support glm-4-9b-chat
Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>
This commit is contained in:
XingXing Qiao
parent
35c6887f78
commit
de3c909db0
6 changed files with 151 additions and 2 deletions
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@ -483,6 +483,9 @@ class Model:
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if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
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# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
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res = "jina-v2-code"
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if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
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# ref: https://huggingface.co/THUDM/glm-4-9b-chat
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res = "chatglm-bpe"
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if res is None:
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logger.warning("\n")
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@ -2729,7 +2732,7 @@ class DeepseekV2Model(Model):
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class ChatGLMModel(Model):
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model_arch = gguf.MODEL_ARCH.CHATGLM
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def set_vocab(self):
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def set_vocab_chatglm3(self):
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[bytearray] = []
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@ -2789,6 +2792,95 @@ class ChatGLMModel(Model):
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special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
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special_vocab.add_to_gguf(self.gguf_writer)
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@staticmethod
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def token_bytes_to_string(b):
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from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
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byte_encoder = bytes_to_unicode()
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return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
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@staticmethod
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def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
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parts = [bytes([b]) for b in token]
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while True:
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min_idx = None
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min_rank = None
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for i, pair in enumerate(zip(parts[:-1], parts[1:])):
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rank = mergeable_ranks.get(pair[0] + pair[1])
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if rank is not None and (min_rank is None or rank < min_rank):
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min_idx = i
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min_rank = rank
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if min_rank is None or (max_rank is not None and min_rank >= max_rank):
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break
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assert min_idx is not None
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parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
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return parts
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def set_vocab(self):
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if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
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self.set_vocab_chatglm3()
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return
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[str] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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vocab_size = hparams["padded_vocab_size"]
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assert max(tokenizer.get_vocab().values()) < vocab_size
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tokpre = self.get_vocab_base_pre(tokenizer)
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.mergeable_ranks
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for token, rank in mergeable_ranks.items():
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vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
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if len(token) == 1:
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continue
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merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
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assert len(merged) >= 2 and len(merged) <= 7
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merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged)))
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# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
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added_vocab = tokenizer.get_added_vocab()
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reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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self.gguf_writer.add_tokenizer_model("gpt2")
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self.gguf_writer.add_tokenizer_pre(tokpre)
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
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special_vocab.chat_template = "ChatGLM4"
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special_vocab.merges = merges
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# only add special tokens when they were not already loaded from config.json
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special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])
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special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
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special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|user|>"])
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special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|observation|>"])
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special_vocab._set_special_token("eot", 151336)
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# this one is usually not in config.json anyway
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special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
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special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
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self.gguf_writer.add_name(self.dir_model.name)
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n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
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@ -717,6 +717,8 @@ return html`
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<option value="vicuna">Tess</option>
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<option value="yi34b">Yi-6/9/34B-Chat</option>
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<option value="zephyr">Zephyr</option>
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<option value="chatglm3">ChatGLM3-6B</option>
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<option value="chatglm4">ChatGLM4-9B</option>
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<option value=""></option>
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</optgroup>
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</select>
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@ -326,6 +326,42 @@ export const promptFormats = {
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userMsgPrefix: "",
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userMsgSuffix: "",
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stops: ""
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},
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// ----------------------------
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"chatglm3": {
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template: `[gMASK]sop<|system|>\n {{prompt}}{{history}}<|{{char}}|>`,
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historyTemplate: `<|{{name}}|>\n {{message}}`,
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char: "assistant",
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charMsgPrefix: "",
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charMsgSuffix: "",
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user: "user",
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userMsgPrefix: "",
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userMsgSuffix: "",
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stops: ""
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},
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// ----------------------------
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"chatglm4": {
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template: `[gMASK]<sop><|system|>\n{{prompt}}{{history}}<|{{char}}|>`,
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historyTemplate: `<|{{name}}|>\n{{message}}`,
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char: "assistant",
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charMsgPrefix: "",
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charMsgSuffix: "",
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user: "user",
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userMsgPrefix: "",
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userMsgSuffix: "",
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stops: ""
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}
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};
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14
llama.cpp
14
llama.cpp
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@ -4730,6 +4730,7 @@ static void llm_load_hparams(
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 28: model.type = e_model::MODEL_7B; break;
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case 40: model.type = e_model::MODEL_8B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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@ -4922,6 +4923,9 @@ static void llm_load_vocab(
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} else if (
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tokenizer_pre == "poro-chat") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
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} else if (
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tokenizer_pre == "chatglm-bpe") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
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} else {
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throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
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}
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@ -13369,6 +13373,7 @@ struct llm_tokenizer_bpe {
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break;
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case LLAMA_VOCAB_PRE_TYPE_DBRX:
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case LLAMA_VOCAB_PRE_TYPE_SMAUG:
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case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
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regex_exprs = {
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// same as llama3
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"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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@ -18914,6 +18919,15 @@ static int32_t llama_chat_apply_template_internal(
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if (add_ass) {
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ss << "<|assistant|>";
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}
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} else if (tmpl.find("ChatGLM4") != std::string::npos) {
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ss << "[gMASK]" << "<sop>";
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for (auto message : chat) {
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std::string role(message->role);
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ss << "<|" << role << "|>" << "\n" << message->content;
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}
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if (add_ass) {
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ss << "<|assistant|>";
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}
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} else {
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// template not supported
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return -1;
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1
llama.h
1
llama.h
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@ -87,6 +87,7 @@ extern "C" {
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LLAMA_VOCAB_PRE_TYPE_DBRX = 13,
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LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
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LLAMA_VOCAB_PRE_TYPE_PORO = 15,
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LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 16,
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};
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// note: these values should be synchronized with ggml_rope
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@ -59,6 +59,8 @@ int main(void) {
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"{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
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// ChatGLM3
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"{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
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// ChatGLM4
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"ChatGLM4",
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};
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std::vector<std::string> expected_output = {
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// teknium/OpenHermes-2.5-Mistral-7B
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@ -97,6 +99,8 @@ int main(void) {
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"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
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// ChatGLM3
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"[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n I am an assistant <|user|>\n Another question<|assistant|>",
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// ChatGLM4
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"[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>",
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};
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std::vector<char> formatted_chat(1024);
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int32_t res;
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