diff options
| -rw-r--r-- | src/llama.cpp | 8 |
1 files changed, 8 insertions, 0 deletions
diff --git a/src/llama.cpp b/src/llama.cpp index cae91619..0aaedb96 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -15756,6 +15756,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; + 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_Q6_K; + } if (qs.model.type == MODEL_70B) { // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with @@ -15796,6 +15800,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { new_type = GGML_TYPE_IQ2_S; } + 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; |