IQ2_K_R4 (#146)

* iq2_k_r4: Zen4

* iq2_k_r4: NEON

* iq2_k_r4: better matrix x vector multiplication on NEON

---------

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

10 files changed, 430 insertions, 11 deletions

diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index a2f83150..73485838 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -53,6 +53,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "IQ4_KS",   LLAMA_FTYPE_MOSTLY_IQ4_KS,   " 4.25 bpw non-linear quantization", },
     { "IQ4_KSS",  LLAMA_FTYPE_MOSTLY_IQ4_KSS,  " 4.0 bpw non-linear quantization",  },
     { "IQ2_K",    LLAMA_FTYPE_MOSTLY_IQ2_K,    " 2.375 bpw non-linear quantization",},
+    { "IQ2_K_R4", LLAMA_FTYPE_MOSTLY_IQ2_K_R4, "IQ2_K repacked",},
     { "IQ2_KS",   LLAMA_FTYPE_MOSTLY_IQ2_KS,   " 2.1875 bpw non-linear quantization",},
     { "IQ3_K",    LLAMA_FTYPE_MOSTLY_IQ3_K,    " 3.44 bpw non-linear quantization", },
     { "IQ3_K_R4", LLAMA_FTYPE_MOSTLY_IQ3_K_R4, "IQ3_K repacked", },
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 04831c2f..77ee0fb9 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -423,6 +423,7 @@ extern "C" {
         GGML_TYPE_BF16_R16  = 230,
         GGML_TYPE_Q6_0_R4   = 233,
         GGML_TYPE_IQ2_BN_R4 = 335,
+        GGML_TYPE_IQ2_K_R4  = 337,
         GGML_TYPE_IQ3_K_R4  = 338,
         GGML_TYPE_IQ4_K_R4  = 339,
         GGML_TYPE_Q8_K_R8   = 399,
@@ -498,6 +499,7 @@ extern "C" {
         GGML_FTYPE_MOSTLY_BF16_R16  = 224, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q6_0_R4   = 227, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_BN_R4 = 329, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_K_R4  = 330, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ3_K_R4  = 331, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ4_K_R4  = 332, // except 1d tensors
         GGML_FTYPE_MOSTLY_Q8_K_R8   = 399, // except 1d tensors
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index ca56704c..03cc3460 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -522,6 +522,14 @@ typedef struct {
 static_assert(sizeof(block_iq2_k) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/32 + QK_K/4, "wrong iq2_k block size/padding");
 
 typedef struct {
+    ggml_half d[4];
+    uint8_t  extra[8];
+    uint8_t  scales[QK_K/8];
+    uint8_t  qs[QK_K];
+} block_iq2_k_r4;
+static_assert(sizeof(block_iq2_k_r4) == 4*sizeof(block_iq2_k), "wrong iq2_k_r4 block size/padding");
+
+typedef struct {
     uint16_t extra;
     uint8_t  scales[QK_K/64];
     uint8_t  qs[QK_K/4];
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 1d022672..a3beba20 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -15207,6 +15207,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
         case GGML_TYPE_Q4_K_R4: break;
         case GGML_TYPE_Q5_K_R4: break;
         case GGML_TYPE_Q6_K_R4: break;
+        case GGML_TYPE_IQ2_K_R4: break;
         case GGML_TYPE_IQ3_K_R4: break;
         case GGML_TYPE_IQ4_K_R4: break;
         case GGML_TYPE_Q8_K_R8: break;
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 4194d943..45c873f2 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -1308,6 +1308,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .nrows                    = 1,
         .row_meta_size            = 0,
     },
+    [GGML_TYPE_IQ2_K_R4] = {
+        .type_name                = "iq2_k_r4",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq2_k),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq2_k_r4,
+        .from_float               = quantize_row_iq2_k_r4,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq2_k_r4_ref,
+        .vec_dot                  = vec_dot_iq2_k_r4_q8_k,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+        .row_meta_size            = 0,
+    },
     [GGML_TYPE_IQ2_KS] = {
         .type_name                = "iq2_ks",
         .blck_size                = QK_K,
@@ -4173,6 +4186,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_IQ4_KS:        wtype = GGML_TYPE_IQ4_KS;   break;
         case GGML_FTYPE_MOSTLY_IQ4_KSS:       wtype = GGML_TYPE_IQ4_KSS;  break;
         case GGML_FTYPE_MOSTLY_IQ2_K:         wtype = GGML_TYPE_IQ2_K;    break;
+        case GGML_FTYPE_MOSTLY_IQ2_K_R4:      wtype = GGML_TYPE_IQ2_K_R4; break;
         case GGML_FTYPE_MOSTLY_IQ2_KS:        wtype = GGML_TYPE_IQ2_KS;   break;
         case GGML_FTYPE_MOSTLY_IQ3_K:         wtype = GGML_TYPE_IQ3_K;    break;
         case GGML_FTYPE_MOSTLY_IQ4_K:         wtype = GGML_TYPE_IQ4_K;    break;
@@ -10711,6 +10725,7 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -11168,6 +11183,7 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -11322,6 +11338,7 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -14522,6 +14539,7 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -14916,6 +14934,7 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -15204,6 +15223,7 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -15821,6 +15841,7 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_IQ4_KS:
         case GGML_TYPE_IQ4_KSS:
         case GGML_TYPE_IQ2_K:
+        case GGML_TYPE_IQ2_K_R4:
         case GGML_TYPE_IQ2_KS:
         case GGML_TYPE_IQ3_K:
         case GGML_TYPE_IQ4_K:
@@ -22666,6 +22687,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_IQ4_KS:  result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_KSS: result = quantize_iq4_kss(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_K:   result = quantize_iq2_k  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_K_R4:result = quantize_iq2_k_r4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ2_KS:  result = quantize_iq2_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ3_K:   result = quantize_iq3_k  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_K:   result = quantize_iq4_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 bcf96c0a..d08491c3 100644
--- a/ggml/src/iqk/iqk_mul_mat.cpp
+++ b/ggml/src/iqk/iqk_mul_mat.cpp
@@ -182,6 +182,7 @@ struct MulMat {
             case GGML_TYPE_Q8_0_R4:
             case GGML_TYPE_IQ4_NL_R4:
             case GGML_TYPE_IQ4_XS_R4:
+            case GGML_TYPE_IQ2_K_R4:
             case GGML_TYPE_IQ3_K_R4:
             case GGML_TYPE_IQ4_K_R4:
             case GGML_TYPE_IQ2_BN_R4: return 4;
@@ -3958,6 +3959,108 @@ static void mul_mat_bf16_r16_bf16(int n, const void * vx, size_t bx, const DataI
 #endif
 
 template <int nrc_y>
+static void mul_mat_iq2_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 ms  = _mm256_set1_epi8(4);
+    auto m03 = _mm256_set1_epi8(0x03);
+    auto shift_shuffle = _mm256_set_epi64x(0x0707070706060606, 0x0505050504040404, 0x0303030302020202, 0x0101010100000000);
+    static const uint8_t kvalues_iq2nl[32] = {1, 19, 33, 49, 6, 24, 38, 54, 1, 19, 33, 49, 6, 24, 38, 54, 1, 19, 33, 49, 6, 24, 38, 54, 1, 19, 33, 49, 6, 24, 38, 54};
+    auto values = _mm256_loadu_si256((const __m256i*)kvalues_iq2nl);
+    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);
+#ifndef HAVE_FANCY_SIMD
+    auto s_shuffle = _mm256_set_epi64x(0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908, 0x0706070605040504, 0x0302030201000100);
+#endif
+    int nbl = n / QK_K;
+    __m256  acc[nrc_y] = {};
+    __m256i qx[4];
+    uint64_t stored_scales[8];
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_iq2_k_r4 * iq2 = (const block_iq2_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 *)iq2[ibl].d));
+            auto d4 = _mm256_set_m128(dl, dl);
+            auto extra = _mm256_set1_epi64x(*(const uint64_t *)iq2[ibl].extra);
+            auto slbits = _mm256_loadu_si256((const __m256i *)iq2[ibl].scales);
+            auto i8scales1 = _mm256_add_epi8(_mm256_and_si256(slbits, m4), _mm256_set1_epi8(-8));
+            auto i8scales2 = _mm256_add_epi8(_mm256_and_si256(_mm256_srli_epi16(slbits, 4), m4), _mm256_set1_epi8(-8));
+            _mm256_storeu_si256((__m256i *)stored_scales+0, i8scales1);
+            _mm256_storeu_si256((__m256i *)stored_scales+1, i8scales2);
+            __m256i isum[nrc_y] = {};
+            {
+                auto t1 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales1, 0)), shuff); // blocks  0,  1,  2,  3 for each row
+                auto t2 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales1, 1)), shuff); // blocks  4,  5,  6,  7 for each row
+                auto t3 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales2, 0)), shuff); // blocks  8,  9, 10, 11 for each row
+                auto t4 = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm256_extracti128_si256(i8scales2, 1)), shuff); // blocks 12, 13, 14, 15 for each row
+                auto s1 = _mm256_mullo_epi16(_mm256_set1_epi16(-32), MM256_SET_M128I(_mm256_extracti128_si256(t3, 0), _mm256_extracti128_si256(t1, 0))); // blocks 0, 1,  8, 9
+                auto s2 = _mm256_mullo_epi16(_mm256_set1_epi16(-32), MM256_SET_M128I(_mm256_extracti128_si256(t3, 1), _mm256_extracti128_si256(t1, 1))); // blocks 2, 3, 10, 11
+                auto s3 = _mm256_mullo_epi16(_mm256_set1_epi16(-32), MM256_SET_M128I(_mm256_extracti128_si256(t4, 0), _mm256_extracti128_si256(t2, 0))); // blocks 4, 5, 12, 13
+                auto s4 = _mm256_mullo_epi16(_mm256_set1_epi16(-32), 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);
+#ifdef HAVE_FANCY_SIMD
+                    isum[iy] = _mm256_dpwssd_epi32(isum[iy], s1, _mm256_shuffle_epi32(bsums, 0x00));
+                    isum[iy] = _mm256_dpwssd_epi32(isum[iy], s2, _mm256_shuffle_epi32(bsums, 0x55));
+                    isum[iy] = _mm256_dpwssd_epi32(isum[iy], s3, _mm256_shuffle_epi32(bsums, 0xaa));
+                    isum[iy] = _mm256_dpwssd_epi32(isum[iy], s4, _mm256_shuffle_epi32(bsums, 0xff));
+#else
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s1, _mm256_shuffle_epi32(bsums, 0x00)));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s2, _mm256_shuffle_epi32(bsums, 0x55)));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s3, _mm256_shuffle_epi32(bsums, 0xaa)));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_madd_epi16(s4, _mm256_shuffle_epi32(bsums, 0xff)));
+#endif
+                }
+            }
+            for (int ib = 0; ib < QK_K/32; ++ib) {
+#ifdef HAVE_FANCY_SIMD
+                auto scales = _mm256_cvtepi8_epi32(_mm_loadl_epi64((const __m128i *)(stored_scales + ib)));
+#else
+                auto scales = _mm256_shuffle_epi8(_mm256_cvtepi8_epi16(_mm_set1_epi64x(stored_scales[ib])), s_shuffle);
+#endif
+                auto lb = _mm256_loadu_si256((const __m256i *)iq2[ibl].qs+ib);
+                auto shift = _mm256_and_si256(ms, _mm256_slli_epi16(extra, 2)); extra = _mm256_srli_epi16(extra, 1);
+                shift = _mm256_shuffle_epi8(shift, shift_shuffle);
+                qx[0] = _mm256_and_si256(lb, m03);
+                qx[1] = _mm256_and_si256(_mm256_srli_epi16(lb, 2), m03);
+                qx[2] = _mm256_and_si256(_mm256_srli_epi16(lb, 4), m03);
+                qx[3] = _mm256_and_si256(_mm256_srli_epi16(lb, 6), m03);
+                qx[0] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[0], shift));
+                qx[1] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[1], shift));
+                qx[2] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[2], shift));
+                qx[3] = _mm256_shuffle_epi8(values, _mm256_add_epi8(qx[3], shift));
+                for (int iy = 0; iy < nrc_y; ++iy) {
+                    auto y = _mm256_loadu_si256((const __m256i*)q8.y[iy][ibl].qs+ib);
+#ifdef HAVE_FANCY_SIMD
+                    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));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_mullo_epi32(scales, sumi));
+#else
+                    auto sumi1 = _mm256_add_epi16(_mm256_maddubs_epi16(qx[0], _mm256_shuffle_epi32(y, 0x00)),
+                                                  _mm256_maddubs_epi16(qx[1], _mm256_shuffle_epi32(y, 0x55)));
+                    auto sumi2 = _mm256_add_epi16(_mm256_maddubs_epi16(qx[2], _mm256_shuffle_epi32(y, 0xaa)),
+                                                  _mm256_maddubs_epi16(qx[3], _mm256_shuffle_epi32(y, 0xff)));
+                    isum[iy] = _mm256_add_epi32(isum[iy], _mm256_add_epi32(_mm256_madd_epi16(scales, sumi1), _mm256_madd_epi16(scales, sumi2)));
+#endif
+                }
+            }
+            for (int iy = 0; iy < nrc_y; ++iy) {
+                acc[iy] = _mm256_fmadd_ps(_mm256_mul_ps(d4, _mm256_set1_ps(q8.scale(iy, ibl))), _mm256_cvtepi32_ps(isum[iy]), 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_iq3_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);
@@ -6281,6 +6384,18 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
             mm.funcs[7] = mul_mat_iq4_k_r4_q8_k<8>;
             expected_typeB = GGML_TYPE_Q8_K;
             break;
+        case GGML_TYPE_IQ2_K_R4:
+            assert (ne00 % QK_K == 0);
+            mm.funcs[0] = mul_mat_iq2_k_r4_q8_k<1>;
+            mm.funcs[1] = mul_mat_iq2_k_r4_q8_k<2>;
+            mm.funcs[2] = mul_mat_iq2_k_r4_q8_k<3>;
+            mm.funcs[3] = mul_mat_iq2_k_r4_q8_k<4>;
+            mm.funcs[4] = mul_mat_iq2_k_r4_q8_k<5>;
+            mm.funcs[5] = mul_mat_iq2_k_r4_q8_k<6>;
+            mm.funcs[6] = mul_mat_iq2_k_r4_q8_k<7>;
+            mm.funcs[7] = mul_mat_iq2_k_r4_q8_k<8>;
+            expected_typeB = GGML_TYPE_Q8_K;
+            break;
         case GGML_TYPE_IQ3_K_R4:
             assert (ne00 % QK_K == 0);
             mm.funcs[0] = mul_mat_iq3_k_r4_q8_k<1>;
@@ -8969,11 +9084,19 @@ void mul_mat_iq4_xs_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& i
     }
 }
 
-template <int nrc_y>
+template <int nrc_y, bool is_iq2k>
 inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8x16x4_t& i8scales, uint8x16_t extra,
-        uint8x16_t ms, int32x4_t * isum) {
-    auto s8_1 = vmulq_s8(i8scales.val[0], vandq_u8(ms, vshlq_n_u8(extra, 2)));
-    auto s8_2 = vmulq_s8(i8scales.val[1], vandq_u8(ms, extra));
+        int32x4_t * isum) {
+    auto ms = is_iq2k ? vdupq_n_s8(5) : vdupq_n_s8(4);
+    int8x16_t s8_1, s8_2;
+    if constexpr (is_iq2k) {
+        auto m1 = vdupq_n_u8(1);
+        s8_1 = vmulq_s8(i8scales.val[0], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
+        s8_2 = vmulq_s8(i8scales.val[1], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
+    } else {
+        s8_1 = vmulq_s8(i8scales.val[0], vandq_u8(ms, vshlq_n_u8(extra, 2)));
+        s8_2 = vmulq_s8(i8scales.val[1], vandq_u8(ms, extra));
+    }
     auto s16_1 = vmovl_s8(vget_low_s8 (s8_1));
     auto s16_2 = vmovl_s8(vget_high_s8(s8_1));
     auto s16_3 = vmovl_s8(vget_low_s8 (s8_2));
@@ -8990,8 +9113,14 @@ inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8
         isum[iy] = vmlal_lane_s16(isum[iy], vget_low_s16 (s16_4), b8, 2);
         isum[iy] = vmlal_lane_s16(isum[iy], vget_high_s16(s16_4), b8, 3);
     }
-    s8_1 = vmulq_s8(i8scales.val[2], vandq_u8(ms, vshrq_n_u8(extra, 2)));
-    s8_2 = vmulq_s8(i8scales.val[3], vandq_u8(ms, vshrq_n_u8(extra, 4)));
+    if constexpr (is_iq2k) {
+        auto m1 = vdupq_n_u8(1);
+        s8_1 = vmulq_s8(i8scales.val[2], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
+        s8_2 = vmulq_s8(i8scales.val[3], vandq_s8(ms, vceqq_u8(vandq_u8(extra, m1), m1))); extra = vshrq_n_u8(extra, 2);
+    } else {
+        s8_1 = vmulq_s8(i8scales.val[2], vandq_u8(ms, vshrq_n_u8(extra, 2)));
+        s8_2 = vmulq_s8(i8scales.val[3], vandq_u8(ms, vshrq_n_u8(extra, 4)));
+    }
     s16_1 = vmovl_s8(vget_low_s8 (s8_1));
     s16_2 = vmovl_s8(vget_high_s8(s8_1));
     s16_3 = vmovl_s8(vget_low_s8 (s8_2));
@@ -9011,6 +9140,111 @@ inline void iq3_4_add_shift(int ibl, const Q8<nrc_y, block_q8_K>& q8, const int8
 }
 
 template <int nrc_y>
+void mul_mat_iq2_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 = vdupq_n_u8(0xf);
+    auto m03 = vdupq_n_u8(0x03);
+    auto ms = vdupq_n_u8(4);
+    uint8x16x2_t shift_shuffle = {
+        vreinterpretq_u8_u64(uint64x2_t{0x0101010100000000, 0x0303030302020202}),
+        vreinterpretq_u8_u64(uint64x2_t{0x0505050504040404, 0x0707070706060606})
+    };
+    auto values8 = vld1_s8(iq2nl_values);
+    auto values = vcombine_s8(values8, values8);
+    int nbl = n / QK_K;
+    int8x16_t qx[4];
+    int8x16x4_t i8scales;
+    int16x8x4_t i16scales;
+    float32x4_t acc[nrc_y] = {};
+    for (int ix = 0; ix < nrc_x; ix += 4) {
+        const block_iq2_k_r4 * iq2 = (const block_iq2_k_r4 *)((const char *)vx + ix*bx);
+        for (int ibl = 0; ibl < nbl; ++ibl) {
+            auto d4 = vcvt_f32_f16(vld1_f16((const float16_t *)iq2[ibl].d));
+            auto extra8 = vld1_u8(iq2[ibl].extra);
+            uint8x16_t extra;
+            if constexpr (nrc_y == 1) {
+                extra = vcombine_u8(extra8, vshr_n_u8(extra8,1));
+            } else {
+                extra = vcombine_u8(extra8, extra8);
+            }
+            auto sl = vld1q_u8_x2(iq2[ibl].scales);
+            i8scales.val[0] = vaddq_s8(vandq_u8(sl.val[0],  m4), vdupq_n_s8(-8));
+            i8scales.val[1] = vaddq_s8(vandq_u8(sl.val[1],  m4), vdupq_n_s8(-8));
+            i8scales.val[2] = vaddq_s8(vshrq_n_u8(sl.val[0], 4), vdupq_n_s8(-8));
+            i8scales.val[3] = vaddq_s8(vshrq_n_u8(sl.val[1], 4), vdupq_n_s8(-8));
+            int32x4_t isum[nrc_y] = {};
+            if constexpr (nrc_y == 1) {
+                iq3_4_add_shift<nrc_y, true>(ibl, q8, i8scales, extra, isum);
+            }
+            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 scales = vmovl_s16(vget_low_s16 (i16scales.val[ib]));
+                    auto bits = vld1q_u8_x2(iq2[ibl].qs + 128*is + 32*ib);
+                    qx[0] = vandq_u8(           bits.val[0],     m03);
+                    qx[1] = vandq_u8(vshrq_n_u8(bits.val[0], 2), m03);
+                    qx[2] = vandq_u8(vshrq_n_u8(bits.val[0], 4), m03);
+                    qx[3] = vandq_u8(vshrq_n_u8(bits.val[0], 6), m03);
+                    uint8x16_t shifts;
+                    if constexpr (nrc_y == 1) {
+                        qx[0] = vqtbl1q_s8(values, qx[0]);  //  0...3 from the 4 rows
+                        qx[1] = vqtbl1q_s8(values, qx[1]);  //  4...7
+                        qx[2] = vqtbl1q_s8(values, qx[2]);  //  8..11
+                        qx[3] = vqtbl1q_s8(values, qx[3]);  // 12..15
+                    } else {
+                        shifts = vandq_u8(ms, vshlq_n_u8(extra, 2));
+                        auto shift = vqtbl1q_u8(shifts, shift_shuffle.val[0]);
+                        extra = vshrq_n_u8(extra, 1);
+                        qx[0] = vqtbl1q_s8(values, vaddq_u8(shift, qx[0]));  //  0...3 from the 4 rows
+                        qx[1] = vqtbl1q_s8(values, vaddq_u8(shift, qx[1]));  //  4...7
+                        qx[2] = vqtbl1q_s8(values, vaddq_u8(shift, qx[2]));  //  8..11
+                        qx[3] = vqtbl1q_s8(values, vaddq_u8(shift, qx[3]));  // 12..15
+                    }
+                    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);
+                    }
+                    qx[0] = vandq_u8(           bits.val[1],     m03);
+                    qx[1] = vandq_u8(vshrq_n_u8(bits.val[1], 2), m03);
+                    qx[2] = vandq_u8(vshrq_n_u8(bits.val[1], 4), m03);
+                    qx[3] = vandq_u8(vshrq_n_u8(bits.val[1], 6), m03);
+                    if constexpr (nrc_y == 1) {
+                        qx[0] = vqtbl1q_s8(values, qx[0]);  //  0...3 from the 4 rows
+                        qx[1] = vqtbl1q_s8(values, qx[1]);  //  4...7
+                        qx[2] = vqtbl1q_s8(values, qx[2]);  //  8..11
+                        qx[3] = vqtbl1q_s8(values, qx[3]);  // 12..15
+                    } else {
+                        auto shift = vqtbl1q_u8(shifts, shift_shuffle.val[1]);
+                        qx[0] = vqtbl1q_s8(values, vaddq_u8(shift, qx[0]));  //  0...3 from the 4 rows
+                        qx[1] = vqtbl1q_s8(values, vaddq_u8(shift, qx[1]));  //  4...7
+                        qx[2] = vqtbl1q_s8(values, vaddq_u8(shift, qx[2]));  //  8..11
+                        qx[3] = vqtbl1q_s8(values, vaddq_u8(shift, qx[3]));  // 12..15
+                    }
+                    scales = vmovl_s16(vget_high_s16(i16scales.val[ib]));
+                    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_iq3_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);
@@ -9054,7 +9288,7 @@ void mul_mat_iq3_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& in
             i8scales.val[3] = vmulq_s8(i8scales.val[3], vorrq_u8(vceqq_u8(vandq_u8(sh, smask.val[1]), smask.val[1]), vdupq_n_u8(1)));
             int32x4_t isum[nrc_y] = {};
             if constexpr (nrc_y == 1) {
-                iq3_4_add_shift(ibl, q8, i8scales, extra, ms, isum);
+                iq3_4_add_shift<nrc_y, false>(ibl, q8, i8scales, extra, isum);
             }
             for (int is = 0; is < 2; ++is) {
                 i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
@@ -9161,7 +9395,7 @@ void mul_mat_iq4_k_r4_q8_k(int n, const void * vx, size_t bx, const DataInfo& in
             i8scales.val[3] = vaddq_s8(vorrq_u8(vshrq_n_u8(sl.val[1], 4), vandq_u8(vshrq_n_u8(sh, 2), m3)), m32);
             int32x4_t isum[nrc_y] = {};
             if constexpr (nrc_y == 1) {
-                iq3_4_add_shift(ibl, q8, i8scales, extra, ms, isum);
+                iq3_4_add_shift<nrc_y, false>(ibl, q8, i8scales, extra, isum);
             }
             for (int is = 0; is < 2; ++is) {
                 i16scales.val[0] = vmovl_s8(vget_low_s8 (i8scales.val[2*is+0]));
@@ -10049,6 +10283,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
             SET_MUL_MAT_FUNCTIONS(m, mul_mat_q8_k_r8_q8_k);
             expected_Btype = GGML_TYPE_Q8_KR8;
             break;
+        case GGML_TYPE_IQ2_K_R4:
+            SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq2_k_r4_q8_k);
+            expected_Btype = GGML_TYPE_Q8_K;
+            break;
         case GGML_TYPE_IQ3_K_R4:
             SET_MUL_MAT_FUNCTIONS(m, mul_mat_iq3_k_r4_q8_k);
             expected_Btype = GGML_TYPE_Q8_K;
diff --git a/ggml/src/iqk/iqk_quantize.cpp b/ggml/src/iqk/iqk_quantize.cpp
index 373a15bb..3077fe21 100644
--- a/ggml/src/iqk/iqk_quantize.cpp
+++ b/ggml/src/iqk/iqk_quantize.cpp
@@ -4931,3 +4931,126 @@ void vec_dot_iq3_k_r4_q8_k(int n, float * s, size_t bs, const void * vx, size_t
     GGML_UNUSED(by);
 }
 
+//
+// ========================================= iq2_k_r4
+//
+
+void quantize_row_iq2_k_r4_ref(const float * x, block_iq2_k_r4 * y, int64_t k) {
+    quantize_iq2_k_r4(x, (void *)y, 4, k/4, nullptr);
+}
+
+void quantize_row_iq2_k_r4(const float * x, void * y, int64_t k) {
+    quantize_iq2_k_r4(x, y, 4, k/4, nullptr);
+}
+
+namespace {
+inline void convert_iq2_k(const block_iq2_k& x, uint8_t * L) {
+    const uint8_t * qs = x.qs;
+    for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+        int shift_l = 2*(ib32%4);
+        for (int j = 0; j < 16; ++j) {
+            L[j+ 0] = ((qs[j+ 0] >> shift_l) & 3);
+            L[j+16] = ((qs[j+16] >> shift_l) & 3);
+        }
+        L += 32;
+        if (shift_l == 6) qs += 32;
+    }
+}
+}
+
+static void repack_iq2_k(int nrows, int n_per_row, const block_iq2_k * x, block_iq2_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_iq2_k * x4[4];
+    uint8_t L[QK_K];
+    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].extra, 0, 8);
+            std::memset(y[ibl].scales, 0, QK_K/8);
+            for (int k = 0; k < 4; ++k) {
+                y[ibl].d[k] = x4[k][ibl].d;
+                auto extra = x4[k][ibl].extra;
+                convert_iq2_k(x4[k][ibl], L);
+                for (int ib = 0; ib < QK_K/32; ++ib) {
+                    if (extra & 1) y[ibl].extra[k+0] |= (1 << ib);
+                    if (extra & 2) y[ibl].extra[k+4] |= (1 << ib);
+                    extra >>= 2;
+                    uint8_t sl1 = x4[k][ibl].scales[ib] & 0xf;
+                    uint8_t sl2 = x4[k][ibl].scales[ib] >>  4;
+                    int i = 8*ib + k;
+                    y[ibl].scales[i%32] |= (sl1 << 4*(i/32));
+                    i += 4;
+                    y[ibl].scales[i%32] |= (sl2 << 4*(i/32));
+                    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);
+                    }
+                }
+            }
+        }
+        x += 4*nblock;
+        y += nblock;
+    }
+}
+
+size_t quantize_iq2_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_IQ2_K, n_per_row);
+    std::vector<char> qtmp(4*row_size);
+    for (int row = 0; row < nrows; row += 4) {
+        quantize_iq2_k(src, (void *)qtmp.data(), 4, n_per_row, imatrix);
+        repack_iq2_k(4, n_per_row, (const block_iq2_k *)qtmp.data(), (block_iq2_k_r4 *)qcur);
+        qcur += 4*row_size;
+        src += 4*n_per_row;
+    }
+    return nrows*row_size;
+}
+
+void dequantize_row_iq2_k_r4(const block_iq2_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;
+            for (int ib = 0; ib < QK_K/32; ++ib) {
+                int is = 8*ib + k;
+                float dl1 = d * (((x[ibl].scales[is%32] >> 4*(is/32)) & 0xf) - 8);
+                is += 4;
+                float dl2 = d * (((x[ibl].scales[is%32] >> 4*(is/32)) & 0xf) - 8);
+                auto values1 = iq2nl_values + (x[ibl].extra[k+0] & (1 << ib) ? 4 : 0);
+                auto values2 = iq2nl_values + (x[ibl].extra[k+4] & (1 << ib) ? 4 : 0);
+                for (int i = 0; i < 4; ++i) {
+                    y4[k][QK_K*ibl+32*ib+i+ 0] = dl1 * values1[(ql[4*k+i+ 0] >> 0) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+ 4] = dl1 * values1[(ql[4*k+i+ 0] >> 2) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+ 8] = dl1 * values1[(ql[4*k+i+ 0] >> 4) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+12] = dl1 * values1[(ql[4*k+i+ 0] >> 6) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+16] = dl2 * values2[(ql[4*k+i+16] >> 0) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+20] = dl2 * values2[(ql[4*k+i+16] >> 2) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+24] = dl2 * values2[(ql[4*k+i+16] >> 4) & 3];
+                    y4[k][QK_K*ibl+32*ib+i+28] = dl2 * values2[(ql[4*k+i+16] >> 6) & 3];
+                }
+                ql += 32;
+            }
+        }
+    }
+}
+
+void vec_dot_iq2_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_IQ2_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 1ca66bd8..8640b59b 100644
--- a/ggml/src/iqk/iqk_quantize.h
+++ b/ggml/src/iqk/iqk_quantize.h
@@ -151,6 +151,12 @@ size_t quantize_iq3_k_r4(const float * GGML_RESTRICT src, void * GGML_RESTRICT d
 void   dequantize_row_iq3_k_r4(const block_iq3_k_r4  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 void   vec_dot_iq3_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_iq2_k_r4_ref(const float * GGML_RESTRICT x, block_iq2_k_r4  * GGML_RESTRICT y, int64_t k);
+void   quantize_row_iq2_k_r4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+size_t quantize_iq2_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_iq2_k_r4(const block_iq2_k_r4  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void   vec_dot_iq2_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_q8_k_r8_ref(const float * GGML_RESTRICT x, block_q8_k_r8  * GGML_RESTRICT y, int64_t k);
 void   quantize_row_q8_k_r8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 size_t quantize_q8_k_r8(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 026cf08e..1627a752 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -193,6 +193,7 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_Q6_0_R4       = 335, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_BF16_R16      = 232, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ2_BN_R4     = 337, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ2_K_R4      = 338, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ3_K_R4      = 339, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ4_K_R4      = 340, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q8_K_R8       = 399, // except 1d tensors
diff --git a/src/llama.cpp b/src/llama.cpp
index 16579e99..62ab4d08 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3866,6 +3866,7 @@ struct llama_model_loader {
                 case GGML_TYPE_IQ4_KS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS;  break;
                 case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
                 case GGML_TYPE_IQ2_K:   ftype = LLAMA_FTYPE_MOSTLY_IQ2_K;   break;
+                case GGML_TYPE_IQ2_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_K_R4;break;
                 case GGML_TYPE_IQ3_K:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_K;   break;
                 case GGML_TYPE_IQ3_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ3_K_R4;break;
                 case GGML_TYPE_IQ4_K:   ftype = LLAMA_FTYPE_MOSTLY_IQ4_K;   break;
@@ -4585,6 +4586,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_IQ4_KS:   return "IQ4_KS - 4.25 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ4_KSS:  return "IQ4_KSS - 4.0 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ2_K:    return "IQ2_K - 2.375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_K_R4: return "IQ2_K_R4 - 2.375 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ3_K:    return "IQ3_K - 3.4325 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ3_K_R4: return "IQ3_K_R4 - 3.4325 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ3_KL:   return "IQ3_KL - 4 bpw";
@@ -15765,7 +15767,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
                      ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
                      ftype == LLAMA_FTYPE_MOSTLY_IQ1_M   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K   ||
-                     ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4) {
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) {
                 new_type = !qs.has_output ? GGML_TYPE_IQ4_K : GGML_TYPE_Q5_K;
             }
             else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
@@ -15822,6 +15824,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             else if (new_type == GGML_TYPE_Q8_K_R8) {
                 new_type = GGML_TYPE_Q8_0;
             }
+            else if (new_type == GGML_TYPE_IQ2_K_R4) {
+                new_type = GGML_TYPE_IQ2_K;
+            }
             else if (new_type == GGML_TYPE_IQ3_K_R4) {
                 new_type = GGML_TYPE_IQ3_K;
             }
@@ -15881,6 +15886,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K) {
             new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ4_K : GGML_TYPE_IQ3_K;
         }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) {
+            new_type = qs.model.hparams.n_gqa() >= 2 ? GGML_TYPE_IQ4_K_R4 : GGML_TYPE_IQ3_K_R4;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
             new_type = GGML_TYPE_Q4_K;
         }
@@ -16047,7 +16055,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                     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_Q3_K_R4 ||
-                    ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4|| ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4) {
+                    ftype == LLAMA_FTYPE_MOSTLY_Q2_K_R4|| ftype == LLAMA_FTYPE_MOSTLY_IQ4_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 ||
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) {
                     new_type = GGML_TYPE_Q5_K;
                 }
             } else {
@@ -16057,6 +16066,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
                 else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_IQ4_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K  ) new_type = GGML_TYPE_IQ3_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4) new_type = GGML_TYPE_IQ3_K_R4;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_KL ) new_type = GGML_TYPE_IQ4_KS;
             }
         } else {
@@ -16118,7 +16128,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         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_Q3_K_R4 || new_type == GGML_TYPE_Q2_K_R4 ||
-        new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4) {
+        new_type == GGML_TYPE_IQ4_K_R4|| new_type == GGML_TYPE_Q8_K_R8 || new_type == GGML_TYPE_IQ3_K_R4||
+        new_type == GGML_TYPE_IQ2_K_R4) {
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];
         if (nx % QK_K != 0) {
@@ -16149,6 +16160,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             case GGML_TYPE_Q3_K:
             case GGML_TYPE_Q3_K_R4:
             case GGML_TYPE_IQ2_K:
+            case GGML_TYPE_IQ2_K_R4:
             case GGML_TYPE_IQ3_K:
             case GGML_TYPE_IQ3_K_R4:
             case GGML_TYPE_IQ4_KSS:
@@ -16285,6 +16297,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_IQ4_KS:  default_type = GGML_TYPE_IQ4_KS;  break;
         case LLAMA_FTYPE_MOSTLY_IQ4_KSS: default_type = GGML_TYPE_IQ4_KSS; break;
         case LLAMA_FTYPE_MOSTLY_IQ2_K:   default_type = GGML_TYPE_IQ2_K;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_K_R4:default_type = GGML_TYPE_IQ2_K_R4;break;
         case LLAMA_FTYPE_MOSTLY_IQ3_K:   default_type = GGML_TYPE_IQ3_K;   break;
         case LLAMA_FTYPE_MOSTLY_IQ3_K_R4:default_type = GGML_TYPE_IQ3_K_R4;break;
         case LLAMA_FTYPE_MOSTLY_IQ3_KL:  default_type = GGML_TYPE_IQ3_K;   break;
@@ -16695,6 +16708,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_BN;
                 else chunk_size_multiplier = 4;
             }
+            else if (new_type == GGML_TYPE_IQ2_K_R4) {
+                if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ2_K;
+                else chunk_size_multiplier = 4;
+            }
             else if (new_type == GGML_TYPE_IQ3_K_R4) {
                 if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ3_K;
                 else chunk_size_multiplier = 4;