kimi-k2 convert script and chat template (#612)

* convert_hf_to_gguf for Kimi-K2-Instruct

Adapt mainline `PR14653` for tokenizer while maintaining proper MLA
tensors. Tested with this workflow using deepseek fp8_cast_bf16.py and
triton-cpu to upcast the fp8 safetensors to bf16 safetensors then used
this convert_hf_to_gguf.

* Add Kimi-K2 chat template

moonshotai/Kimi-K2-Instruct

https://github.com/ikawrakow/ik_llama.cpp/pull/609#issuecomment-3071259454

* kimi-k2 add ass to template to get response

convert_hf_to_gguf.py

@@ -639,6 +639,9 @@ class Model:
         if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec":
             # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base
             res = "seed-coder"
+        if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
+            # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
+            res = "kimi-k2"
 
         if res is None:
             logger.warning("\n")
@@ -3379,6 +3382,60 @@ class DeepseekV2Model(Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
     def set_vocab(self):
+
+        if self.hparams["vocab_size"] == 163840:  # Kimi-K2 model
+            from transformers import AutoTokenizer
+
+            tokenizer = AutoTokenizer.from_pretrained(
+                self.dir_model, trust_remote_code=True
+            )
+            tokpre = self.get_vocab_base_pre(tokenizer)
+
+            # Build merges list using the approach similar to HunYuanMoE
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.model._mergeable_ranks
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(
+                        " ".join(map(QwenModel.token_bytes_to_string, merged))
+                    )
+
+            # Build token list
+            vocab_size = self.hparams["vocab_size"]
+            special_tokens = tokenizer.special_tokens
+            reverse_vocab = {
+                id_: encoded_tok
+                for encoded_tok, id_ in {**vocab, **special_tokens}.items()
+            }
+            tokens: list[str] = []
+            toktypes: list[int] = []
+
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+        else:
         self._set_vocab_gpt2()
 
     def set_gguf_parameters(self):

convert_hf_to_gguf_update.py

@@ -96,6 +96,7 @@ models = [
     {"name": "smollm",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
     {"name": "deepseek-v3",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
     {"name": "seed-coder",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
+    {"name": "kimi-k2",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base", "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890", },
 ]
 
 

src/llama.cpp

@@ -1695,6 +1695,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_BITNET,
     LLM_CHAT_TEMPLATE_DOTS1,
     LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
+    LLM_CHAT_TEMPLATE_KIMI_K2,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 
@@ -1733,6 +1734,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "megrez",            LLM_CHAT_TEMPLATE_MEGREZ            },
     { "llama4",            LLM_CHAT_TEMPLATE_LLAMA4            },
     { "hunyuan-moe",       LLM_CHAT_TEMPLATE_HUNYUAN_MOE       },
+    { "kimi-k2",           LLM_CHAT_TEMPLATE_KIMI_K2           },
     { "bitnet",            LLM_CHAT_TEMPLATE_BITNET            },
 };
 
@@ -23270,6 +23272,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_DOTS1;
     } else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
         return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
+    } else if (tmpl_contains("<|im_middle|>") && tmpl_contains("<|im_end|>")) {
+        return LLM_CHAT_TEMPLATE_KIMI_K2;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -23715,6 +23719,21 @@ static int32_t llama_chat_apply_template_internal(
                 ss << "<|startoftext|>" << message->content << "<|extra_0|>";
             }
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_KIMI_K2) {
+        // moonshotai/Kimi-K2-Instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|im_system|>system<|im_middle|>" << message->content << "<|im_end|>";
+            } else if (role == "assistant") {
+                ss << "<|im_user|>user<|im_middle|>" << message->content << "<|im_end|>";
+            } else {
+                ss << "<|im_assistant|>assistant<|im_middle|>" << message->content << "<|im_end|>";
+            }
+        }
+        if (add_ass) {
+            ss << "<|im_assistant|>assistant<|im_middle|>";
+        }
     } else {
         // template not supported
         return -1;