diff --git a/.gitignore b/.gitignore
index 054759157..4da89db48 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,6 +16,8 @@ build/
 build-em/
 build-debug/
 build-release/
+build-ci-debug/
+build-ci-release/
 build-static/
 build-cublas/
 build-opencl/
@@ -25,6 +27,10 @@ build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
 out/
+tmp/
+
+models/*
+models-mnt
 
 /main
 /quantize
@@ -70,4 +76,4 @@ koboldcpp_openblas_noavx2.dll
 koboldcpp_clblast.dll
 koboldcpp_cublas.dll
 cublas64_11.dll
-cublasLt64_11.dll
\ No newline at end of file
+cublasLt64_11.dll
diff --git a/ci/README.md b/ci/README.md
new file mode 100644
index 000000000..6c74c8138
--- /dev/null
+++ b/ci/README.md
@@ -0,0 +1,20 @@
+# CI
+
+In addition to [Github Actions](https://github.com/ggerganov/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
+
+https://github.com/ggml-org/ci
+
+It monitors the `master` branch for new commits and runs the
+[ci/run.sh](https://github.com/ggerganov/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us
+to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
+to cover various hardware architectures, including GPU and Apple Silicon instances.
+
+Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.
+Only the branches of this repo are monitored for this keyword.
+
+It is a good practice, before publishing changes to execute the full CI locally on your machine:
+
+```bash
+mkdir tmp
+bash ./ci/run.sh ./tmp/results ./tmp/mnt
+```
diff --git a/ci/run.sh b/ci/run.sh
new file mode 100644
index 000000000..c823bc467
--- /dev/null
+++ b/ci/run.sh
@@ -0,0 +1,262 @@
+#/bin/bash
+
+if [ -z "$2" ]; then
+    echo "usage: $0 <output-dir> <mnt-dir>"
+    exit 1
+fi
+
+mkdir -p "$1"
+mkdir -p "$2"
+
+OUT=$(realpath "$1")
+MNT=$(realpath "$2")
+
+rm -v $OUT/*.log
+rm -v $OUT/*.exit
+rm -v $OUT/*.md
+
+sd=`dirname $0`
+cd $sd/../
+SRC=`pwd`
+
+## helpers
+
+# download a file if it does not exist or if it is outdated
+function gg_wget {
+    local out=$1
+    local url=$2
+
+    local cwd=`pwd`
+
+    mkdir -p $out
+    cd $out
+
+    # should not re-download if file is the same
+    wget -nv -N $url
+
+    cd $cwd
+}
+
+function gg_printf {
+    printf -- "$@" >> $OUT/README.md
+}
+
+function gg_run {
+    ci=$1
+
+    set -o pipefail
+    set -x
+
+    gg_run_$ci | tee $OUT/$ci.log
+    cur=$?
+    echo "$cur" > $OUT/$ci.exit
+
+    set +x
+    set +o pipefail
+
+    gg_sum_$ci
+
+    ret=$((ret | cur))
+}
+
+## ci
+
+# ctest_debug
+
+function gg_run_ctest_debug {
+    cd ${SRC}
+
+    rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Debug ..     ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                               ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+
+    set +e
+}
+
+function gg_sum_ctest_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
+# ctest_release
+
+function gg_run_ctest_release {
+    cd ${SRC}
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release ..   ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                               ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    if [ -z $GG_BUILD_LOW_PERF ]; then
+        (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    else
+        (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    fi
+
+    set +e
+}
+
+function gg_sum_ctest_release {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest in release mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
+# open_llama_3b_v2
+
+function gg_run_open_llama_3b_v2 {
+    cd ${SRC}
+
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json
+
+    gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
+    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+    head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
+
+    path_models="../models-mnt/open-llama/3B-v2"
+    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../convert.py ${path_models}
+
+    model_f16="${path_models}/ggml-model-f16.bin"
+    model_q8_0="${path_models}/ggml-model-q8_0.bin"
+    model_q4_0="${path_models}/ggml-model-q4_0.bin"
+    model_q4_1="${path_models}/ggml-model-q4_1.bin"
+    model_q5_0="${path_models}/ggml-model-q5_0.bin"
+    model_q5_1="${path_models}/ggml-model-q5_1.bin"
+    model_q3_k="${path_models}/ggml-model-q3_k.bin"
+    model_q4_k="${path_models}/ggml-model-q4_k.bin"
+    model_q5_k="${path_models}/ggml-model-q5_k.bin"
+    model_q6_k="${path_models}/ggml-model-q6_k.bin"
+
+    wiki_test_60="${path_wiki}/wiki.test-60.raw"
+
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
+
+    (time ./bin/main --model ${model_f16}  -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    function check_ppl {
+        qnt="$1"
+        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+            return 20
+        fi
+
+        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
+        return 0
+    }
+
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    set +e
+}
+
+function gg_sum_open_llama_3b_v2 {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'OpenLLaMA 3B-v2:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+}
+
+## main
+
+if [ -z $GG_BUILD_LOW_PERF ]; then
+    rm -rf ${SRC}/models-mnt
+
+    mnt_models=$(realpath ${MNT}/models)
+    mkdir -p ${mnt_models}
+    ln -sfn ${mnt_models} ${SRC}/models-mnt
+
+    python3 -m pip install -r ${SRC}/requirements.txt
+fi
+
+ret=0
+
+#test $ret -eq 0 && gg_run ctest_debug
+#test $ret -eq 0 && gg_run ctest_release
+
+if [ -z $GG_BUILD_LOW_PERF ]; then
+    test $ret -eq 0 && gg_run open_llama_3b_v2
+fi
+
+exit $ret
diff --git a/examples/common.cpp b/examples/common.cpp
index 94875b054..fd6dbc0e3 100644
--- a/examples/common.cpp
+++ b/examples/common.cpp
@@ -168,6 +168,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
+        } else if (arg == "--rope-freq-base") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_base = std::stof(argv[i]);
+        } else if (arg == "--rope-freq-scale") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_scale = std::stof(argv[i]);
         } else if (arg == "--memory-f32") {
             params.memory_f16 = false;
         } else if (arg == "--top-p") {
@@ -267,6 +279,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_keep = std::stoi(argv[i]);
+        } else if (arg == "--chunks") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_chunks = std::stoi(argv[i]);
         } else if (arg == "-m" || arg == "--model") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -493,6 +511,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
     fprintf(stderr, "  --cfg-smooth-factor N smooth factor between old and new logits (default: %f, 1.0 = no smoothing)\n", params.cfg_smooth_factor);
     fprintf(stderr, "  -c N, --ctx-size N    size of the prompt context (default: %d)\n", params.n_ctx);
+    fprintf(stderr, "  --rope-freq-base N    RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
+    fprintf(stderr, "  --rope-freq-scale N   RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
     fprintf(stderr, "  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
     fprintf(stderr, "  --no-penalize-nl      do not penalize newline token\n");
     fprintf(stderr, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
@@ -501,6 +521,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  --perplexity          compute perplexity over the prompt\n");
     fprintf(stderr, "  --keep                number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
+    fprintf(stderr, "  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     if (llama_mlock_supported()) {
         fprintf(stderr, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
@@ -573,6 +594,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     lparams.use_mlock    = params.use_mlock;
     lparams.logits_all   = params.perplexity;
     lparams.embedding    = params.embedding;
+    lparams.rope_freq_base  = params.rope_freq_base;
+    lparams.rope_freq_scale = params.rope_freq_scale;
 
     return lparams;
 }
diff --git a/examples/common.h b/examples/common.h
index 6315df961..037a4eecb 100644
--- a/examples/common.h
+++ b/examples/common.h
@@ -28,10 +28,13 @@ 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_chunks                        = -1;  // max number of chunks to process (-1 = unlimited)
     int32_t n_gpu_layers                    = 0;   // number of layers to store in VRAM
     int32_t main_gpu                        = 0;   // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
     int32_t n_probs                         = 0;   // if greater than 0, output the probabilities of top n_probs tokens.
+    float   rope_freq_base                  = 10000.0f; // RoPE base frequency
+    float   rope_freq_scale                 = 1.0f;     // RoPE frequency scaling factor
 
     // sampling parameters
     std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 2248c2458..bcbcf12b0 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -84,9 +84,17 @@ int main(int argc, char ** argv) {
         return 0;
     }
 
+    if (params.rope_freq_base != 10000.0) {
+        fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 1.0) {
+        fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
+    }
+
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
-                "expect poor results\n", __func__, params.n_ctx);
+        fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified);"
+                " you are on your own\n", __func__, params.n_ctx);
     } else if (params.n_ctx < 8) {
         fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
         params.n_ctx = 8;
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 7e120ff12..bfad99939 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -32,13 +32,15 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     // BOS tokens will be added for each chunk before eval
     auto tokens = ::llama_tokenize(ctx, params.prompt, true);
 
-    int count   = 0;
+    const int n_chunk_max = tokens.size() / params.n_ctx;
 
-    const int n_chunk = tokens.size() / params.n_ctx;
+    const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
     const int n_vocab = llama_n_vocab(ctx);
     const int n_batch = params.n_batch;
 
+    int count = 0;
     double nll = 0.0;
+
     fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
 
     for (int i = 0; i < n_chunk; ++i) {
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index fcf960a50..0d5b906fb 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -12,103 +12,27 @@ struct quant_option {
 };
 
 static const std::vector<struct quant_option> QUANT_OPTIONS = {
-    {
-        "Q4_0",
-        LLAMA_FTYPE_MOSTLY_Q4_0,
-        " 3.50G, +0.2499 ppl @ 7B - small, very high quality loss - legacy, prefer using Q3_K_M",
-    },
-    {
-        "Q4_1",
-        LLAMA_FTYPE_MOSTLY_Q4_1,
-        " 3.90G, +0.1846 ppl @ 7B - small, substantial quality loss - legacy, prefer using Q3_K_L",
-    },
-    {
-        "Q5_0",
-        LLAMA_FTYPE_MOSTLY_Q5_0,
-        " 4.30G, +0.0796 ppl @ 7B - medium, balanced quality - legacy, prefer using Q4_K_M",
-    },
-    {
-        "Q5_1",
-        LLAMA_FTYPE_MOSTLY_Q5_1,
-        " 4.70G, +0.0415 ppl @ 7B - medium, low quality loss - legacy, prefer using Q5_K_M",
-    },
+    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.50G, +0.2499 ppl @ 7B", },
+    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1846 ppl @ 7B", },
+    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.30G, +0.0796 ppl @ 7B", },
+    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0415 ppl @ 7B", },
 #ifdef GGML_USE_K_QUANTS
-    {
-        "Q2_K",
-        LLAMA_FTYPE_MOSTLY_Q2_K,
-        " 2.67G, +0.8698 ppl @ 7B - smallest, extreme quality loss - not recommended",
-    },
-    {
-        "Q3_K",
-        LLAMA_FTYPE_MOSTLY_Q3_K_M,
-        "alias for Q3_K_M"
-    },
-    {
-        "Q3_K_S",
-        LLAMA_FTYPE_MOSTLY_Q3_K_S,
-        " 2.75G, +0.5505 ppl @ 7B - very small, very high quality loss",
-    },
-    {
-        "Q3_K_M",
-        LLAMA_FTYPE_MOSTLY_Q3_K_M,
-        " 3.06G, +0.2437 ppl @ 7B - very small, very high quality loss",
-    },
-    {
-        "Q3_K_L",
-        LLAMA_FTYPE_MOSTLY_Q3_K_L,
-        " 3.35G, +0.1803 ppl @ 7B - small, substantial quality loss",
-    },
-    {
-        "Q4_K",
-        LLAMA_FTYPE_MOSTLY_Q4_K_M,
-        "alias for Q4_K_M",
-    },
-    {
-        "Q4_K_S",
-        LLAMA_FTYPE_MOSTLY_Q4_K_S,
-        " 3.56G, +0.1149 ppl @ 7B - small, significant quality loss",
-    },
-    {
-        "Q4_K_M",
-        LLAMA_FTYPE_MOSTLY_Q4_K_M,
-        " 3.80G, +0.0535 ppl @ 7B - medium, balanced quality - *recommended*",
-    },
-    {
-        "Q5_K",
-        LLAMA_FTYPE_MOSTLY_Q5_K_M,
-        "alias for Q5_K_M",
-    },
-    {
-        "Q5_K_S",
-        LLAMA_FTYPE_MOSTLY_Q5_K_S,
-        " 4.33G, +0.0353 ppl @ 7B - large, low quality loss - *recommended*",
-    },
-    {
-        "Q5_K_M",
-        LLAMA_FTYPE_MOSTLY_Q5_K_M,
-        " 4.45G, +0.0142 ppl @ 7B - large, very low quality loss - *recommended*",
-    },
-    {
-        "Q6_K",
-        LLAMA_FTYPE_MOSTLY_Q6_K,
-        " 5.15G, +0.0044 ppl @ 7B - very large, extremely low quality loss",
-    },
+    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.67G, +0.8698 ppl @ 7B", },
+    { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
+    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5505 ppl @ 7B", },
+    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.06G, +0.2437 ppl @ 7B", },
+    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1803 ppl @ 7B", },
+    { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
+    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.56G, +0.1149 ppl @ 7B", },
+    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0535 ppl @ 7B", },
+    { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
+    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0353 ppl @ 7B", },
+    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0142 ppl @ 7B", },
+    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0044 ppl @ 7B", },
 #endif
-    {
-        "Q8_0",
-        LLAMA_FTYPE_MOSTLY_Q8_0,
-        " 6.70G, +0.0004 ppl @ 7B - very large, extremely low quality loss - not recommended",
-    },
-    {
-        "F16",
-        LLAMA_FTYPE_MOSTLY_F16,
-        "13.00G              @ 7B - extremely large, virtually no quality loss - not recommended",
-    },
-    {
-        "F32",
-        LLAMA_FTYPE_ALL_F32,
-        "26.00G              @ 7B - absolutely huge, lossless - not recommended",
-    },
+    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ 7B", },
+    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
+    { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
 };
 
 
diff --git a/examples/server/README.md b/examples/server/README.md
index ad9b6bb08..e5ca8269b 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -66,6 +66,7 @@ Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the
 ```sh
 curl --request POST \
     --url http://localhost:8080/completion \
+    --header "Content-Type: application/json" \
     --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
 ```
 
diff --git a/examples/server/chat.sh b/examples/server/chat.sh
index a89f8e908..014360121 100644
--- a/examples/server/chat.sh
+++ b/examples/server/chat.sh
@@ -32,6 +32,7 @@ tokenize() {
         --silent \
         --request POST \
         --url "${API_URL}/tokenize" \
+        --header "Content-Type: application/json" \
         --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
     | jq '.tokens[]'
 }
@@ -64,6 +65,7 @@ chat_completion() {
         --no-buffer \
         --request POST \
         --url "${API_URL}/completion" \
+        --header "Content-Type: application/json" \
         --data-raw "${DATA}")
 
     printf "\n"
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 296c5d646..f442f2b56 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -608,6 +608,8 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stderr, "  -v, --verbose         verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
     fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
     fprintf(stderr, "  -c N, --ctx-size N    size of the prompt context (default: %d)\n", params.n_ctx);
+    fprintf(stderr, "  --rope-freq-base N    RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
+    fprintf(stderr, "  --rope-freq-scale N   RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
     fprintf(stderr, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stderr, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
@@ -722,6 +724,22 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.n_ctx = std::stoi(argv[i]);
         }
+        else if (arg == "--rope-freq-base")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_base = std::stof(argv[i]);
+        }
+        else if (arg == "--rope-freq-scale")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_scale = std::stof(argv[i]);
+        }
         else if (arg == "--memory-f32" || arg == "--memory_f32")
         {
             params.memory_f16 = false;
diff --git a/expose.h b/expose.h
index 2e88946d7..b7814a6ce 100644
--- a/expose.h
+++ b/expose.h
@@ -36,7 +36,8 @@ struct load_model_inputs
     const int debugmode = 0;
     const int forceversion = 0;
     const int gpulayers = 0;
-    const bool linear_rope;
+    const float rope_freq_scale = 1.0f;
+    const float rope_freq_base = 10000.0f;
     const char * banned_tokens[ban_token_max];
 };
 struct generation_inputs
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 3cf9e29e0..923d10f76 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -2977,10 +2977,8 @@ inline void ggml_cuda_op_rope(
     const int mode   = ((int32_t *) src1->data)[2];
     const int n_ctx  = ((int32_t *) src1->data)[3];
 
-    const float theta_scale = get_theta_scale(n_dims,n_past,n_ctx);
-    const float p0 = ((mode & 1) == 0 ? n_past + i02 : i02);
-
-    const float p = get_ntk_rope_scale_mode()?p0:(n_ctx <= GGML_TRAINING_CTX ? p0 : p0 * GGML_TRAINING_CTX / n_ctx);
+    const float theta_scale = powf(10000.0, -2.0f/n_dims);
+    const float p = ((mode & 1) == 0 ? n_past + i02 : i02);
 
     bool is_glm = mode & 4;
 
@@ -3564,6 +3562,11 @@ void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tens
     (void) dst;
 }
 
+void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_cpy(src0, dst, nullptr);
+    (void) src1;
+}
+
 void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
     ggml_cuda_op(src0, src1, dst, ggml_cuda_op_diag_mask_inf, true, true);
@@ -3697,7 +3700,7 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
     // recursively assign CUDA buffers until a compute tensor is found
     if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
         const ggml_op src0_op = tensor->src[0]->op;
-        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW) {
+        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
             ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace);
         }
     }
@@ -3803,6 +3806,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
         || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
 
     switch (tensor->op) {
+        case GGML_OP_DUP:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_dup;
+            break;
         case GGML_OP_ADD:
             if (!any_on_device) {
                 return false;
@@ -3857,6 +3866,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
             }
             func = ggml_cuda_cpy;
             break;
+        case GGML_OP_CONT:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_dup;
+            break;
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
diff --git a/ggml-metal.m b/ggml-metal.m
index c795ee227..ee205bcdf 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -881,28 +881,35 @@ void ggml_metal_graph_compute(
 
                             const int n_past = ((int32_t *)(src1->data))[0];
 
+                            float freq_base;
+                            float freq_scale;
+                            memcpy(&freq_base,  (int32_t *) src1->data + 4, sizeof(float));
+                            memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
+
                             [encoder setComputePipelineState:ctx->pipeline_rope];
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00   length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&ne01   length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne02   length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne03   length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&nb00   length:sizeof(uint64_t) atIndex:6];
-                            [encoder setBytes:&nb01   length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb02   length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb03   length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&ne0    length:sizeof( int64_t) atIndex:10];
-                            [encoder setBytes:&ne1    length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne2    length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne3    length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&nb0    length:sizeof(uint64_t) atIndex:14];
-                            [encoder setBytes:&nb1    length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb2    length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb3    length:sizeof(uint64_t) atIndex:17];
-                            [encoder setBytes:&n_past length:sizeof(     int) atIndex:18];
-                            [encoder setBytes:&n_dims length:sizeof(     int) atIndex:19];
-                            [encoder setBytes:&mode   length:sizeof(     int) atIndex:20];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
+                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
+                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBytes:&n_past  length:sizeof(     int) atIndex:18];
+                            [encoder setBytes:&n_dims  length:sizeof(     int) atIndex:19];
+                            [encoder setBytes:&mode    length:sizeof(     int) atIndex:20];
+                            [encoder setBytes:&freq_base  length:sizeof(float) atIndex:21];
+                            [encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                         } break;
diff --git a/ggml-metal.metal b/ggml-metal.metal
index f094a1d40..9f9a4fbd7 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -656,17 +656,19 @@ kernel void kernel_rope(
         constant       int & n_past,
         constant       int & n_dims,
         constant       int & mode,
+        constant     float & freq_base,
+        constant     float & freq_scale,
         uint3 tpig[[thread_position_in_grid]]) {
     const int64_t i3 = tpig[2];
     const int64_t i2 = tpig[1];
     const int64_t i1 = tpig[0];
 
     const bool is_neox = mode & 2;
-    const float theta_scale = pow(10000.0, -2.0f/n_dims);
+    const float theta_scale = pow(freq_base, -2.0f/n_dims);
 
     const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
 
-    float theta = (float)p;
+    float theta = freq_scale * (float)p;
 
     if (!is_neox) {
         for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
diff --git a/ggml.c b/ggml.c
index a288b778d..436f8806c 100644
--- a/ggml.c
+++ b/ggml.c
@@ -4292,35 +4292,6 @@ static inline int ggml_up(int n, int m) {
 #define ggml_assert_aligned(ptr) \
     GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0)
 
-static bool useNtkRope = true; //uses linear rope if not NTK
-void set_ntk_rope_scale_mode(bool useNtk)
-{
-    useNtkRope = useNtk;
-}
-bool get_ntk_rope_scale_mode()
-{
-    return useNtkRope;
-}
-float get_theta_scale(int n_dims,int n_past,int n_ctx)
-{
-    if (!get_ntk_rope_scale_mode())
-    {
-        return powf(10000.0, -2.0f / n_dims);
-    }
-    if (n_ctx <= 2048) //normie mode
-    {
-        return powf(10000.0, -2.0f / n_dims);
-    }
-    else
-    {
-        //using scaled NTK aware ctx
-        float a = (n_ctx <= 4096 ? 4.0 : 8.0);
-        float m = powf(a, n_dims / (n_dims - 2.0));
-        float s = powf(10000.0 * m, -2.0f / n_dims);
-        return s;
-    }
-}
-
 ////////////////////////////////////////////////////////////////////////////////
 
 struct ggml_context * ggml_init(struct ggml_init_params params) {
@@ -4442,8 +4413,8 @@ void ggml_free(struct ggml_context * ctx) {
         if (&g_state.contexts[i].context == ctx) {
             g_state.contexts[i].used = false;
 
-            GGML_PRINT_DEBUG("%s: context %d with %d objects has been freed. memory used = %zu\n",
-                    __func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size);
+            GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n",
+                    __func__, i, ggml_used_mem(ctx));
 
             if (ctx->mem_buffer_owned) {
                 GGML_ALIGNED_FREE(ctx->mem_buffer);
@@ -6986,6 +6957,8 @@ struct ggml_tensor * ggml_rope_impl(
         int                   n_past,
         int                   n_dims,
         int                   mode,
+        float                 freq_base,
+        float                 freq_scale,
         int                   n_ctx,
         bool                  inplace) {
     GGML_ASSERT(n_past >= 0);
@@ -6999,12 +6972,14 @@ struct ggml_tensor * ggml_rope_impl(
 
     ggml_scratch_save(ctx);
 
-    struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4);
+    struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6);
 
     ((int32_t *) b->data)[0] = n_past;
     ((int32_t *) b->data)[1] = n_dims;
     ((int32_t *) b->data)[2] = mode;
     ((int32_t *) b->data)[3] = n_ctx;
+    memcpy((int32_t *) b->data + 4, &freq_base,  sizeof(float));
+    memcpy((int32_t *) b->data + 5, &freq_scale, sizeof(float));
 
     ggml_scratch_load(ctx);
 
@@ -7023,7 +6998,7 @@ struct ggml_tensor * ggml_rope(
         int                   n_dims,
         int                   mode,
         int                   n_ctx) {
-    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, false);
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, false);
 }
 
 struct ggml_tensor * ggml_rope_inplace(
@@ -7033,7 +7008,19 @@ struct ggml_tensor * ggml_rope_inplace(
         int                   n_dims,
         int                   mode,
         int                   n_ctx) {
-    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, true);
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, true);
+}
+
+struct ggml_tensor * ggml_rope_custom_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   n_past,
+        int                   n_dims,
+        int                   mode,
+        float                 freq_base,
+        float                 freq_scale,
+        int                   n_ctx) {
+    return ggml_rope_impl(ctx, a, n_past, n_dims, mode, freq_base, freq_scale, n_ctx, true);
 }
 
 // ggml_rope_back
@@ -12104,16 +12091,21 @@ static void ggml_compute_forward_rope_f32(
         const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
-    GGML_ASSERT(ggml_nelements(src1) == 4);
+    GGML_ASSERT(ggml_nelements(src1) == 6);
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
     }
 
+    float freq_base;
+    float freq_scale;
+
     const int n_past = ((int32_t *) src1->data)[0];
     const int n_dims = ((int32_t *) src1->data)[1];
     const int mode   = ((int32_t *) src1->data)[2];
     const int n_ctx  = ((int32_t *) src1->data)[3];
+    memcpy(&freq_base,  (int32_t *) src1->data + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
 
     assert(n_past >= 0);
 
@@ -12142,7 +12134,7 @@ static void ggml_compute_forward_rope_f32(
     // row index used to determine which thread to use
     int ir = 0;
 
-    const float theta_scale = get_theta_scale(n_dims,n_past,n_ctx);
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
@@ -12154,7 +12146,7 @@ static void ggml_compute_forward_rope_f32(
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta = (float)p;
+                float theta = freq_scale * (float)p;
 
                 if (is_glm) {
                     theta = MIN(p, n_ctx - 2);
@@ -12182,9 +12174,6 @@ static void ggml_compute_forward_rope_f32(
                         dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta;
                     }
                 } else if (!is_neox) {
-                    if (!get_ntk_rope_scale_mode() && n_ctx > GGML_TRAINING_CTX) {
-                        theta = theta * GGML_TRAINING_CTX / n_ctx;
-                    }
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                         const float cos_theta = cosf(theta);
                         const float sin_theta = sinf(theta);
@@ -12234,16 +12223,21 @@ static void ggml_compute_forward_rope_f16(
         const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
-    GGML_ASSERT(ggml_nelements(src1) == 4);
+    GGML_ASSERT(ggml_nelements(src1) == 6);
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
     }
 
+    float freq_base;
+    float freq_scale;
+
     const int n_past = ((int32_t *) src1->data)[0];
     const int n_dims = ((int32_t *) src1->data)[1];
     const int mode   = ((int32_t *) src1->data)[2];
     const int n_ctx  = ((int32_t *) src1->data)[3];
+    memcpy(&freq_base,  (int32_t *) src1->data + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
 
     assert(n_past >= 0);
 
@@ -12272,7 +12266,7 @@ static void ggml_compute_forward_rope_f16(
     // row index used to determine which thread to use
     int ir = 0;
 
-    const float theta_scale = get_theta_scale(n_dims,n_past,n_ctx);
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
@@ -12284,7 +12278,7 @@ static void ggml_compute_forward_rope_f16(
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta = (float)p;
+                float theta = freq_scale * (float)p;
 
                 if (is_glm) {
                     theta = MIN(p, n_ctx - 2);
@@ -12312,9 +12306,6 @@ static void ggml_compute_forward_rope_f16(
                         dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
                     }
                 } if (!is_neox) {
-                    if (!get_ntk_rope_scale_mode() && n_ctx > GGML_TRAINING_CTX) {
-                        theta = theta * GGML_TRAINING_CTX / n_ctx;
-                    }
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                         const float cos_theta = cosf(theta);
                         const float sin_theta = sinf(theta);
@@ -12348,7 +12339,7 @@ static void ggml_compute_forward_rope_f16(
                             const float x0 = GGML_FP16_TO_FP32(src[0]);
                             const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
 
-                            dst_data[0]     = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
+                            dst_data[0]        = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
                             dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
                         }
                     }
@@ -12400,7 +12391,6 @@ static void ggml_compute_forward_rope_back_f32(
     const int n_past = ((int32_t *) src1->data)[0];
     const int n_dims = ((int32_t *) src1->data)[1];
     const int mode   = ((int32_t *) src1->data)[2];
-    const int n_ctx  = ((int32_t *) src1->data)[3];
 
     assert(n_past >= 0);
 
@@ -12426,7 +12416,7 @@ static void ggml_compute_forward_rope_back_f32(
     // row index used to determine which thread to use
     int ir = 0;
 
-    const float theta_scale = get_theta_scale(n_dims,n_past,n_ctx);
+    const float theta_scale = powf(10000.0, -2.0f/n_dims);
 
     const bool is_neox = mode & 2;
 
@@ -12440,9 +12430,6 @@ static void ggml_compute_forward_rope_back_f32(
                 float theta = (float)p;
 
                 if (!is_neox) {
-                    if (!get_ntk_rope_scale_mode() && n_ctx > GGML_TRAINING_CTX) {
-                        theta = theta * GGML_TRAINING_CTX / n_ctx;
-                    }
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                         const float cos_theta = cosf(theta);
                         const float sin_theta = sinf(theta);
@@ -12503,7 +12490,6 @@ static void ggml_compute_forward_rope_back_f16(
     const int n_past = ((int32_t *) src1->data)[0];
     const int n_dims = ((int32_t *) src1->data)[1];
     const int mode   = ((int32_t *) src1->data)[2];
-    const int n_ctx  = ((int32_t *) src1->data)[3];
 
     assert(n_past >= 0);
 
@@ -12529,7 +12515,7 @@ static void ggml_compute_forward_rope_back_f16(
     // row index used to determine which thread to use
     int ir = 0;
 
-    const float theta_scale = get_theta_scale(n_dims,n_past,n_ctx);
+    const float theta_scale = powf(10000.0, -2.0f/n_dims);
 
     const bool is_neox = mode & 2;
 
@@ -12543,9 +12529,6 @@ static void ggml_compute_forward_rope_back_f16(
                 float theta = (float)p;
 
                 if (!is_neox) {
-                    if (!get_ntk_rope_scale_mode() && n_ctx > GGML_TRAINING_CTX) {
-                        theta = theta * GGML_TRAINING_CTX / n_ctx;
-                    }
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                         const float cos_theta = cosf(theta);
                         const float sin_theta = sinf(theta);
@@ -15754,7 +15737,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                 // necessary for llama
                 if (src0->grad) {
                     assert(src1->type == GGML_TYPE_I32);
-                    assert(ggml_nelements(src1) == 4);
+                    assert(ggml_nelements(src1) == 6);
                     const int n_past = ((int32_t *) src1->data)[0];
                     const int n_dims = ((int32_t *) src1->data)[1];
                     const int mode   = ((int32_t *) src1->data)[2];
@@ -15775,7 +15758,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 if (src0->grad) {
                     assert(src1->type == GGML_TYPE_I32);
-                    assert(ggml_nelements(src1) == 4);
+                    assert(ggml_nelements(src1) == 3);
                     const int n_past = ((int32_t *) src1->data)[0];
                     const int n_dims = ((int32_t *) src1->data)[1];
                     const int mode   = ((int32_t *) src1->data)[2];
@@ -16335,8 +16318,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 if (GGML_OP_HAS_FINALIZE[node->op]) {
                     params.nth = n_tasks_arr[node_n];
                     ggml_compute_forward(&params, node);
-                    ggml_graph_compute_perf_stats_node(node, state->shared);
                 }
+                ggml_graph_compute_perf_stats_node(node, state->shared);
             }
 
             // distribute new work or execute it direct if 1T
@@ -16366,8 +16349,9 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                     if (GGML_OP_HAS_FINALIZE[node->op]) {
                         params.type = GGML_TASK_FINALIZE;
                         ggml_compute_forward(&params, node);
-                        ggml_graph_compute_perf_stats_node(node, state->shared);
                     }
+
+                    ggml_graph_compute_perf_stats_node(node, state->shared);
                 } else {
                     break;
                 }
@@ -16909,9 +16893,6 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
 }
 
 void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
-    //assert(cgraph->work      == NULL);
-    //assert(cgraph->work_size == 0);
-
     uint64_t size_eval = 0;
 
     // compute size of intermediate results
@@ -17350,9 +17331,6 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
 
     GGML_PRINT("=== GRAPH ===\n");
 
-    GGML_PRINT_DEBUG("n_threads       = %d\n",        cgraph->n_threads);
-    GGML_PRINT_DEBUG("total work size = %zu bytes\n", cgraph->work_size);
-
     GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];
@@ -18708,4 +18686,4 @@ int ggml_cpu_has_vsx(void) {
 #endif
 }
 
-////////////////////////////////////////////////////////////////////////////////
\ No newline at end of file
+////////////////////////////////////////////////////////////////////////////////
diff --git a/ggml.h b/ggml.h
index 999ab2778..524c8c22c 100644
--- a/ggml.h
+++ b/ggml.h
@@ -207,12 +207,6 @@
 
 #define GGML_UNUSED(x) (void)(x)
 
-// Maximum training context of the model in use
-// For the LLaMA models this is normally 2048, but somehow "stepping out" by 128 gives better results (tested at 7B and 13B)
-#ifndef GGML_TRAINING_CTX
-#define GGML_TRAINING_CTX 2048
-#endif
-
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
@@ -551,10 +545,6 @@ extern "C" {
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
-    GGML_API void set_ntk_rope_scale_mode(bool useNtk);
-    GGML_API bool get_ntk_rope_scale_mode();
-    GGML_API float get_theta_scale(int n_dims,int n_past,int n_ctx);
-
     // main
 
     GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
@@ -1130,6 +1120,17 @@ extern "C" {
             int                   mode,
             int                   n_ctx);
 
+    // custom RoPE, in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past,
+            int                   n_dims,
+            int                   mode,
+            float                 freq_base,
+            float                 freq_scale,
+            int                   n_ctx);
+
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
     GGML_API struct ggml_tensor * ggml_rope_back(
diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 121b70b97..e304f81ca 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -348,12 +348,32 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
     = gpt2_ctx_v1.hparams.n_ctx = gpt2_ctx_v2.hparams.n_ctx = gpt2_ctx_v3.hparams.n_ctx
     = mpt_ctx_v3.hparams.n_ctx = params.n_ctx;
 
-    //handle linear rope
-    if(inputs.linear_rope)
+    //determine rope scaling params
+    float rope_freq_scale = 1.0f;
+    float rope_freq_base = 10000.0f;
+    if(inputs.rope_freq_scale>0.0f)
     {
-        printf("Using Linear RoPE scaling instead of NTK-Aware scaling.\n");
+        rope_freq_scale = inputs.rope_freq_scale;
+        rope_freq_base = inputs.rope_freq_base;
+        printf("Using Custom RoPE scaling (scale:%.3f, base:%.1f).\n",rope_freq_scale,rope_freq_base);
     }
-    set_ntk_rope_scale_mode(!inputs.linear_rope);
+    else
+    {
+        rope_freq_scale = 1.0f;
+        if (params.n_ctx <= 2048) //normie mode
+        {
+            rope_freq_base = 10000.0f;
+        }
+        else
+        {
+            //approximate NTK aware ctx
+            rope_freq_base = (params.n_ctx <= 4096 ? 40880.0f : 82684.0f);
+        }
+
+        printf("Using automatic RoPE scaling (scale:%.3f, base:%.1f)\n",rope_freq_scale,rope_freq_base);
+    }
+    gptj_ctx_v3.hparams.rope_freq_scale = neox_ctx_v3.hparams.rope_freq_scale = rope_freq_scale;
+    gptj_ctx_v3.hparams.rope_freq_base = neox_ctx_v3.hparams.rope_freq_base = rope_freq_base;
 
     //handle custom token bans
     banned_tokens.clear();
@@ -444,6 +464,8 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
         llama_ctx_params.use_mlock = inputs.use_mlock;
         llama_ctx_params.n_gpu_layers = inputs.gpulayers;
         llama_ctx_params.main_gpu = cu_parseinfo_maindevice;
+        llama_ctx_params.rope_freq_base = rope_freq_base;
+        llama_ctx_params.rope_freq_scale = rope_freq_scale;
 
         llama_ctx_v3 = llama_init_from_file(modelname.c_str(), llama_ctx_params);
 
diff --git a/koboldcpp.py b/koboldcpp.py
index 1c55fd302..926be06b6 100755
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -36,7 +36,8 @@ class load_model_inputs(ctypes.Structure):
                 ("debugmode", ctypes.c_int),
                 ("forceversion", ctypes.c_int),
                 ("gpulayers", ctypes.c_int),
-                ("linear_rope", ctypes.c_bool),
+                ("rope_freq_scale", ctypes.c_float),
+                ("rope_freq_base", ctypes.c_float),
                 ("banned_tokens", ctypes.c_char_p * ban_token_max)]
 
 class generation_inputs(ctypes.Structure):
@@ -189,7 +190,11 @@ def load_model(model_filename):
     inputs.blasbatchsize = args.blasbatchsize
     inputs.forceversion = args.forceversion
     inputs.gpulayers = args.gpulayers
-    inputs.linear_rope = args.linearrope
+    inputs.rope_freq_scale = args.ropeconfig[0]
+    if len(args.ropeconfig)>1:
+        inputs.rope_freq_base = args.ropeconfig[1]
+    else:
+        inputs.rope_freq_base = 10000
     clblastids = 0
     if args.useclblast:
         clblastids = 100 + int(args.useclblast[0])*10 + int(args.useclblast[1])
@@ -1434,7 +1439,7 @@ if __name__ == '__main__':
     parser.add_argument("--highpriority", help="Experimental flag. If set, increases the process CPU priority, potentially speeding up generation. Use caution.", action='store_true')
     parser.add_argument("--contextsize", help="Controls the memory allocated for maximum context size, only change if you need more RAM for big contexts. (default 2048)", type=int,choices=[512,1024,2048,3072,4096,6144,8192], default=2048)
     parser.add_argument("--blasbatchsize", help="Sets the batch size used in BLAS processing (default 512). Setting it to -1 disables BLAS mode, but keeps other benefits like GPU offload.", type=int,choices=[-1,32,64,128,256,512,1024], default=512)
-    parser.add_argument("--linearrope", help="If set, uses linear RoPE scaling. Otherwise, uses NTK-Aware scaling.", action='store_true')
+    parser.add_argument("--ropeconfig", help="If set, uses customized RoPE scaling from configured frequency scale and frequency base (e.g. --ropeconfig 0.25 10000). Otherwise, uses NTK-Aware scaling set automatically based on context size. For linear rope, simply set the freq-scale and ignore the freq-base",metavar=('[rope-freq-scale]', '[rope-freq-base]'), default=[0.0, 10000.0], type=float, nargs='+')
     parser.add_argument("--stream", help="Uses streaming when generating tokens. Only for the Kobold Lite UI.", action='store_true')
     parser.add_argument("--smartcontext", help="Reserving a portion of context to try processing less frequently.", action='store_true')
     parser.add_argument("--unbantokens", help="Normally, KoboldAI prevents the EOS token from being generated. This flag unbans it.", action='store_true')
diff --git a/llama.cpp b/llama.cpp
index d60f5a339..9984c0e09 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -102,14 +102,15 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
 // memory sizes
 //
 
-static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
+static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0(int n_ctx)
 {
     static std::map<e_model, size_t> k_sizes = {
-        { MODEL_3B,    256ull * MB },
-        { MODEL_7B,    512ull * MB },
-        { MODEL_13B,   512ull * MB },
-        { MODEL_30B,   640ull * MB },
-        { MODEL_65B,  1024ull * MB },
+        /* empirical scaling, still a guess */
+        { MODEL_3B,   ((size_t) n_ctx / 16ull + 180ull) * MB },
+        { MODEL_7B,   ((size_t) n_ctx / 16ull + 320ull) * MB },
+        { MODEL_13B,  ((size_t) n_ctx / 12ull + 460ull) * MB },
+        { MODEL_30B,  ((size_t) n_ctx / 10ull + 620ull) * MB },
+        { MODEL_65B,  ((size_t) n_ctx /  8ull + 860ull) * MB },
     };
     return k_sizes;
 }
@@ -141,14 +142,14 @@ static const std::map<e_model, size_t> & MEM_REQ_KV_SELF()
 
 // this is mostly needed for temporary mul_mat buffers to dequantize the data
 // not actually needed if BLAS is disabled
-static const std::map<e_model, size_t> & MEM_REQ_EVAL()
+static const std::map<e_model, size_t> & MEM_REQ_EVAL(int n_ctx)
 {
     static std::map<e_model, size_t> k_sizes = {
-        { MODEL_3B,   512ull * MB },
-        { MODEL_7B,   800ull * MB },
-        { MODEL_13B, 1024ull * MB },
-        { MODEL_30B, 1380ull * MB },
-        { MODEL_65B, 1536ull * MB },
+        { MODEL_3B,  ((size_t) n_ctx / 256ull +  512ull) * MB },
+        { MODEL_7B,  ((size_t) n_ctx / 256ull +  800ull) * MB },
+        { MODEL_13B, ((size_t) n_ctx / 256ull + 1024ull) * MB },
+        { MODEL_30B, ((size_t) n_ctx / 256ull + 1380ull) * MB },
+        { MODEL_65B, ((size_t) n_ctx / 256ull + 1536ull) * MB },
     };
     return k_sizes;
 }
@@ -190,6 +191,10 @@ struct llama_hparams {
     uint32_t n_head  = 32;
     uint32_t n_layer = 32;
     uint32_t n_rot   = 64;
+
+    float rope_freq_base  = 10000.0f;
+    float rope_freq_scale = 1.0f;
+
     enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
 
     bool operator!=(const llama_hparams & other) const {
@@ -648,7 +653,7 @@ struct llama_model_loader {
         *ctx_size_p = *mmapped_size_p = 0;
         for (const llama_load_tensor & lt : tensors_map.tensors) {
             *ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
-            *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size;
+            *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size + 16;
         }
     }
 
@@ -844,6 +849,8 @@ struct llama_context_params llama_context_default_params() {
         /*.gpu_layers                  =*/ 0,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ {0},
+        /*.rope_freq_base              =*/ 10000.0f,
+        /*.rope_freq_scale             =*/ 1.0f,
         /*.progress_callback           =*/ nullptr,
         /*.progress_callback_user_data =*/ nullptr,
         /*.low_vram                    =*/ false,
@@ -967,6 +974,8 @@ static void llama_model_load_internal(
         int n_gpu_layers,
         int main_gpu,
         const float * tensor_split,
+        float rope_freq_base,
+        float rope_freq_scale,
         bool low_vram,
         ggml_type memory_type,
         bool use_mmap,
@@ -1001,22 +1010,27 @@ static void llama_model_load_internal(
         }
 
         hparams.n_ctx = n_ctx;
+
+        hparams.rope_freq_base  = rope_freq_base;
+        hparams.rope_freq_scale = rope_freq_scale;
     }
 
     const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
 
     {
-        fprintf(stderr, "%s: format     = %s\n",  __func__, llama_file_version_name(file_version));
-        fprintf(stderr, "%s: n_vocab    = %u\n",  __func__, hparams.n_vocab);
-        fprintf(stderr, "%s: n_ctx      = %u\n",  __func__, hparams.n_ctx);
-        fprintf(stderr, "%s: n_embd     = %u\n",  __func__, hparams.n_embd);
-        fprintf(stderr, "%s: n_mult     = %u\n",  __func__, hparams.n_mult);
-        fprintf(stderr, "%s: n_head     = %u\n",  __func__, hparams.n_head);
-        fprintf(stderr, "%s: n_layer    = %u\n",  __func__, hparams.n_layer);
-        fprintf(stderr, "%s: n_rot      = %u\n",  __func__, hparams.n_rot);
+        fprintf(stderr, "%s: format     = %s\n",   __func__, llama_file_version_name(file_version));
+        fprintf(stderr, "%s: n_vocab    = %u\n",   __func__, hparams.n_vocab);
+        fprintf(stderr, "%s: n_ctx      = %u\n",   __func__, hparams.n_ctx);
+        fprintf(stderr, "%s: n_embd     = %u\n",   __func__, hparams.n_embd);
+        fprintf(stderr, "%s: n_mult     = %u\n",   __func__, hparams.n_mult);
+        fprintf(stderr, "%s: n_head     = %u\n",   __func__, hparams.n_head);
+        fprintf(stderr, "%s: n_layer    = %u\n",   __func__, hparams.n_layer);
+        fprintf(stderr, "%s: n_rot      = %u\n",   __func__, hparams.n_rot);
+        fprintf(stderr, "%s: freq_base  = %.1f\n", __func__, hparams.rope_freq_base);
+        fprintf(stderr, "%s: freq_scale = %g\n",   __func__, hparams.rope_freq_scale);
         fprintf(stderr, "%s: ftype      = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
-        fprintf(stderr, "%s: n_ff       = %u\n",  __func__, n_ff);
-        fprintf(stderr, "%s: model size = %s\n",  __func__, llama_model_type_name(model.type));
+        fprintf(stderr, "%s: n_ff       = %u\n",   __func__, n_ff);
+        fprintf(stderr, "%s: model size = %s\n",   __func__, llama_model_type_name(model.type));
     }
 
     if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
@@ -1162,13 +1176,12 @@ static void llama_model_load_internal(
         const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
 
         // this is the total memory required to run the inference
-        const size_t bigctxmul = (hparams.n_ctx>4096?3:(hparams.n_ctx>2048?2:1));
         const size_t mem_required =
             ctx_size +
             mmapped_size - vram_weights + // weights in VRAM not in memory
-            MEM_REQ_SCRATCH0().at(model.type)*bigctxmul +
-            MEM_REQ_SCRATCH1().at(model.type)*bigctxmul +
-            MEM_REQ_EVAL().at    (model.type);
+            MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
+            MEM_REQ_SCRATCH1().at(model.type) +
+            MEM_REQ_EVAL(hparams.n_ctx).at(model.type);
 
         // this is the memory required by one llama_state
         const size_t mem_required_state =
@@ -1272,6 +1285,8 @@ static bool llama_model_load(
         int n_gpu_layers,
         int main_gpu,
         float * tensor_split,
+        float rope_freq_base,
+        float rope_freq_scale,
         bool low_vram,
         ggml_type memory_type,
         bool use_mmap,
@@ -1280,7 +1295,7 @@ static bool llama_model_load(
         llama_progress_callback progress_callback,
         void *progress_callback_user_data) {
     try {
-        llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type,
+        llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
                                   use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
         return true;
     } catch (const std::exception & err) {
@@ -1332,6 +1347,9 @@ static bool llama_eval_internal(
     const int n_rot        = hparams.n_embd/hparams.n_head;
     const int n_gpu_layers = model.n_gpu_layers;
 
+    const float freq_base  = hparams.rope_freq_base;
+    const float freq_scale = hparams.rope_freq_scale;
+
     auto & mem_per_token = lctx.mem_per_token;
     auto & buf_compute   = lctx.buf_compute;
 
@@ -1429,11 +1447,11 @@ static bool llama_eval_internal(
             offload_func_kq(tmpq);
             ggml_set_name(tmpq, "tmpq");
 
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, n_ctx);
+            struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
             offload_func_kq(Kcur);
             ggml_set_name(Kcur, "Kcur");
 
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, n_ctx);
+            struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
             offload_func_kq(Qcur);
             ggml_set_name(Qcur, "Qcur");
 
@@ -2007,9 +2025,18 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
     }
 
     // Normalize the second derivatives
-    float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
-    for (float & value : second_derivatives) {
-        value /= second_derivatives_sum;
+    {
+        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
+
+        if (second_derivatives_sum > 1e-6f) {
+            for (float & value : second_derivatives) {
+                value /= second_derivatives_sum;
+            }
+        } else {
+            for (float & value : second_derivatives) {
+                value = 1.0f / second_derivatives.size();
+            }
+        }
     }
 
     float cum_sum = 0.0f;
@@ -2188,7 +2215,7 @@ void llama_sample_classifier_free_guidance(
           struct llama_context * guidance_ctx,
                          float   scale,
                          float   smooth_factor) {
-    int64_t t_start_sample_us = t_start_sample_us = ggml_time_us();
+    int64_t t_start_sample_us = ggml_time_us();
 
     assert(ctx);
     auto n_vocab = llama_n_vocab(ctx);
@@ -2676,8 +2703,9 @@ struct llama_model * llama_load_model_from_file(
     ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
 
     if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers,
-                params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock,
-                params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
+                params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
+                memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
+                params.progress_callback_user_data)) {
         delete model;
         fprintf(stderr, "%s: failed to load model\n", __func__);
         return nullptr;
@@ -2752,11 +2780,10 @@ struct llama_context * llama_new_context_with_model(
             ctx->embedding.resize(hparams.n_embd);
         }
 
-        ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type));
+        ctx->buf_compute.resize(MEM_REQ_EVAL(hparams.n_ctx).at(ctx->model.type));
 
-        const size_t bigctxmul = (hparams.n_ctx>4096?3:(hparams.n_ctx>2048?2:1));
-        ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type)*bigctxmul);
-        ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type)*bigctxmul);
+        ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
+        ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
     }
 
 #ifdef GGML_USE_METAL
diff --git a/llama.h b/llama.h
index e7c60f40c..e744584f2 100644
--- a/llama.h
+++ b/llama.h
@@ -89,6 +89,11 @@ extern "C" {
         int32_t  n_gpu_layers;                 // number of layers to store in VRAM
         int32_t  main_gpu;                     // the GPU that is used for scratch and small tensors
         float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
+
+        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
+        float    rope_freq_base;  // RoPE base frequency
+        float    rope_freq_scale; // RoPE frequency scaling factor
+
         // called with a progress value between 0 and 1, pass NULL to disable
         llama_progress_callback progress_callback;
         // context pointer passed to the progress callback
diff --git a/otherarch/gptj_v3.cpp b/otherarch/gptj_v3.cpp
index 46f1ad064..21cf40500 100644
--- a/otherarch/gptj_v3.cpp
+++ b/otherarch/gptj_v3.cpp
@@ -409,6 +409,9 @@ bool gptj_eval(
     const int n_vocab = hparams.n_vocab;
     const int n_rot   = hparams.n_rot;
 
+    const float freq_base  = hparams.rope_freq_base;
+    const float freq_scale = hparams.rope_freq_scale;
+
     static size_t buf_size = 256u*1024*1024;
     static void * buf = malloc(buf_size);
 
@@ -475,8 +478,8 @@ bool gptj_eval(
 
         // self-attention
         {
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, n_ctx);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, n_ctx);
+            struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, n_ctx);
+            struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, n_ctx);
 
             // store key and value to memory
             {
diff --git a/otherarch/neox_v3.cpp b/otherarch/neox_v3.cpp
index d8ccaa9b6..d9d7225cb 100644
--- a/otherarch/neox_v3.cpp
+++ b/otherarch/neox_v3.cpp
@@ -425,6 +425,9 @@ bool gpt_neox_eval(
     const int n_vocab = hparams.n_vocab;
     const int n_rot   = hparams.n_rot;
 
+    const float freq_base  = hparams.rope_freq_base;
+    const float freq_scale = hparams.rope_freq_scale;
+
     static size_t buf_size = 256u*1024*1024;
     static void * buf = malloc(buf_size);
 
@@ -503,8 +506,8 @@ bool gpt_neox_eval(
             struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head));
 
             // using mode = 2 for GPT-NeoX mode
-            Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2, n_ctx);
-            Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2, n_ctx);
+            Qcur = ggml_rope_custom_inplace(ctx0, Qcur, n_past, n_rot, 2, freq_base, freq_scale, n_ctx);
+            Kcur = ggml_rope_custom_inplace(ctx0, Kcur, n_past, n_rot, 2, freq_base, freq_scale, n_ctx);
 
             // store key and value to memory
             {
diff --git a/otherarch/otherarch.h b/otherarch/otherarch.h
index 7d396453d..a1ec3bf63 100644
--- a/otherarch/otherarch.h
+++ b/otherarch/otherarch.h
@@ -24,6 +24,9 @@ struct gptj_hparams {
     int32_t n_layer = 28;
     int32_t n_rot   = 64;
     int32_t ftype   = 1;
+
+    float rope_freq_base  = 10000.0f;
+    float rope_freq_scale = 1.0f;
 };
 
 struct gptj_layer {
@@ -309,6 +312,9 @@ struct gpt_neox_hparams {
     int32_t n_rot   = 32; // rotary_pct * (n_embd / n_head)
     int32_t par_res = 1; // 1 = true, 0 = false
     int32_t ftype   = 1;
+
+    float rope_freq_base  = 10000.0f;
+    float rope_freq_scale = 1.0f;
 };
 
 struct gpt_neox_layer_v2 {