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llama : add enum for built-in chat templates (#10623)

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* llama : add enum for supported chat templates

* use "built-in" instead of "supported"

* arg: print list of built-in templates

* fix test

* update server README
This commit is contained in:
Xuan Son Nguyen 2024-12-02 22:10:19 +01:00 committed by GitHub
parent 8648c52101
commit 642330ac7c
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5 changed files with 310 additions and 107 deletions
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16
common/arg.cpp
View File

@ -348,6 +348,18 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
return true;
}
static std::string list_builtin_chat_templates() {
std::vector<const char *> supported_tmpl;
int32_t res = llama_chat_builtin_templates(nullptr, 0);
supported_tmpl.resize(res);
res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size());
std::ostringstream msg;
for (auto & tmpl : supported_tmpl) {
msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", ");
}
return msg.str();
}
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
// load dynamic backends
ggml_backend_load_all();
@ -1814,9 +1826,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--chat-template"}, "JINJA_TEMPLATE",
string_format(
"set custom jinja chat template (default: template taken from model's metadata)\n"
"if suffix/prefix are specified, template will be disabled\n"
"only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
"list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
),
[](common_params & params, const std::string & value) {
if (!common_chat_verify_template(value)) {
throw std::runtime_error(string_format(

11
examples/server/README.md
View File

@ -69,6 +69,8 @@ The project is under active development, and we are [looking for feedback and co
| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_NO_MMAP) |
| `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggerganov/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
| `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
| `--list-devices` | print list of available devices and exit |
| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
@ -158,9 +160,16 @@ The project is under active development, and we are [looking for feedback and co
| `--props` | enable changing global properties via POST /props (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_PROPS) |
| `--no-slots` | disables slots monitoring endpoint<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
| `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted:<br/>https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>list of built-in templates:<br/>chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
| `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16) |
| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5) |
| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9) |
| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model) |
| `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model |
| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused) |
Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.

3
include/llama.h
View File

@ -990,6 +990,9 @@ extern "C" {
char * buf,
int32_t length);
// Get list of built-in chat templates
int32_t llama_chat_builtin_templates(const char ** output, size_t len);
//
// Sampling API
//

247
src/llama.cpp
View File

@ -1549,6 +1549,67 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
},
};
enum llm_chat_template {
LLM_CHAT_TEMPLATE_CHATML,
LLM_CHAT_TEMPLATE_LLAMA_2,
LLM_CHAT_TEMPLATE_LLAMA_2_SYS,
LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS,
LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP,
LLM_CHAT_TEMPLATE_MISTRAL_V1,
LLM_CHAT_TEMPLATE_MISTRAL_V3,
LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
LLM_CHAT_TEMPLATE_MISTRAL_V7,
LLM_CHAT_TEMPLATE_PHI_3,
LLM_CHAT_TEMPLATE_ZEPHYR,
LLM_CHAT_TEMPLATE_MONARCH,
LLM_CHAT_TEMPLATE_GEMMA,
LLM_CHAT_TEMPLATE_ORION,
LLM_CHAT_TEMPLATE_OPENCHAT,
LLM_CHAT_TEMPLATE_VICUNA,
LLM_CHAT_TEMPLATE_VICUNA_ORCA,
LLM_CHAT_TEMPLATE_DEEPSEEK,
LLM_CHAT_TEMPLATE_DEEPSEEK_2,
LLM_CHAT_TEMPLATE_COMMAND_R,
LLM_CHAT_TEMPLATE_LLAMA_3,
LLM_CHAT_TEMPLATE_CHATGML_3,
LLM_CHAT_TEMPLATE_CHATGML_4,
LLM_CHAT_TEMPLATE_MINICPM,
LLM_CHAT_TEMPLATE_EXAONE_3,
LLM_CHAT_TEMPLATE_RWKV_WORLD,
LLM_CHAT_TEMPLATE_GRANITE,
LLM_CHAT_TEMPLATE_UNKNOWN,
};
static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "chatml", LLM_CHAT_TEMPLATE_CHATML },
{ "llama2", LLM_CHAT_TEMPLATE_LLAMA_2 },
{ "llama2-sys", LLM_CHAT_TEMPLATE_LLAMA_2_SYS },
{ "llama2-sys-bos", LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS },
{ "llama2-sys-strip", LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP },
{ "mistral-v1", LLM_CHAT_TEMPLATE_MISTRAL_V1 },
{ "mistral-v3", LLM_CHAT_TEMPLATE_MISTRAL_V3 },
{ "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
{ "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 },
{ "phi3", LLM_CHAT_TEMPLATE_PHI_3 },
{ "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR },
{ "monarch", LLM_CHAT_TEMPLATE_MONARCH },
{ "gemma", LLM_CHAT_TEMPLATE_GEMMA },
{ "orion", LLM_CHAT_TEMPLATE_ORION },
{ "openchat", LLM_CHAT_TEMPLATE_OPENCHAT },
{ "vicuna", LLM_CHAT_TEMPLATE_VICUNA },
{ "vicuna-orca", LLM_CHAT_TEMPLATE_VICUNA_ORCA },
{ "deepseek", LLM_CHAT_TEMPLATE_DEEPSEEK },
{ "deepseek2", LLM_CHAT_TEMPLATE_DEEPSEEK_2 },
{ "command-r", LLM_CHAT_TEMPLATE_COMMAND_R },
{ "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 },
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 },
{ "chatglm4", LLM_CHAT_TEMPLATE_CHATGML_4 },
{ "minicpm", LLM_CHAT_TEMPLATE_MINICPM },
{ "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 },
{ "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD },
{ "granite", LLM_CHAT_TEMPLATE_GRANITE },
};
static llm_arch llm_arch_from_string(const std::string & name) {
for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
if (kv.second == name) {
@ -21843,18 +21904,109 @@ int32_t llama_detokenize(
// chat templates
//
static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
if (LLM_CHAT_TEMPLATES.find(tmpl) != LLM_CHAT_TEMPLATES.end()) {
return LLM_CHAT_TEMPLATES.at(tmpl);
}
auto tmpl_contains = [&tmpl](const char * haystack) -> bool {
return tmpl.find(haystack) != std::string::npos;
};
if (tmpl_contains("<|im_start|>")) {
return LLM_CHAT_TEMPLATE_CHATML;
} else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
if (tmpl_contains("[SYSTEM_PROMPT]")) {
return LLM_CHAT_TEMPLATE_MISTRAL_V7;
} else if (
// catches official 'v1' template
tmpl_contains("' [INST] ' + system_message")
// catches official 'v3' and 'v3-tekken' templates
|| tmpl_contains("[AVAILABLE_TOOLS]")
) {
// Official mistral 'v1', 'v3' and 'v3-tekken' templates
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
if (tmpl_contains(" [INST]")) {
return LLM_CHAT_TEMPLATE_MISTRAL_V1;
} else if (tmpl_contains("\"[INST]\"")) {
return LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN;
}
return LLM_CHAT_TEMPLATE_MISTRAL_V3;
} else {
// llama2 template and its variants
// [variant] support system message
// See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
bool support_system_message = tmpl_contains("<<SYS>>");
bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
bool strip_message = tmpl_contains("content.strip()");
if (strip_message) {
return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
} else if (add_bos_inside_history) {
return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
} else if (support_system_message) {
return LLM_CHAT_TEMPLATE_LLAMA_2_SYS;
} else {
return LLM_CHAT_TEMPLATE_LLAMA_2;
}
}
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
return LLM_CHAT_TEMPLATE_PHI_3;
} else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
return LLM_CHAT_TEMPLATE_ZEPHYR;
} else if (tmpl_contains("bos_token + message['role']")) {
return LLM_CHAT_TEMPLATE_MONARCH;
} else if (tmpl_contains("<start_of_turn>")) {
return LLM_CHAT_TEMPLATE_GEMMA;
} else if (tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
// OrionStarAI/Orion-14B-Chat
return LLM_CHAT_TEMPLATE_ORION;
} else if (tmpl_contains("GPT4 Correct ")) {
// openchat/openchat-3.5-0106
return LLM_CHAT_TEMPLATE_OPENCHAT;
} else if (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: ")) {
// eachadea/vicuna-13b-1.1 (and Orca variant)
if (tmpl_contains("SYSTEM: ")) {
return LLM_CHAT_TEMPLATE_VICUNA_ORCA;
}
return LLM_CHAT_TEMPLATE_VICUNA;
} else if (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>")) {
// deepseek-ai/deepseek-coder-33b-instruct
return LLM_CHAT_TEMPLATE_DEEPSEEK;
} else if (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>")) {
// CohereForAI/c4ai-command-r-plus
return LLM_CHAT_TEMPLATE_COMMAND_R;
} else if (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>")) {
return LLM_CHAT_TEMPLATE_LLAMA_3;
} else if (tmpl_contains("[gMASK]sop")) {
// chatglm3-6b
return LLM_CHAT_TEMPLATE_CHATGML_3;
} else if (tmpl_contains("[gMASK]<sop>")) {
return LLM_CHAT_TEMPLATE_CHATGML_4;
} else if (tmpl_contains(LU8("<用户>"))) {
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
return LLM_CHAT_TEMPLATE_MINICPM;
} else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
} else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
// EXAONE-3.0-7.8B-Instruct
return LLM_CHAT_TEMPLATE_EXAONE_3;
} else if (tmpl_contains("rwkv-world")) {
return LLM_CHAT_TEMPLATE_RWKV_WORLD;
} else if (tmpl_contains("<|start_of_role|>")) {
return LLM_CHAT_TEMPLATE_GRANITE;
}
return LLM_CHAT_TEMPLATE_UNKNOWN;
}
// Simple version of "llama_apply_chat_template" that only works with strings
// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
static int32_t llama_chat_apply_template_internal(
const std::string & tmpl,
const llm_chat_template tmpl,
const std::vector<const llama_chat_message *> & chat,
std::string & dest, bool add_ass) {
// Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
std::stringstream ss;
auto tmpl_contains = [&tmpl](std::string haystack) -> bool {
return tmpl.find(haystack) != std::string::npos;
};
if (tmpl == "chatml" || tmpl_contains("<|im_start|>")) {
if (tmpl == LLM_CHAT_TEMPLATE_CHATML) {
// chatml template
for (auto message : chat) {
ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
@ -21862,8 +22014,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|im_start|>assistant\n";
}
} else if (tmpl == "llama2" || tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
if (tmpl == "mistral-v7" || tmpl_contains("[SYSTEM_PROMPT]")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) {
// Official mistral 'v7' template
// See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
for (auto message : chat) {
@ -21878,15 +22029,14 @@ static int32_t llama_chat_apply_template_internal(
ss << " " << content << "</s>";
}
}
} else if (tmpl == "mistral-v1" || tmpl == "mistral-v3" || tmpl == "mistral-v3-tekken"
|| tmpl_contains("' [INST] ' + system_message") // catches official 'v1' template
|| tmpl_contains("[AVAILABLE_TOOLS]")) { // catches official 'v3' and 'v3-tekken' templates
// Official mistral 'v1', 'v3' and 'v3-tekken' templates
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1
|| tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3
|| tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN) {
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
std::string leading_space = (tmpl == "mistral-v1" || tmpl_contains(" [INST]") ? " " : "");
std::string trailing_space = (tmpl == "mistral-v3-tekken" || tmpl_contains("\"[INST]\"") ? "" : " ");
bool trim_assistant_message = tmpl_contains("|trim + eos_token");
std::string leading_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1 ? " " : "";
std::string trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN ? "" : " ";
bool trim_assistant_message = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3;
bool is_inside_turn = false;
for (auto message : chat) {
if (!is_inside_turn) {
@ -21904,17 +22054,19 @@ static int32_t llama_chat_apply_template_internal(
is_inside_turn = false;
}
}
} else {
} else if (
tmpl == LLM_CHAT_TEMPLATE_LLAMA_2
|| tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS
|| tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS
|| tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP) {
// llama2 template and its variants
// [variant] support system message
// See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
bool support_system_message = tmpl_contains("<<SYS>>") || tmpl == "llama2";
// [variant] space before + after response
bool space_around_response = tmpl_contains("' ' + eos_token");
bool support_system_message = tmpl != LLM_CHAT_TEMPLATE_LLAMA_2;
// [variant] add BOS inside history
bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
bool add_bos_inside_history = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
// [variant] trim spaces from the input message
bool strip_message = tmpl_contains("content.strip()");
bool strip_message = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
// construct the prompt
bool is_inside_turn = true; // skip BOS at the beginning
ss << "[INST] ";
@ -21935,13 +22087,11 @@ static int32_t llama_chat_apply_template_internal(
} else if (role == "user") {
ss << content << " [/INST]";
} else {
ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << "</s>";
ss << content << "</s>";
is_inside_turn = false;
}
}
// llama2 templates seem to not care about "add_generation_prompt
}
} else if (tmpl == "phi3" || (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>"))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_PHI_3) {
// Phi 3
for (auto message : chat) {
std::string role(message->role);
@ -21950,7 +22100,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|assistant|>\n";
}
} else if (tmpl == "zephyr" || tmpl_contains("<|user|>")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
// zephyr template
for (auto message : chat) {
ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
@ -21958,7 +22108,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|assistant|>\n";
}
} else if (tmpl == "monarch" || tmpl_contains("bos_token + message['role']")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_MONARCH) {
// mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
for (auto message : chat) {
std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
@ -21967,7 +22117,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<s>assistant\n";
}
} else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl_contains("<start_of_turn>")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_GEMMA) {
// google/gemma-7b-it
std::string system_prompt = "";
for (auto message : chat) {
@ -21989,7 +22139,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<start_of_turn>model\n";
}
} else if (tmpl == "orion" || tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_ORION) {
// OrionStarAI/Orion-14B-Chat
std::string system_prompt = "";
for (auto message : chat) {
@ -22009,7 +22159,7 @@ static int32_t llama_chat_apply_template_internal(
ss << message->content << "</s>";
}
}
} else if (tmpl == "openchat" || tmpl_contains("GPT4 Correct ")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_OPENCHAT) {
// openchat/openchat-3.5-0106,
for (auto message : chat) {
std::string role(message->role);
@ -22023,13 +22173,13 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "GPT4 Correct Assistant:";
}
} else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: "))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_VICUNA || tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
// eachadea/vicuna-13b-1.1 (and Orca variant)
for (auto message : chat) {
std::string role(message->role);
if (role == "system") {
// Orca-Vicuna variant uses a system prefix
if (tmpl == "vicuna-orca" || tmpl_contains("SYSTEM: ")) {
if (tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
ss << "SYSTEM: " << message->content << "\n";
} else {
ss << message->content << "\n\n";
@ -22043,7 +22193,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "ASSISTANT:";
}
} else if (tmpl == "deepseek" || (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>"))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK) {
// deepseek-ai/deepseek-coder-33b-instruct
for (auto message : chat) {
std::string role(message->role);
@ -22058,7 +22208,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "### Response:\n";
}
} else if (tmpl == "command-r" || (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>"))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_COMMAND_R) {
// CohereForAI/c4ai-command-r-plus
for (auto message : chat) {
std::string role(message->role);
@ -22073,7 +22223,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
}
} else if (tmpl == "llama3" || (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>"))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA_3) {
// Llama 3
for (auto message : chat) {
std::string role(message->role);
@ -22082,7 +22232,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
}
} else if (tmpl == "chatglm3" || tmpl_contains("[gMASK]sop")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) {
// chatglm3-6b
ss << "[gMASK]" << "sop";
for (auto message : chat) {
@ -22092,7 +22242,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|assistant|>";
}
} else if (tmpl == "chatglm4" || tmpl_contains("[gMASK]<sop>")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) {
ss << "[gMASK]" << "<sop>";
for (auto message : chat) {
std::string role(message->role);
@ -22101,7 +22251,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|assistant|>";
}
} else if (tmpl == "minicpm" || tmpl_contains(LU8("<用户>"))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
for (auto message : chat) {
std::string role(message->role);
@ -22113,7 +22263,7 @@ static int32_t llama_chat_apply_template_internal(
ss << trim(message->content);
}
}
} else if (tmpl == "deepseek2" || tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_2) {
// DeepSeek-V2
for (auto message : chat) {
std::string role(message->role);
@ -22128,7 +22278,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "Assistant:";
}
} else if (tmpl == "exaone3" || (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]"))) {
} else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
// EXAONE-3.0-7.8B-Instruct
for (auto message : chat) {
@ -22144,7 +22294,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "[|assistant|]";
}
} else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
// this template requires the model to have "\n\n" as EOT token
for (auto message : chat) {
std::string role(message->role);
@ -22154,7 +22304,7 @@ static int32_t llama_chat_apply_template_internal(
ss << message->content << "\n\n";
}
}
} else if (tmpl == "granite" || tmpl_contains("<|start_of_role|>")) {
} else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) {
// IBM Granite template
for (const auto & message : chat) {
std::string role(message->role);
@ -22206,7 +22356,11 @@ int32_t llama_chat_apply_template(
}
std::string formatted_chat;
int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
llm_chat_template detected_tmpl = llama_chat_detect_template(curr_tmpl);
if (detected_tmpl == LLM_CHAT_TEMPLATE_UNKNOWN) {
return -1;
}
int32_t res = llama_chat_apply_template_internal(detected_tmpl, chat_vec, formatted_chat, add_ass);
if (res < 0) {
return res;
}
@ -22216,6 +22370,15 @@ int32_t llama_chat_apply_template(
return res;
}
int32_t llama_chat_builtin_templates(const char ** output, size_t len) {
auto it = LLM_CHAT_TEMPLATES.begin();
for (size_t i = 0; i < std::min(len, LLM_CHAT_TEMPLATES.size()); i++) {
output[i] = it->first.c_str();
std::advance(it, 1);
}
return (int32_t) LLM_CHAT_TEMPLATES.size();
}
//
// sampling
//

20
tests/test-chat-template.cpp
View File

@ -82,9 +82,9 @@ int main(void) {
// mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt)
"[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
// TheBloke/FusionNet_34Bx2_MoE-AWQ
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST] I am an assistant </s><s>[INST] Another question [/INST]",
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s><s>[INST] Who are you [/INST] I am an assistant </s><s>[INST] Another question [/INST]",
// bofenghuang/vigogne-2-70b-chat
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST]Hi there</s>[INST] Who are you [/INST]I am an assistant</s>[INST] Another question [/INST]",
// mlabonne/AlphaMonarch-7B
"system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n I am an assistant </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
// google/gemma-7b-it
@ -133,6 +133,17 @@ int main(void) {
std::vector<char> formatted_chat(1024);
int32_t res;
// list all supported templates
std::vector<const char *> supported_tmpl;
res = llama_chat_builtin_templates(nullptr, 0);
assert(res > 0);
supported_tmpl.resize(res);
res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size());
printf("Built-in chat templates:\n");
for (auto tmpl : supported_tmpl) {
printf(" %s\n", tmpl);
}
// test invalid chat template
res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size());
assert(res < 0);
@ -174,7 +185,8 @@ int main(void) {
assert(fmt_sys("mistral-v3") == "[INST] You are a helpful assistant\n\n");
assert(fmt_sys("mistral-v3-tekken") == "[INST]You are a helpful assistant\n\n");
assert(fmt_sys("mistral-v7") == "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT]");
assert(fmt_sys("llama2") == "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\n");
assert(fmt_sys("llama2") == "[INST] You are a helpful assistant\n");
assert(fmt_sys("llama2-sys") == "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\n");
assert(fmt_sys("mistral") == "[INST] You are a helpful assistant\n"); // for old pre-v1 templates
assert(fmt_sys("gemma") == ""); // for gemma, system message is merged with user message
assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>");
@ -203,5 +215,7 @@ int main(void) {
assert(fmt_single("gemma") == "\n<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
printf("Test chat templates: OK\n");
return 0;
}