Q3_K_R4 (#134)

* q3_k_r4: Zen4 works, but not as good as it should be

238 t/s, so sloghtly slower than q6_k_r4.

* q3_k_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 106.9 t/s.
This is 1.93X faster than q3_K_S!

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

10 files changed, 356 insertions, 4 deletions

diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index db0fc0d4..2c8b33c2 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -36,6 +36,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "IQ3_S",    LLAMA_FTYPE_MOSTLY_IQ3_S,    " 3.44 bpw quantization",            },
     { "IQ3_M",    LLAMA_FTYPE_MOSTLY_IQ3_M,    " 3.66 bpw quantization mix",        },
     { "Q3_K",     LLAMA_FTYPE_MOSTLY_Q3_K_M,   "alias for Q3_K_M" },
+    { "Q3_K_R4",  LLAMA_FTYPE_MOSTLY_Q3_K_R4,  "Q3_K_S repacked" },
     { "IQ3_XS",   LLAMA_FTYPE_MOSTLY_IQ3_XS,   " 3.3 bpw quantization"   ,          },
     { "Q3_K_S",   LLAMA_FTYPE_MOSTLY_Q3_K_S,   " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
     { "Q3_K_M",   LLAMA_FTYPE_MOSTLY_Q3_K_M,   " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 7f766497..0ab34f27 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -412,6 +412,7 @@ extern "C" {
         GGML_TYPE_Q4_0_R4   = 202,
         GGML_TYPE_Q5_0_R4   = 206,
         GGML_TYPE_Q8_0_R4   = 208,
+        GGML_TYPE_Q3_K_R4   = 211,
         GGML_TYPE_Q4_K_R4   = 212,
         GGML_TYPE_Q5_K_R4   = 213,
         GGML_TYPE_Q6_K_R4   = 214,
@@ -481,6 +482,7 @@ extern "C" {
         GGML_FTYPE_MOSTLY_Q4_0_R4   = 202, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q8_0_R4   = 207, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q5_0_R4   = 208, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K_R4   = 211, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q4_K_R4   = 212, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q5_K_R4   = 215, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q6_K_R4   = 214, // except 1d tensors
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index 2d73d3f8..bc34718e 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -288,6 +288,15 @@ typedef struct {
 } block_q3_K;
 static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
 
+typedef struct {
+    ggml_half d[4];             // super-block scales
+    uint8_t scales_h[QK_K/16];  // scales quantized with 6 bits (high 2 bits)
+    uint8_t scales_l[QK_K/8];   // scales quantized with 6 bits (low  4 bits)
+    uint8_t qh[QK_K/2];         // quants - high bit
+    uint8_t qs[QK_K];           // quants - low 2 bits
+} block_q3_k_r4;
+static_assert(sizeof(block_q3_k_r4) == 4*sizeof(ggml_half) + QK_K/16 + QK_K/8 + QK_K/2 + QK_K, "wrong q3_k_r4 block size/padding");
+
 // 4-bit quantization
 // 8 blocks of 32 elements each
 // weight is represented as x = a * q + b
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index f4f375c9..c2fdf6fa 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -15202,6 +15202,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_Q5_0_R4: break;
         case GGML_TYPE_Q6_0_R4: break;
         case GGML_TYPE_Q8_0_R4: break;
+        case GGML_TYPE_Q3_K_R4: break;
         case GGML_TYPE_Q4_K_R4: break;
         case GGML_TYPE_Q5_K_R4: break;
         case GGML_TYPE_Q6_K_R4: break;
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 53c51ba6..0bb59d2b 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -875,6 +875,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .nrows                    = 1,
         .row_meta_size            = 0,
     },
+    [GGML_TYPE_Q3_K_R4] = {
+        .type_name                = "q3_k_r4",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q3_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q3_k_r4,
+        .from_float               = quantize_row_q3_k_r4,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q3_k_r4_ref,
+        .vec_dot                  = vec_dot_q3_k_r4_q8_k,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+        .row_meta_size            = 0,
+    },
     [GGML_TYPE_Q4_K] = {
         .type_name                = "q4_K",
         .blck_size                = QK_K,
@@ -4058,7 +4071,8 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_Q8_0:          wtype = GGML_TYPE_Q8_0;  break;
         case GGML_FTYPE_MOSTLY_Q2_K:          wtype = GGML_TYPE_Q2_K;  break;
         case GGML_FTYPE_MOSTLY_Q3_K:          wtype = GGML_TYPE_Q3_K;  break;
-        case GGML_FTYPE_MOSTLY_Q4_K:          wtype = GGML_TYPE_Q4_K;  break;
+        case GGML_FTYPE_MOSTLY_Q3_K_R4:       wtype = GGML_TYPE_Q3_K_R4;  break;
+        case GGML_FTYPE_MOSTLY_Q4_K:          wtype = GGML_TYPE_Q4_K;     break;
         case GGML_FTYPE_MOSTLY_Q4_K_R4:       wtype = GGML_TYPE_Q4_K_R4;  break;
         case GGML_FTYPE_MOSTLY_Q5_K:          wtype = GGML_TYPE_Q5_K;     break;
         case GGML_FTYPE_MOSTLY_Q5_K_R4:       wtype = GGML_TYPE_Q5_K_R4;  break;
@@ -10591,6 +10605,7 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -11043,6 +11058,7 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -11192,6 +11208,7 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -14387,6 +14404,7 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -14776,6 +14794,7 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -15059,6 +15078,7 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -15669,6 +15689,7 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q3_K_R4:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q4_K_R4:
         case GGML_TYPE_Q5_K:
@@ -22507,6 +22528,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_Q8_0:    result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q2_K:    result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q3_K:    result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q3_K_R4: result = quantize_q3_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q4_K:    result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q4_K_R4: result = quantize_q4_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_Q5_K:    result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
diff --git a/ggml/src/iqk/iqk_mul_mat.cpp b/ggml/src/iqk/iqk_mul_mat.cpp
index a1600cbc..d3869f42 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -3440,6 +3440,85 @@ static void mul_mat_q5_k_r4_q8_k(int n, const void * vx, size_t bx, const DataIn
 #endif
 
 template <int nrc_y>
+static void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%4 == 0);
+    Q8<nrc_y, block_q8_K> q8(info);
+    auto m4 = _mm256_set1_epi8(0xf);
+    auto m30 = _mm256_set1_epi8(0x30);
+    auto m32 = _mm256_set1_epi8(32);
+    auto m03 = _mm256_set1_epi8(0x03);
+    auto m04 = _mm256_set1_epi8(0x04);
+    static const uint8_t k_shuff[32] = {0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15};
+    auto shuff = _mm256_loadu_si256((const __m256i *)k_shuff);
+    __m256 d4s[nrc_y];
+    int nbl = n / QK_K;
+    __m256  acc[nrc_y] = {};
+    __m256i qx[4];
+    int8_t scales[64];
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_q3_k_r4 * iq3 = (const block_q3_k_r4 *)((const char *)vx + (ix+0)*bx);
+        for (int ibl = 0; ibl < nbl; ++ibl) { // Block of 256
+            auto dl = _mm_cvtph_ps(_mm_loadl_epi64((const __m128i *)iq3[ibl].d));
+            auto d4 = _mm256_set_m128(dl, dl);
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                d4s[iy] = _mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl)));
+            }
+            auto slb = _mm256_loadu_si256((const __m256i *)iq3[ibl].scales_l);
+            auto shbits = _mm_loadu_si128((const __m128i *)iq3[ibl].scales_h);
+            auto shb = MM256_SET_M128I(_mm_srli_epi16(shbits, 2), shbits);
+            auto scales1 = _mm256_sub_epi8(_mm256_or_si256(_mm256_and_si256(slb, m4), _mm256_and_si256(_mm256_slli_epi16(shb, 4), m30)), m32);
+            auto scales2 = _mm256_sub_epi8(_mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(slb, 4), m4), _mm256_and_si256(shb, m30)), m32);
+            _mm256_storeu_si256((__m256i *)scales+0, scales1);
+            _mm256_storeu_si256((__m256i *)scales+1, scales2);
+            {
+                auto t1 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales1, 0)), shuff); // blocks  0,  1,  2,  3 for each row
+                auto t2 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales1, 1)), shuff); // blocks  4,  5,  6,  7 for each row
+                auto t3 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales2, 0)), shuff); // blocks  8,  9, 10, 11 for each row
+                auto t4 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(scales2, 1)), shuff); // blocks 12, 13, 14, 15 for each row
+                auto s1 = MM256_SET_M128I(_mm256_extracti128_si256(t3, 0), _mm256_extracti128_si256(t1, 0)); // blocks 0, 1,  8, 9
+                auto s2 = MM256_SET_M128I(_mm256_extracti128_si256(t3, 1), _mm256_extracti128_si256(t1, 1)); // blocks 2, 3, 10, 11
+                auto s3 = MM256_SET_M128I(_mm256_extracti128_si256(t4, 0), _mm256_extracti128_si256(t2, 0)); // blocks 4, 5, 12, 13
+                auto s4 = MM256_SET_M128I(_mm256_extracti128_si256(t4, 1), _mm256_extracti128_si256(t2, 1)); // blocks 6, 7, 14, 15
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto bsums = q8.load_bsums(iy, ibl);
+                    auto sumi = _mm256_setzero_si256();
+                    sumi = _mm256_dpwssd_epi32(sumi, s1, _mm256_shuffle_epi32(bsums, 0x00));
+                    sumi = _mm256_dpwssd_epi32(sumi, s2, _mm256_shuffle_epi32(bsums, 0x55));
+                    sumi = _mm256_dpwssd_epi32(sumi, s3, _mm256_shuffle_epi32(bsums, 0xaa));
+                    sumi = _mm256_dpwssd_epi32(sumi, s4, _mm256_shuffle_epi32(bsums, 0xff));
+                    acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4s[iy], _mm256_set1_ps(-4.f)), _mm256_cvtepi32_ps(sumi), acc[iy]);
+                }
+            }
+            for (int ib = 0; ib < QK_K/32; ++ib) {
+                auto iscales = _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i *)(scales + 8*ib)));
+                auto scales  = _mm256_cvtepi32_ps(iscales);
+                auto lb = _mm256_loadu_si256((const __m256i *)iq3[ibl].qs+ib);
+                auto hbits = _mm_loadu_si128((const __m128i *)iq3[ibl].qh+ib);
+                auto hb = MM256_SET_M128I(hbits, _mm_slli_epi16(hbits, 4));
+                qx[0] = _mm256_or_si256(_mm256_and_si256(lb, m03),                       _mm256_and_si256(m04, _mm256_srli_epi16(hb, 2)));
+                qx[1] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 2), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 3)));
+                qx[2] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 4), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 4)));
+                qx[3] = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(lb, 6), m03), _mm256_and_si256(m04, _mm256_srli_epi16(hb, 5)));
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib);
+                    auto sumi = _mm256_setzero_si256();
+                    sumi = _mm256_dpbusd_epi32(sumi, qx[0], _mm256_shuffle_epi32(y, 0x00));
+                    sumi = _mm256_dpbusd_epi32(sumi, qx[1], _mm256_shuffle_epi32(y, 0x55));
+                    sumi = _mm256_dpbusd_epi32(sumi, qx[2], _mm256_shuffle_epi32(y, 0xaa));
+                    sumi = _mm256_dpbusd_epi32(sumi, qx[3], _mm256_shuffle_epi32(y, 0xff));
+                    acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(scales, d4s[iy]), _mm256_cvtepi32_ps(sumi), acc[iy]);
+                }
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            auto sum = _mm_add_ps(_mm256_castps256_ps128(acc[iy]), _mm256_extractf128_ps(acc[iy], 1));
+            acc[iy] = _mm256_setzero_ps();
+            info.store(ix+0, iy, sum);
+        }
+    }
+}
+
+template <int nrc_y>
 static void mul_mat_q6_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
     GGML_ASSERT(nrc_x%4 == 0);
     Q8<nrc_y, block_q8_K> q8(info);
@@ -5546,6 +5625,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
             mm.funcs[7] = mul_mat_iq4_xs_r4_q8_k<8>;
             expected_typeB = GGML_TYPE_Q8_K32;
             break;
+        case GGML_TYPE_Q3_K_R4:
+            assert (ne00 % QK_K == 0);
+            mm.funcs[0] = mul_mat_q3_k_r4_q8_k<1>;
+            mm.funcs[1] = mul_mat_q3_k_r4_q8_k<2>;
+            mm.funcs[2] = mul_mat_q3_k_r4_q8_k<3>;
+            mm.funcs[3] = mul_mat_q3_k_r4_q8_k<4>;
+            mm.funcs[4] = mul_mat_q3_k_r4_q8_k<5>;
+            mm.funcs[5] = mul_mat_q3_k_r4_q8_k<6>;
+            mm.funcs[6] = mul_mat_q3_k_r4_q8_k<7>;
+            mm.funcs[7] = mul_mat_q3_k_r4_q8_k<8>;
+            expected_typeB = GGML_TYPE_Q8_K;
+            break;
         case GGML_TYPE_Q4_K_R4:
             assert (ne00 % QK_K == 0);
             mm.funcs[0] = mul_mat_q4_k_r4_q8_k<1>;
@@ -8274,6 +8365,73 @@ IQK_ALWAYS_INLINE void prepare_q4_k_quants(const uint8x16_t& m4, const uint8x16x
 }
 
 template <int nrc_y>
+void mul_mat_q3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
+    GGML_ASSERT(nrc_x%4 == 0);
+    Q8<nrc_y, block_q8_K> q8(info);
+    auto mf = vdupq_n_u8(0x0f);
+    auto m30 = vdupq_n_u8(0x30);
+    auto m32 = vdupq_n_s8(-32);
+    auto m03 = vdupq_n_u8(0x03);
+    auto m04 = vdupq_n_u8(0x04);
+    int nbl = n / QK_K;
+    int8x16_t qx[4];
+    float32x4_t acc[nrc_y] = {};
+    int8x16x4_t i8scales;
+    int16x8x4_t i16scales;
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_q3_k_r4 * iq3 = (const block_q3_k_r4 *)((const char *)vx + ix*bx);
+        for (int ibl = 0; ibl < nbl; ++ibl) {
+            int32x4_t isum[nrc_y] = {};
+            auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq3[ibl].d));
+            auto sl = vld1q_u8_x2(iq3[ibl].scales_l);
+            auto sh = vld1q_u8(iq3[ibl].scales_h);
+            i8scales.val[0] = vaddq_s8(m32, vorrq_u8(vandq_u8(sl.val[0],  mf), vandq_u8(vshlq_n_u8(sh, 4), m30)));
+            i8scales.val[1] = vaddq_s8(m32, vorrq_u8(vandq_u8(sl.val[1],  mf), vandq_u8(vshlq_n_u8(sh, 2), m30)));
+            i8scales.val[2] = vaddq_s8(m32, vorrq_u8(vshrq_n_u8(sl.val[0], 4), vandq_u8(sh, m30)));
+            i8scales.val[3] = vaddq_s8(m32, vorrq_u8(vshrq_n_u8(sl.val[1], 4), vandq_u8(vshrq_n_u8(sh, 2), m30)));
+            for (int is = 0; is < 2; ++is) {
+                i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
+                i16scales.val[1] = vmovl_s8(vget_high_s8(i8scales.val[2*is+0]));
+                i16scales.val[2] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+1]));
+                i16scales.val[3] = vmovl_s8(vget_high_s8(i8scales.val[2*is+1]));
+                for (int ib = 0; ib < 4; ++ib) {
+                    auto lbits = vld1q_u8_x2(iq3[ibl].qs + 128*is + 32*ib);
+                    auto hbits = vld1q_u8(iq3[ibl].qh + 64*is + 16*ib);
+                    hbits = veorq_u8(hbits, vdupq_n_u8(0xff));
+                    auto scales = vmovl_s16(vget_low_s16 (i16scales.val[ib]));
+                    qx[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(           lbits.val[0],     m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshlq_n_u8(hbits, 2))));
+                    qx[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(lbits.val[0], 2), m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshlq_n_u8(hbits, 1))));
+                    qx[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(lbits.val[0], 4), m03)), vreinterpretq_s8_u8(vandq_u8(m04, hbits)));
+                    qx[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(lbits.val[0], 6), m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshrq_n_u8(hbits, 1))));
+                    for (int iy = 0; iy < nrc_y; ++iy) {
+                        auto y = vld1q_s8(q8.y[iy][ibl].qs+128*is+32*ib);
+                        auto sumi = interleaved_dotq(qx, y);
+                        isum[iy] = vmlaq_s32(isum[iy], scales, sumi);
+                    }
+                    scales = vmovl_s16(vget_high_s16(i16scales.val[ib]));
+                    qx[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(           lbits.val[1],     m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshrq_n_u8(hbits, 2))));
+                    qx[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(lbits.val[1], 2), m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshrq_n_u8(hbits, 3))));
+                    qx[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(lbits.val[1], 4), m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshrq_n_u8(hbits, 4))));
+                    qx[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(lbits.val[1], 6), m03)), vreinterpretq_s8_u8(vandq_u8(m04, vshrq_n_u8(hbits, 5))));
+                    for (int iy = 0; iy < nrc_y; ++iy) {
+                        auto y = vld1q_s8(q8.y[iy][ibl].qs+128*is+32*ib+16);
+                        auto sumi = interleaved_dotq(qx, y);
+                        isum[iy] = vmlaq_s32(isum[iy], scales, sumi);
+                    }
+                }
+            }
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                acc[iy] = vfmaq_f32(acc[iy], vmulq_f32(d4, vdupq_n_f32(q8.scale(iy, ibl))), vcvtq_f32_s32(isum[iy]));
+            }
+        }
+        for (int iy = 0; iy < nrc_y; ++iy) {
+            info.store(ix, iy, acc[iy]);
+            acc[iy] = vdupq_n_f32(0.f);
+        }
+    }
+}
+
+template <int nrc_y>
 void mul_mat_q4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
     GGML_ASSERT(nrc_x%4 == 0);
     Q8<nrc_y, block_q8_K> q8(info);
@@ -8874,6 +9032,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
             SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq4_xs_r4_q8_k);
             expected_Btype = GGML_TYPE_Q8_K;
             break;
+        case GGML_TYPE_Q3_K_R4:
+            SET_MUL_MAT_FUNCTIONS(m, mul_mat_q3_k_r4_q8_k);
+            expected_Btype = GGML_TYPE_Q8_K;
+            break;
         case GGML_TYPE_Q4_K_R4:
             SET_MUL_MAT_FUNCTIONS(m, mul_mat_q4_k_r4_q8_k);
             expected_Btype = GGML_TYPE_Q8_K32;
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index 8ca18060..2e59fefe 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -4301,3 +4301,139 @@ void vec_dot_q5_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
     GGML_UNUSED(by);
 }
 
+//
+// ========================================= q3_k_r4
+//
+
+void quantize_row_q3_k_r4_ref(const float * x, block_q3_k_r4 * y, int64_t k) {
+    quantize_q3_k_r4(x, (void *)y, 4, k/4, nullptr);
+}
+
+void quantize_row_q3_k_r4(const float * x, void * y, int64_t k) {
+    quantize_q3_k_r4(x, y, 4, k/4, nullptr);
+}
+
+namespace {
+inline void convert_q3_k(const block_q3_K& x, uint8_t * L, uint8_t * Ld) {
+    constexpr uint32_t kmask1 = 0x03030303;
+    constexpr uint32_t kmask2 = 0x0f0f0f0f;
+    uint32_t aux[4];
+    memcpy(aux, x.scales, 12);
+    uint32_t tmp = aux[2];
+    aux[2] = ((aux[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+    aux[3] = ((aux[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+    aux[0] = (aux[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+    aux[1] = (aux[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+    std::memcpy(Ld, aux, 16);
+
+    const uint8_t * q = x.qs;
+    const uint8_t * hm = x.hmask;
+    uint8_t m = 1;
+    for (int n = 0; n < QK_K; n += 128) {
+        int shift = 0;
+        for (int j = 0; j < 4; ++j) {
+            for (int l = 0; l < 32; ++l) {
+                *L++ = ((q[l] >> shift) & 3) + ((hm[l] & m) ? 4 : 0);
+            }
+            shift += 2;
+            m <<= 1;
+        }
+        q += 32;
+    }
+}
+}
+
+static void repack_q3_k(int nrows, int n_per_row, const block_q3_K * x, block_q3_k_r4 * y) {
+    GGML_ASSERT(nrows%4 == 0);
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    const block_q3_K * x4[4];
+    uint8_t L[QK_K], Ld[QK_K/16];
+    for (int row = 0; row < nrows; row += 4) {
+        for (int k = 0; k < 4; ++k) x4[k] = x + nblock*k;
+        for (int ibl = 0; ibl < nblock; ++ibl) {
+            std::memset(y[ibl].scales_l, 0, QK_K/8);
+            std::memset(y[ibl].scales_h, 0, QK_K/16);
+            for (int k = 0; k < 4; ++k) {
+                y[ibl].d[k] = x4[k][ibl].d;
+                convert_q3_k(x4[k][ibl], L, Ld);
+                for (int ib = 0; ib < QK_K/32; ++ib) {
+                    int is = 8*ib+k;
+                    y[ibl].scales_l[is%32] |= (Ld[2*ib+0] & 0xf) << 4*(is/32);
+                    y[ibl].scales_h[is%16] |= (Ld[2*ib+0] >>  4) << 2*(is/16);
+                    is += 4;
+                    y[ibl].scales_l[is%32] |= (Ld[2*ib+1] & 0xf) << 4*(is/32);
+                    y[ibl].scales_h[is%16] |= (Ld[2*ib+1] >>  4) << 2*(is/16);
+                    for (int i = 0; i < 4; ++i) {
+                        y[ibl].qs[32*ib+4*k+i+ 0] = ((L[32*ib+i+ 0] & 0x3) << 0) | ((L[32*ib+i+ 4] & 0x3) << 2) | ((L[32*ib+i+ 8] & 0x3) << 4) | ((L[32*ib+i+12] & 0x3) << 6);
+                        y[ibl].qs[32*ib+4*k+i+16] = ((L[32*ib+i+16] & 0x3) << 0) | ((L[32*ib+i+20] & 0x3) << 2) | ((L[32*ib+i+24] & 0x3) << 4) | ((L[32*ib+i+28] & 0x3) << 6);
+                        y[ibl].qh[16*ib+4*k+i+ 0] = ((L[32*ib+i+ 0]  >> 2) << 0) | ((L[32*ib+i+ 4]  >> 2) << 1) | ((L[32*ib+i+ 8]  >> 2) << 2) | ((L[32*ib+i+12]  >> 2) << 3)
+                                                  | ((L[32*ib+i+16]  >> 2) << 4) | ((L[32*ib+i+20]  >> 2) << 5) | ((L[32*ib+i+24]  >> 2) << 6) | ((L[32*ib+i+28]  >> 2) << 7);
+                    }
+                }
+            }
+        }
+        x += 4*nblock;
+        y += nblock;
+    }
+}
+
+size_t quantize_q3_k_r4(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
+    GGML_ASSERT(nrows%4 == 0);
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    char * qcur = (char *)dst;
+    auto row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row);
+    std::vector<char> qtmp(4*row_size);
+    for (int row = 0; row < nrows; row += 4) {
+        quantize_q3_K(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
+        repack_q3_k(4, n_per_row, (const block_q3_K *)qtmp.data(), (block_q3_k_r4 *)qcur);
+        qcur += 4*row_size;
+        src += 4*n_per_row;
+    }
+    return nrows*row_size;
+}
+
+void dequantize_row_q3_k_r4(const block_q3_k_r4 * x, float * y, int64_t k) {
+    auto n_per_row = k/4;
+    float * y4[4] = {y, y + n_per_row, y + 2*n_per_row, y + 3*n_per_row};
+    int nblock = n_per_row/QK_K;
+    for (int ibl = 0; ibl < nblock; ++ibl) {
+        for (int k = 0; k < 4; ++k) {
+            const float d = GGML_FP16_TO_FP32(x[ibl].d[k]);
+            auto ql = x[ibl].qs;
+            auto qh = x[ibl].qh;
+            for (int ib = 0; ib < QK_K/32; ++ib) {
+                int is = 8*ib + k;
+                float dl1 = d * ((((x[ibl].scales_l[is%32] >> 4*(is/32)) & 0xf) | (((x[ibl].scales_h[is%16] >> 2*(is/16)) & 0x03) << 4)) - 32);
+                is += 4;
+                float dl2 = d * ((((x[ibl].scales_l[is%32] >> 4*(is/32)) & 0xf) | (((x[ibl].scales_h[is%16] >> 2*(is/16)) & 0x03) << 4)) - 32);
+                for (int i = 0; i < 4; ++i) {
+                    y4[k][QK_K*ibl+32*ib+i+ 0] = dl1 * ((((ql[4*k+i+ 0] >> 0) & 3) | ((qh[4*k+i] << 2) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+ 4] = dl1 * ((((ql[4*k+i+ 0] >> 2) & 3) | ((qh[4*k+i] << 1) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+ 8] = dl1 * ((((ql[4*k+i+ 0] >> 4) & 3) | ((qh[4*k+i] << 0) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+12] = dl1 * ((((ql[4*k+i+ 0] >> 6) & 3) | ((qh[4*k+i] >> 1) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+16] = dl2 * ((((ql[4*k+i+16] >> 0) & 3) | ((qh[4*k+i] >> 2) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+20] = dl2 * ((((ql[4*k+i+16] >> 2) & 3) | ((qh[4*k+i] >> 3) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+24] = dl2 * ((((ql[4*k+i+16] >> 4) & 3) | ((qh[4*k+i] >> 4) & 4)) - 4);
+                    y4[k][QK_K*ibl+32*ib+i+28] = dl2 * ((((ql[4*k+i+16] >> 6) & 3) | ((qh[4*k+i] >> 5) & 4)) - 4);
+                }
+                ql += 32;
+                qh += 16;
+            }
+        }
+    }
+}
+
+void vec_dot_q3_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
+#if GGML_USE_IQK_MULMAT
+    if (iqk_mul_mat(1, 1, n, GGML_TYPE_Q3_K_R4, vx, 0, GGML_TYPE_Q8_K, vy, 0, s, 0, 0, 1)) {
+        return;
+    }
+#endif
+    GGML_ASSERT(n%QK4_NL == 0);
+    GGML_ASSERT(nrc == 1);
+    GGML_UNUSED(bs);
+    GGML_UNUSED(bx);
+    GGML_UNUSED(by);
+}
+
diff --git a/ggml/src/iqk/iqk_quantize.h b/ggml/src/iqk/iqk_quantize.h
index 77c34fea..f3a4d8e2 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -109,6 +109,12 @@ void   dequantize_row_iq2_bn_r4(const block_iq2_bn * GGML_RESTRICT x, float * GG
 size_t quantize_iq2_bn_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 void   vec_dot_iq2_bn_r4_q8_K64(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
+void   quantize_row_q3_k_r4_ref(const float * GGML_RESTRICT x, block_q3_k_r4  * GGML_RESTRICT y, int64_t k);
+void   quantize_row_q3_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_q3_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+void   dequantize_row_q3_k_r4(const block_q3_k_r4  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void   vec_dot_q3_k_r4_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void   quantize_row_q4_k_r4_ref(const float * GGML_RESTRICT x, block_q4_k_r4  * GGML_RESTRICT y, int64_t k);
 void   quantize_row_q4_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 size_t quantize_q4_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
diff --git a/include/llama.h b/include/llama.h
index 7290f18f..f87d13ff 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -183,6 +183,7 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_Q4_0_R4       = 202, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q8_0_R4       = 207, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q5_0_R4       = 208, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_R4       = 211, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q4_K_R4       = 214, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q5_K_R4       = 216, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q6_K_R4       = 218, // except 1d tensors
diff --git a/src/llama.cpp b/src/llama.cpp
index dc6d307d..9f41724f 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3836,6 +3836,7 @@ struct llama_model_loader {
                 case GGML_TYPE_Q8_0:    ftype = LLAMA_FTYPE_MOSTLY_Q8_0;    break;
                 case GGML_TYPE_Q2_K:    ftype = LLAMA_FTYPE_MOSTLY_Q2_K;    break;
                 case GGML_TYPE_Q3_K:    ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M;  break;
+                case GGML_TYPE_Q3_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_R4; break;
                 case GGML_TYPE_Q4_K:    ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M;  break;
                 case GGML_TYPE_Q4_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_R4; break;
                 case GGML_TYPE_Q5_K:    ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M;  break;
@@ -4548,6 +4549,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
         case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
         case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_R4:  return "Q3_K_R4";
         case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
         case LLAMA_FTYPE_MOSTLY_Q4_K_R4:  return "Q4_K_R4";
         case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
@@ -15792,6 +15794,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             else if (new_type == GGML_TYPE_IQ4_XS_R4) {
                 new_type = GGML_TYPE_IQ4_XS;
             }
+            else if (new_type == GGML_TYPE_Q3_K_R4) {
+                new_type = GGML_TYPE_Q3_K;
+            }
             else if (new_type == GGML_TYPE_Q4_K_R4) {
                 new_type = GGML_TYPE_Q4_K;
             }
@@ -15904,6 +15909,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if (qs.model.hparams.n_gqa() >= 4) {
             if      (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
             else if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_IQ3_S  ) new_type = GGML_TYPE_Q4_K;
+            else if (new_type == GGML_TYPE_Q3_K_R4) new_type = GGML_TYPE_Q4_K_R4;
             else if (new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_IQ4_XS) new_type = GGML_TYPE_Q5_K;
             else if (new_type == GGML_TYPE_IQ4_NL) new_type = GGML_TYPE_Q5_K;
             else if (new_type == GGML_TYPE_IQ4_NL_R4) new_type = GGML_TYPE_Q5_K;
@@ -15935,7 +15941,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) {
             if (qs.model.hparams.n_vocab >= 127999 && (qs.model.type == MODEL_8B || qs.model.type == MODEL_70B))
                 new_type = GGML_TYPE_Q4_K;
-        } 
+        }
     } else if (name.find("ffn_down") != std::string::npos) {
         auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
         int i_layer = info.first, n_layer = info.second;
@@ -16003,7 +16009,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                     ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S   ||
                     ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_K   ||
                     ftype == LLAMA_FTYPE_MOSTLY_IQ2_K  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K  || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_R4 ||
-                    ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4) {
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS_R4 || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_R4) {
                     new_type = GGML_TYPE_Q5_K;
                 }
             } else {
@@ -16073,7 +16079,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         new_type == GGML_TYPE_IQ5_K   || new_type == GGML_TYPE_IQ3_K   || new_type == GGML_TYPE_Q4_K_R4 ||
         new_type == GGML_TYPE_IQ6_K   || new_type == GGML_TYPE_IQ4_KS  || new_type == GGML_TYPE_IQ4_XS_R4 ||
         new_type == GGML_TYPE_IQ2_KS  || new_type == GGML_TYPE_IQ4_KSS || new_type == GGML_TYPE_Q6_K_R4 ||
-        new_type == GGML_TYPE_Q5_K_R4) {
+        new_type == GGML_TYPE_Q5_K_R4 || new_type == GGML_TYPE_Q3_K_R4) {
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];
         if (nx % QK_K != 0) {
@@ -16101,6 +16107,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             case GGML_TYPE_IQ1_M:
             case GGML_TYPE_Q2_K:
             case GGML_TYPE_Q3_K:
+            case GGML_TYPE_Q3_K_R4:
             case GGML_TYPE_IQ2_K:
             case GGML_TYPE_IQ3_K:
             case GGML_TYPE_IQ4_KSS:
@@ -16201,6 +16208,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_Q3_K_S:
         case LLAMA_FTYPE_MOSTLY_Q3_K_M:
         case LLAMA_FTYPE_MOSTLY_Q3_K_L:  default_type = GGML_TYPE_Q3_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_R4: default_type = GGML_TYPE_Q3_K_R4; break;
         case LLAMA_FTYPE_MOSTLY_Q4_K_S:
         case LLAMA_FTYPE_MOSTLY_Q4_K_M:  default_type = GGML_TYPE_Q4_K;    break;
         case LLAMA_FTYPE_MOSTLY_Q4_K_R4: default_type = GGML_TYPE_Q4_K_R4; break;
@@ -16608,6 +16616,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q8_0;
                 else chunk_size_multiplier = 4;
             }
+            else if (new_type == GGML_TYPE_Q3_K_R4) {
+                if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q3_K;
+                else chunk_size_multiplier = 4;
+            }
             else if (new_type == GGML_TYPE_Q4_K_R4) {
                 if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_K;
                 else chunk_size_multiplier = 4;