diff --git a/include/llama.h b/include/llama.h
index 14e12d7c51..f745e2110b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -366,6 +366,7 @@ extern "C" {
         void * tensor_types;                  // pointer to vector containing tensor types
         void * prune_layers;                  // pointer to vector containing layer indices to prune
         float target_bpw;                     // target bits per weight (bpw)
+        bool keep_bpw_state;                  // keep bpw state file
     } llama_model_quantize_params;
 
     typedef struct llama_logit_bias {
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 56e63f9bb7..4b243f1f55 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -659,7 +659,6 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
         GGML_TYPE_IQ2_S,
         GGML_TYPE_Q2_K,
         GGML_TYPE_IQ3_XXS,
-        GGML_TYPE_IQ3_S,
         GGML_TYPE_Q3_K,
         GGML_TYPE_IQ4_XS,
         GGML_TYPE_IQ4_NL,
@@ -773,11 +772,9 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
     auto save_bpw_state = [&](const std::vector<tensor_info> & all_vec) {
         const std::string tmp = checkpoint_file + ".tmp";
         std::ofstream ofs(tmp, std::ios::binary | std::ios::trunc);
-        if (!ofs) { return; } // best-effort
-        const float target_bpw = params->target_bpw;
+        if (!ofs) { return; }
         ofs.write((const char *)&file_magic, sizeof(file_magic));
         ofs.write((const char *)&model_id, sizeof(model_id));
-        ofs.write((const char *)&target_bpw, sizeof(target_bpw));
         const uint64_t n = all_vec.size();
         ofs.write((const char *)&n, sizeof(n));
         for (const auto & ti : all_vec) {
@@ -817,19 +814,14 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
 
         uint32_t magic = 0;
         uint64_t id = 0;
-        float bpw = 0.0f;
         ifs.read((char *)&magic, sizeof(magic));
         ifs.read((char *)&id, sizeof(id));
-        ifs.read((char *)&bpw, sizeof(bpw));
         if (magic != file_magic) {
             LLAMA_LOG_WARN("%s: invalid resume file, ignoring: %s\n", func, checkpoint_file.c_str());
             return out;
         } else if (id != model_id) {
             LLAMA_LOG_WARN("%s: model ID mismatch, ignoring: %s\n", func, checkpoint_file.c_str());
             return out;
-        } else if (bpw != params->target_bpw) {
-            LLAMA_LOG_WARN("%s: target bpw of %f does not match %f, ignoring: %s\n", func, params->target_bpw, bpw, checkpoint_file.c_str());
-            return out;
         } else {
             LLAMA_LOG_INFO("%s: resuming tensor quantization\n", func);
         }
@@ -874,7 +866,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
 
     auto delete_bpw_state = [&] {
         std::ifstream ifs(checkpoint_file);
-        if (ifs.good()) {
+        if (ifs.good() && !params->keep_bpw_state) {
             LLAMA_LOG_INFO("%s: deleting %s\n", func, checkpoint_file.c_str());
             std::remove(checkpoint_file.c_str());
         }
@@ -1489,6 +1481,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
     }
 
     check_signal_handler(all);
+    if (params->keep_bpw_state) { save_bpw_state(all); }
 
     if (all.empty()) { return {}; }
 
@@ -2240,7 +2233,8 @@ llama_model_quantize_params llama_model_quantize_default_params() {
         /*.kv_overrides                =*/ nullptr,
         /*.tensor_type                 =*/ nullptr,
         /*.prune_layers                =*/ nullptr,
-        /*.target_bpw                  =*/ -1.0f
+        /*.target_bpw                  =*/ -1.0f,
+        /*.keep_bpw_state              =*/ false
     };
 
     return result;
diff --git a/tools/quantize/quantize.cpp b/tools/quantize/quantize.cpp
index c254c3f6b2..ad2563a48d 100644
--- a/tools/quantize/quantize.cpp
+++ b/tools/quantize/quantize.cpp
@@ -118,7 +118,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 [[noreturn]]
 static void usage(const char * executable) {
     printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights]\n", executable);
-    printf("       [--target-bpw n] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--prune-layers] [--keep-split] [--override-kv]\n");
+    printf("       [--target-bpw n] [--keep-bpw-state] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--prune-layers] [--keep-split] [--override-kv]\n");
     printf("       model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
     printf("  --allow-requantize: allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
     printf("  --leave-output-tensor: will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
@@ -134,6 +134,7 @@ static void usage(const char * executable) {
     printf("      Advanced option to remove all tensors from the given layers\n");
     printf("  --target-bpw: target bits per weight (bpw). Must be a positive number between 0.0 and 8.0\n");
     printf("      Advanced option to automatically select quantization types to achieve a total bits per weight (bpw) target\n");
+    printf("  --keep-bpw-state: preserve the bpw computations in a state file\n");
     printf("  --keep-split: will generate quantized model in the same shards as input\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
@@ -557,6 +558,8 @@ int main(int argc, char ** argv) {
             if (arg_idx == argc-1 || !parse_target_bpw(argv[++arg_idx], target_bpw)) {
                 usage(argv[0]);
             }
+        } else if (strcmp(argv[arg_idx], "--keep-bpw-state") == 0) {
+            params.keep_bpw_state = true;
         } else if (strcmp(argv[arg_idx], "--prune-layers") == 0) {
             if (arg_idx == argc-1 || !parse_layer_prune(argv[++arg_idx], prune_layers)) {
                 usage(argv[0]);