
+        if (in.fail()) {
+            printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
             exit(1);
         }
-    }
-
-    for (const auto & sc : sums_counts_for) {
-        const        std::string & name   = sc.first;
-        const struct ggml_tensor * sums   = sc.second.first;
-        const struct ggml_tensor * counts = sc.second.second;
-
-        if (!sums || !counts) {
-            fprintf(stderr, "%s: mismatched sums and counts for %s\n", __func__, name.c_str());
-            gguf_free(ctx_gguf);
-            ggml_free(ctx);
-            exit(1);
-        }
-
-        const int64_t ne0 = sums->ne[0];
-        const int64_t ne1 = sums->ne[1];
-
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
         auto & e = imatrix_data[name];
-        e.resize(ggml_nelements(sums));
-        float max_count = 0.0f;
-        for (int64_t j = 0; j < ne1; ++j) {
-            const float count = ((const float *) counts->data)[j];
-            for (int64_t i = 0; i < ne0; ++i) {
-                e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count;
-            }
-            if (count > max_count) {
-                max_count = count;
-            }
+        int ncall;
+        in.read((char *)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char *)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            printf("%s: failed reading number of values for entry %d\n", __func__, i);
+            imatrix_data = {};
+            exit(1);
         }
+        e.resize(nval);
+        in.read((char *)e.data(), nval*sizeof(float));
+        if (in.fail()) {
+            printf("%s: failed reading data for entry %d\n", __func__, i);
+            imatrix_data = {};
+            exit(1);
+        }
+        if (ncall > 0) {
+            for (auto& v : e) v /= ncall;
+        }
+
         if (getenv("LLAMA_TRACE")) {
-            printf("%s: loaded data (size = %6d, n_tokens = %6d, n_chunks = %6d) for '%s'\n", __func__, int(e.size()), int(max_count), int(max_count / chunk_size), name.c_str());
+            printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
         }
     }
 
-    int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx);
-    imatrix_dataset = gguf_get_val_str(ctx_gguf, dataset_idx);
-
-    printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
-    printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_chunk);
-
-    gguf_free(ctx_gguf);
-    ggml_free(ctx);
-
-    return m_last_chunk;
+    // latest imatrix version contains the dataset filename at the end of the file
+    int m_last_call = 0;
+    if (in.peek() != EOF) {
+        in.read((char *)&m_last_call, sizeof(m_last_call));
+        int dataset_len;
+        in.read((char *)&dataset_len, sizeof(dataset_len));
+        std::vector<char> dataset_as_vec(dataset_len);
+        in.read(dataset_as_vec.data(), dataset_len);
+        imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
+        printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
+    }
+    printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
+    return m_last_call;
 }
 
 static int prepare_imatrix(const std::string & imatrix_file,
diff --git a/ggml/include/ggml-vulkan.h b/ggml/include/ggml-vulkan.h
index 53cdba072..ed5ea5f79 100644
--- a/ggml/include/ggml-vulkan.h
+++ b/ggml/include/ggml-vulkan.h
@@ -10,8 +10,6 @@ extern "C" {
 #define GGML_VK_NAME "Vulkan"
 #define GGML_VK_MAX_DEVICES 16
 
-GGML_BACKEND_API void ggml_vk_instance_init(void);
-
 // backend API
 GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num);
 
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index d32ba4efb..bffe95086 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -167,6 +167,7 @@ struct vk_device_struct {
     uint32_t subgroup_size;
     uint32_t shader_core_count;
     bool uma;
+    bool prefer_host_memory;
     bool float_controls_rte_fp16;
 
     bool subgroup_size_control;
@@ -1294,7 +1295,9 @@ static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk:
 static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
     vk_buffer buf;
     try {
-        if (device->uma) {
+        if (device->prefer_host_memory) {
+            buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        } else if (device->uma) {
             // Fall back to host memory type
             buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
         } else {
@@ -2199,6 +2202,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
         device->physical_device = physical_devices[dev_num];
         const std::vector<vk::ExtensionProperties> ext_props = device->physical_device.enumerateDeviceExtensionProperties();
 
+        const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY");
+        device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr;
+
         bool fp16_storage = false;
         bool fp16_compute = false;
         bool maintenance4_support = false;
@@ -2787,14 +2793,12 @@ static void ggml_vk_print_gpu_info(size_t idx) {
 static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
 
-void ggml_vk_instance_init() {
+static void ggml_vk_instance_init() {
     if (vk_instance_initialized) {
         return;
     }
     VK_LOG_DEBUG("ggml_vk_instance_init()");
 
-    vk_instance_initialized = true;
-
     uint32_t api_version = vk::enumerateInstanceVersion();
 
     if (api_version < VK_API_VERSION_1_2) {
@@ -2845,6 +2849,7 @@ void ggml_vk_instance_init() {
         GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n");
     }
     vk_instance.instance = vk::createInstance(instance_create_info);
+    vk_instance_initialized = true;
 
     size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
 
@@ -2869,7 +2874,7 @@ void ggml_vk_instance_init() {
         // Make sure at least one device exists
         if (devices.empty()) {
             std::cerr << "ggml_vulkan: Error: No devices found." << std::endl;
-            GGML_ABORT("fatal error");
+            return;
         }
 
         // Default to using all dedicated GPUs
@@ -8344,8 +8349,13 @@ ggml_backend_reg_t ggml_backend_vk_reg() {
         /* .iface       = */ ggml_backend_vk_reg_i,
         /* .context     = */ nullptr,
     };
-
-    return &reg;
+    try {
+        ggml_vk_instance_init();
+        return &reg;
+    } catch (const vk::SystemError& e) {
+        VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: System error: " << e.what());
+        return nullptr;
+    }
 }
 
 // Extension availability
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 0b1f21fc7..ecac5b4bb 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -211,12 +211,6 @@ class Keys:
         TYPE       = "adapter.type"
         LORA_ALPHA = "adapter.lora.alpha"
 
-    class IMatrix:
-        CHUNK_COUNT = "imatrix.chunk_count"
-        CHUNK_SIZE  = "imatrix.chunk_size"
-        DATASET     = "imatrix.dataset"
-
-
 #
 # recommended mapping of model tensor names for storage in gguf
 #
@@ -225,7 +219,6 @@ class Keys:
 class GGUFType:
     MODEL   = "model"
     ADAPTER = "adapter"
-    IMATRIX = "imatrix"
 
 
 class MODEL_ARCH(IntEnum):
diff --git a/requirements.txt b/requirements.txt
index 98c53db81..9e190ae27 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,6 +8,5 @@
 
 -r ./requirements/requirements-convert_hf_to_gguf.txt
 -r ./requirements/requirements-convert_hf_to_gguf_update.txt
--r ./requirements/requirements-convert_legacy_imatrix_to_gguf.txt
 -r ./requirements/requirements-convert_llama_ggml_to_gguf.txt
 -r ./requirements/requirements-convert_lora_to_gguf.txt
diff --git a/requirements/requirements-convert_legacy_imatrix_to_gguf.txt b/requirements/requirements-convert_legacy_imatrix_to_gguf.txt
deleted file mode 100644
index afe2747d4..000000000
--- a/requirements/requirements-convert_legacy_imatrix_to_gguf.txt
+++ /dev/null
@@ -1 +0,0 @@
--r ./requirements-convert_legacy_llama.txt