diff --git a/README.md b/README.md
index 81f29eeb4..36ab60451 100644
--- a/README.md
+++ b/README.md
@@ -26,6 +26,7 @@ What does it mean? You get llama.cpp with a fancy UI, persistent stories, editin
 - No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields. Python will ALWAYS provide the memory, we just write to it.
 - No external libraries or dependencies. That means no Flask, Pybind and whatever. All You Need Is Python.
 - Since v1.0.6, requires libopenblas, the prebuilt windows binaries are included in this repo. If not found, it will fall back to a mode without BLAS. If you want you can also link your own install of OpenBLAS manually with `LLAMA_OPENBLAS=1`
+- I plan to keep backwards compatibility with all past ggml llama.cpp AND alpaca.cpp models.
 
 ## License
 - The original GGML library and llama.cpp by ggerganov are licensed under the MIT License
diff --git a/extra.cpp b/extra.cpp
index da3ea0dff..95618aa2b 100644
--- a/extra.cpp
+++ b/extra.cpp
@@ -1,4 +1,4 @@
-
+#include "ggml.h"
 #include "extra.h"
 #include "llama.cpp"
 
@@ -43,8 +43,539 @@
     return fileformat;
  }
 
+ //legacy llama model format v1 and v2 loader. there is a lot of duplicate code, 
+ //but it may be better to freeze it as such rather than risk tiny breaking changes
+ static bool legacy_llama_model_load(
+        const std::string & fname,
+        llama_context & lctx,
+        int n_ctx,
+        int n_parts,
+        ggml_type memory_type,
+        bool vocab_only,
+        llama_progress_callback progress_callback,
+        void *progress_callback_user_data) {
+    fprintf(stderr, "%s: Legacy loading model from '%s' - please wait ...\n", __func__, fname.c_str());
 
+    const int64_t t_start_us = ggml_time_us();
 
+    lctx.t_start_us = t_start_us;
+
+    std::vector<char> f_buf(1024*1024);
+
+    auto & model = lctx.model;
+    auto & vocab = lctx.vocab;
+
+    auto fin = std::ifstream(fname, std::ios::binary);
+    fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
+    if (!fin) {
+        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
+    bool legacy_file_format = false;
+    // verify magic
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        if (magic == 0x67676d6c) { // 'ggml' in hex, very first version
+            fprintf(stderr, "%s: very old v1 model file '%s' (please regenerate your model files if you can!)\n",
+                    __func__, fname.c_str());
+            legacy_file_format = true;
+        }
+        else
+        {
+        if (magic != 0x67676d66) { // 'ggmf' in hex, second version
+            fprintf(stderr, "%s: invalid legacy model file '%s' (bad magic)\n", __func__, fname.c_str());
+            return false;
+        }
+
+        uint32_t format_version;
+        fin.read((char *) &format_version, sizeof(format_version));
+
+        uint32_t v2_format_version = 1;
+        if (format_version != v2_format_version) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
+                    __func__, fname.c_str(), format_version, v2_format_version);
+            return false;
+        }
+        }
+    }
+
+    int n_ff = 0;
+
+    // load hparams
+    {
+        auto & hparams = model.hparams;
+
+        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        //fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        fin.read((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
+        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        fin.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
+        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));
+
+        hparams.n_ctx = n_ctx;
+
+        n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
+
+        if (n_parts < 1) {
+            n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
+        }
+
+        // temp warning to tell the user to use "--n_parts"
+        if (hparams.f16 == 4 && n_parts != 1) {
+            fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts);
+            fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
+        }
+
+        if (hparams.n_layer == 32) {
+            model.type = e_model::MODEL_7B;
+        }
+
+        if (hparams.n_layer == 40) {
+            model.type = e_model::MODEL_13B;
+        }
+
+        if (hparams.n_layer == 60) {
+            model.type = e_model::MODEL_30B;
+        }
+
+        if (hparams.n_layer == 80) {
+            model.type = e_model::MODEL_65B;
+        }
+
+        fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        fprintf(stderr, "%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        fprintf(stderr, "%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        fprintf(stderr, "%s: n_mult  = %d\n", __func__, hparams.n_mult);
+        fprintf(stderr, "%s: n_head  = %d\n", __func__, hparams.n_head);
+        fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
+        fprintf(stderr, "%s: n_rot   = %d\n", __func__, hparams.n_rot);
+        fprintf(stderr, "%s: f16     = %d\n", __func__, hparams.f16);
+        fprintf(stderr, "%s: n_ff    = %d\n", __func__, n_ff);
+        fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
+        fprintf(stderr, "%s: type    = %d\n", __func__, model.type);
+    }
+
+    // load vocab
+    {
+        std::string word;
+        vocab.id_to_token.resize(model.hparams.n_vocab);
+        std::vector<char> tmp(64);
+
+        for (int i = 0; i < model.hparams.n_vocab; i++) {
+            uint32_t len;
+            fin.read((char *) &len, sizeof(len));
+
+            word.resize(len);
+            if (len > 0) {
+                tmp.resize(len);
+                fin.read(tmp.data(), len);
+                word.assign(tmp.data(), len);
+            } else {
+                word.clear();
+            }
+
+            float score;
+            if(!legacy_file_format)
+            {
+            fin.read((char *) &score, sizeof(score));
+            }
+
+            vocab.token_to_id[word] = i;
+
+            auto &tok_score = vocab.id_to_token[i];
+            tok_score.tok = word;
+            tok_score.score = score;
+        }
+    }
+
+    if (vocab_only) {
+        return true;
+    }
+
+    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
+    // in order to save memory and also to speed up the computation
+    // wtype is for per-layer weights, while vtype is for other weights
+    ggml_type wtype, vtype;
+    switch (model.hparams.f16) {
+        case 0: wtype = vtype = GGML_TYPE_F32;  break;
+        case 1: wtype = vtype = GGML_TYPE_F16;  break;
+        case 2: wtype = vtype = GGML_TYPE_Q4_0; break;
+        case 3: wtype = vtype = GGML_TYPE_Q4_1; break;
+        case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break;
+        default:
+                {
+                    fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
+                            __func__, fname.c_str(), model.hparams.f16);
+                    return false;
+                }
+    }
+
+    auto & ctx = model.ctx;
+
+    size_t ctx_size = 0;
+
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
+
+        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
+
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
+
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo
+
+        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm
+
+        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
+        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
+        ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
+
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
+
+        ctx_size += (5 + 10*n_layer)*256; // object overhead
+
+        fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+    }
+
+    // print memory requirements
+    {
+        const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
+
+        // this is the total memory required to run the inference
+        const size_t mem_required =
+            ctx_size +
+            MEM_REQ_SCRATCH0.at(model.type) +
+            MEM_REQ_SCRATCH1.at(model.type) +
+            MEM_REQ_EVAL.at    (model.type);
+
+        // this is the memory required by one llama_state
+        const size_t mem_required_state =
+            scale*MEM_REQ_KV_SELF.at(model.type);
+
+        fprintf(stderr, "%s: mem required  = %7.2f MB (+ %7.2f MB per state)\n", __func__,
+                mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
+    }
+
+    // create the ggml context
+    {
+        lctx.model.buf.resize(ctx_size);
+
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ lctx.model.buf.size(),
+            /*.mem_buffer =*/ lctx.model.buf.data(),
+        };
+
+        model.ctx = ggml_init(params);
+        if (!model.ctx) {
+            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            return false;
+        }
+    }
+
+    // prepare memory for the weights
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_vocab = hparams.n_vocab;
+
+        model.layers.resize(n_layer);
+
+        model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
+
+        model.norm   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        model.output = ggml_new_tensor_2d(ctx, vtype,         n_embd, n_vocab);
+
+        // map by name
+        model.tensors["tok_embeddings.weight"] = model.tok_embeddings;
+
+        model.tensors["norm.weight"]   = model.norm;
+        model.tensors["output.weight"] = model.output;
+
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = model.layers[i];
+
+            layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+            layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+            layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+
+            layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+            layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);
+            layer.w2 = ggml_new_tensor_2d(ctx, wtype,   n_ff, n_embd);
+            layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd,   n_ff);
+
+            // map by name
+            model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;
+
+            model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;
+            model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;
+            model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;
+            model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;
+
+            model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;
+
+            model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
+            model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
+            model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;
+        }
+    }
+
+    const size_t file_offset = fin.tellg();
+
+    fin.close();
+
+    std::vector<uint8_t> tmp;
+
+    if (progress_callback) {
+        progress_callback(0.0, progress_callback_user_data);
+    }
+
+    for (int i = 0; i < n_parts; ++i) {
+        const int part_id = i;
+        //const int part_id = n_parts - i - 1;
+
+        std::string fname_part = fname;
+        if (i > 0) {
+            fname_part += "." + std::to_string(i);
+        }
+
+        fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
+
+        fin = std::ifstream(fname_part, std::ios::binary);
+        fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
+
+        fin.seekg(0, fin.end);
+        const size_t file_size = fin.tellg();
+
+        fin.seekg(file_offset);
+
+        // load weights
+        {
+            size_t total_size = 0;
+
+            model.n_loaded = 0;
+
+            fprintf(stderr, "%s: ", __func__);
+
+            while (true) {
+                int32_t n_dims;
+                int32_t length;
+                int32_t ftype;
+
+                fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+                fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+                fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+
+                if (fin.eof()) {
+                    break;
+                }
+
+                int32_t nelements = 1;
+                int32_t ne[2] = { 1, 1 };
+                for (int i = 0; i < n_dims; ++i) {
+                    fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+                    nelements *= ne[i];
+                }
+
+                std::string name(length, 0);
+                fin.read(&name[0], length);
+
+                if (model.tensors.find(name.data()) == model.tensors.end()) {
+                    fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
+                    return false;
+                }
+
+                // split_type = 0: split by columns
+                // split_type = 1: split by rows
+                int split_type = 0;
+
+                // split_type = 0:
+                // regex:
+                //   - tok_embeddings.*
+                //   - layers.*.attention.wo.weight
+                //   - layers.*.feed_forward.w2.weight
+
+                // split_type = 1:
+                // regex:
+                //   - output.*
+                //   - layers.*.attention.wq.weight
+                //   - layers.*.attention.wk.weight
+                //   - layers.*.attention.wv.weight
+                //   - layers.*.feed_forward.w1.weight
+                //   - layers.*.feed_forward.w3.weight
+                if (name.find("tok_embeddings") != std::string::npos) {
+                    split_type = 0;
+                } else if (name.find("layers") != std::string::npos) {
+                    if (name.find("attention.wo.weight") != std::string::npos) {
+                        split_type = 0;
+                    } else if (name.find("feed_forward.w2.weight") != std::string::npos) {
+                        split_type = 0;
+                    } else {
+                        split_type = 1;
+                    }
+                } else if (name.find("output") != std::string::npos) {
+                    split_type = 1;
+                }
+
+                auto tensor = model.tensors[name.data()];
+
+                if (n_dims == 1) {
+                    if (ggml_nelements(tensor) != nelements) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                        return false;
+                    }
+                } else {
+                    if (ggml_nelements(tensor)/n_parts != nelements) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                        return false;
+                    }
+                }
+
+                if (n_dims == 1) {
+                    if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
+                        return false;
+                    }
+                } else {
+                    if (split_type == 0) {
+                        if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
+                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                    __func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
+                            return false;
+                        }
+                    } else {
+                        if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
+                            fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                                    __func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
+                            return false;
+                        }
+                    }
+                }
+
+                if (0) {
+                    static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
+                    fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
+                }
+
+                size_t bpe = 0;
+
+                switch (ftype) {
+                    case 0: bpe = ggml_type_size(GGML_TYPE_F32);  break;
+                    case 1: bpe = ggml_type_size(GGML_TYPE_F16);  break;
+                    case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
+                    case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
+                    default:
+                            {
+                                fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
+                                return false;
+                            }
+                };
+
+                if (n_dims == 1 || n_parts == 1) {
+                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                                __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+                        return false;
+                    }
+
+                    if (part_id == 0) {
+                        fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+                    } else {
+                        fin.seekg(ggml_nbytes(tensor), std::ios::cur);
+                    }
+
+                    total_size += ggml_nbytes(tensor);
+                } else {
+                    if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
+                        fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                                __func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
+                        return false;
+                    }
+
+                    if (split_type == 0) {
+                        const int np0 = ne[0];
+
+                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+                        assert(row_size == tensor->nb[1]);
+
+                        for (int i1 = 0; i1 < ne[1]; ++i1) {
+                            const size_t offset_row = i1*row_size;
+                            const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+                            fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
+                        }
+                    } else {
+                        const int np1 = ne[1];
+
+                        const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+
+                        for (int i1 = 0; i1 < ne[1]; ++i1) {
+                            const size_t offset_row = (i1 + part_id*np1)*row_size;
+                            fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
+                        }
+                    }
+
+                    total_size += ggml_nbytes(tensor)/n_parts;
+                }
+
+                //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+                model.n_loaded++;
+
+                // progress
+                if (progress_callback) {
+                    float current_file_progress = float(size_t(fin.tellg()) - file_offset) / float(file_size - file_offset);
+                    float current_progress = (float(i) + current_file_progress) / float(n_parts);
+                    progress_callback(current_progress, progress_callback_user_data);
+                }
+                if (model.n_loaded % 8 == 0) {
+                    fprintf(stderr, ".");
+                    fflush(stderr);
+                }
+            }
+
+            fprintf(stderr, " done\n");
+
+            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
+            if (model.n_loaded == 0) {
+                fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
+            } else if (model.n_loaded != (int) model.tensors.size()) {
+                fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
+                return false;
+            }
+        }
+
+        fin.close();
+    }
+
+    lctx.t_load_us = ggml_time_us() - t_start_us;
+
+    if (progress_callback) {
+        progress_callback(1.0, progress_callback_user_data);
+    }
+
+    return true;
+}
 
 // TODO: Calculate this constant from the vocabulary
 #define MAX_TOKEN_LEN 18
diff --git a/extra.h b/extra.h
index c302ef093..ca7eabfd9 100644
--- a/extra.h
+++ b/extra.h
@@ -12,8 +12,10 @@
 #include <vector>
 
 #include "llama.h"
+#include "ggml.h"
 
 
 int check_file_format(const std::string & fname);
 
 std::vector<llama_token> legacy_llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);
+static bool legacy_llama_model_load(const std::string & fname, llama_context & lctx, int n_ctx, int n_parts, ggml_type memory_type, bool vocab_only, llama_progress_callback progress_callback, void *progress_callback_user_data);
\ No newline at end of file