diff --git a/common/arg.cpp b/common/arg.cpp index c1ec3c4f99c37..6880117ed8001 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -691,7 +691,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params) { params.ctx_shift = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); add_opt(llama_arg( {"--chunks"}, "N", format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), @@ -1102,7 +1102,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; } else { throw std::invalid_argument("invalid value"); } } - ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING")); add_opt(llama_arg( {"--attention"}, "{causal,non,causal}", "attention type for embeddings, use model default if unspecified", @@ -1121,77 +1121,77 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } else { throw std::invalid_argument("invalid value"); } } - )); + ).set_env("LLAMA_ARG_ROPE_SCALING_TYPE")); add_opt(llama_arg( {"--rope-scale"}, "N", "RoPE context scaling factor, expands context by a factor of N", [](gpt_params & params, const std::string & value) { params.rope_freq_scale = 1.0f / std::stof(value); } - )); + ).set_env("LLAMA_ARG_ROPE_SCALE")); add_opt(llama_arg( {"--rope-freq-base"}, "N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)", [](gpt_params & params, const std::string & value) { params.rope_freq_base = std::stof(value); } - )); + ).set_env("LLAMA_ARG_ROPE_FREQ_BASE")); add_opt(llama_arg( {"--rope-freq-scale"}, "N", "RoPE frequency scaling factor, expands context by a factor of 1/N", [](gpt_params & params, const std::string & value) { params.rope_freq_scale = std::stof(value); } - )); + ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE")); add_opt(llama_arg( {"--yarn-orig-ctx"}, "N", format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx), [](gpt_params & params, int value) { params.yarn_orig_ctx = value; } - )); + ).set_env("LLAMA_ARG_YARN_ORIG_CTX")); add_opt(llama_arg( {"--yarn-ext-factor"}, "N", format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), [](gpt_params & params, const std::string & value) { params.yarn_ext_factor = std::stof(value); } - )); + ).set_env("LLAMA_ARG_YARN_EXT_FACTOR")); add_opt(llama_arg( {"--yarn-attn-factor"}, "N", format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor), [](gpt_params & params, const std::string & value) { params.yarn_attn_factor = std::stof(value); } - )); + ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR")); add_opt(llama_arg( {"--yarn-beta-slow"}, "N", format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow), [](gpt_params & params, const std::string & value) { params.yarn_beta_slow = std::stof(value); } - )); + ).set_env("LLAMA_ARG_YARN_BETA_SLOW")); add_opt(llama_arg( {"--yarn-beta-fast"}, "N", format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast), [](gpt_params & params, const std::string & value) { params.yarn_beta_fast = std::stof(value); } - )); + ).set_env("LLAMA_ARG_YARN_BETA_FAST")); add_opt(llama_arg( {"-gan", "--grp-attn-n"}, "N", format("group-attention factor (default: %d)", params.grp_attn_n), [](gpt_params & params, int value) { params.grp_attn_n = value; } - )); + ).set_env("LLAMA_ARG_GRP_ATTN_N")); add_opt(llama_arg( {"-gaw", "--grp-attn-w"}, "N", format("group-attention width (default: %.1f)", (double)params.grp_attn_w), [](gpt_params & params, int value) { params.grp_attn_w = value; } - )); + ).set_env("LLAMA_ARG_GRP_ATTN_W")); add_opt(llama_arg( {"-dkvc", "--dump-kv-cache"}, "verbose print of the KV cache", @@ -1205,7 +1205,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params) { params.no_kv_offload = true; } - )); + ).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); add_opt(llama_arg( {"-ctk", "--cache-type-k"}, "TYPE", format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()), @@ -1213,7 +1213,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, // TODO: get the type right here params.cache_type_k = value; } - )); + ).set_env("LLAMA_ARG_CACHE_TYPE_K")); add_opt(llama_arg( {"-ctv", "--cache-type-v"}, "TYPE", format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()), @@ -1221,7 +1221,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, // TODO: get the type right here params.cache_type_v = value; } - )); + ).set_env("LLAMA_ARG_CACHE_TYPE_V")); add_opt(llama_arg( {"--perplexity", "--all-logits"}, format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"), @@ -1355,7 +1355,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params, const std::string & value) { params.rpc_servers = value; } - )); + ).set_env("LLAMA_ARG_RPC")); #endif add_opt(llama_arg( {"--mlock"}, @@ -1363,14 +1363,14 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params) { params.use_mlock = true; } - )); + ).set_env("LLAMA_ARG_MLOCK")); add_opt(llama_arg( {"--no-mmap"}, "do not memory-map model (slower load but may reduce pageouts if not using mlock)", [](gpt_params & params) { params.use_mmap = false; } - )); + ).set_env("LLAMA_ARG_NO_MMAP")); add_opt(llama_arg( {"--numa"}, "TYPE", "attempt optimizations that help on some NUMA systems\n" @@ -1385,7 +1385,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } else { throw std::invalid_argument("invalid value"); } } - )); + ).set_env("LLAMA_ARG_NUMA")); add_opt(llama_arg( {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N", "number of layers to store in VRAM", @@ -1433,7 +1433,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n"); } } - )); + ).set_env("LLAMA_ARG_SPLIT_MODE")); add_opt(llama_arg( {"-ts", "--tensor-split"}, "N0,N1,N2,...", "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1", @@ -1460,7 +1460,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n"); } } - )); + ).set_env("LLAMA_ARG_TENSOR_SPLIT")); add_opt(llama_arg( {"-mg", "--main-gpu"}, "INDEX", format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu), @@ -1470,7 +1470,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n"); } } - )); + ).set_env("LLAMA_ARG_MAIN_GPU")); add_opt(llama_arg( {"--check-tensors"}, format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), @@ -1533,7 +1533,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params, const std::string & value) { params.model_alias = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS")); add_opt(llama_arg( {"-m", "--model"}, "FNAME", ex == LLAMA_EXAMPLE_EXPORT_LORA @@ -1741,7 +1741,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params, const std::string & value) { params.public_path = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH")); add_opt(llama_arg( {"--embedding", "--embeddings"}, format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), @@ -1779,14 +1779,14 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, [](gpt_params & params, const std::string & value) { params.ssl_file_key = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_KEY_FILE")); add_opt(llama_arg( {"--ssl-cert-file"}, "FNAME", "path to file a PEM-encoded SSL certificate", [](gpt_params & params, const std::string & value) { params.ssl_file_cert = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE")); add_opt(llama_arg( {"-to", "--timeout"}, "N", format("server read/write timeout in seconds (default: %d)", params.timeout_read), @@ -1794,7 +1794,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, params.timeout_read = value; params.timeout_write = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT")); add_opt(llama_arg( {"--threads-http"}, "N", format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http), diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp index b6d4725fd1167..4b19a9dc24741 100644 --- a/examples/gen-docs/gen-docs.cpp +++ b/examples/gen-docs/gen-docs.cpp @@ -6,42 +6,73 @@ // Export usage message (-h) to markdown format +static void write_table_header(std::ofstream & file) { + file << "| Argument | Explanation |\n"; + file << "| -------- | ----------- |\n"; +} + +static void write_table_entry(std::ofstream & file, const llama_arg & opt) { + file << "| `"; + // args + for (const auto & arg : opt.args) { + if (arg == opt.args.front()) { + file << arg; + if (opt.args.size() > 1) file << ", "; + } else { + file << arg << (arg != opt.args.back() ? ", " : ""); + } + } + // value hint + if (opt.value_hint) { + std::string md_value_hint(opt.value_hint); + string_replace_all(md_value_hint, "|", "\\|"); + file << " " << md_value_hint; + } + if (opt.value_hint_2) { + std::string md_value_hint_2(opt.value_hint_2); + string_replace_all(md_value_hint_2, "|", "\\|"); + file << " " << md_value_hint_2; + } + // help text + std::string md_help(opt.help); + string_replace_all(md_help, "\n", "
"); + string_replace_all(md_help, "|", "\\|"); + file << "` | " << md_help << " |\n"; +} + +static void write_table(std::ofstream & file, std::vector & opts) { + write_table_header(file); + for (const auto & opt : opts) { + write_table_entry(file, *opt); + } +} + static void export_md(std::string fname, llama_example ex) { std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc); gpt_params params; auto ctx_arg = gpt_params_parser_init(params, ex); - file << "| Argument | Explanation |\n"; - file << "| -------- | ----------- |\n"; + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; for (auto & opt : ctx_arg.options) { - file << "| `"; - // args - for (const auto & arg : opt.args) { - if (arg == opt.args.front()) { - file << arg; - if (opt.args.size() > 1) file << ", "; - } else { - file << arg << (arg != opt.args.back() ? ", " : ""); - } - } - // value hint - if (opt.value_hint) { - std::string md_value_hint(opt.value_hint); - string_replace_all(md_value_hint, "|", "\\|"); - file << " " << md_value_hint; + // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); } - if (opt.value_hint_2) { - std::string md_value_hint_2(opt.value_hint_2); - string_replace_all(md_value_hint_2, "|", "\\|"); - file << " " << md_value_hint_2; - } - // help text - std::string md_help(opt.help); - string_replace_all(md_help, "\n", "
"); - string_replace_all(md_help, "|", "\\|"); - file << "` | " << md_help << " |\n"; } + + file << "**Common params**\n\n"; + write_table(file, common_options); + file << "\n\n**Sampling params**\n\n"; + write_table(file, sparam_options); + file << "\n\n**Example-specific params**\n\n"; + write_table(file, specific_options); } int main(int, char **) { diff --git a/examples/server/README.md b/examples/server/README.md index 741950c8a5193..dfca07f988824 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -17,6 +17,8 @@ The project is under active development, and we are [looking for feedback and co ## Usage +**Common params** + | Argument | Explanation | | -------- | ----------- | | `-h, --help, --usage` | print usage and exit | @@ -38,7 +40,6 @@ The project is under active development, and we are [looking for feedback and co | `-b, --batch-size N` | logical maximum batch size (default: 2048)
(env: LLAMA_ARG_BATCH) | | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) | -| `--no-context-shift` | disables context shift on inifinite text generation (default: disabled) | | `-fa, --flash-attn` | enable Flash Attention (default: disabled)
(env: LLAMA_ARG_FLASH_ATTN) | | `-p, --prompt PROMPT` | prompt to start generation with | | `--no-perf` | disable internal libllama performance timings (default: false)
(env: LLAMA_ARG_NO_PERF) | @@ -46,8 +47,56 @@ The project is under active development, and we are [looking for feedback and co | `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) | | `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | | `--no-escape` | do not process escape sequences | -| `-sp, --special` | special tokens output enabled (default: false) | -| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | +| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | +| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N
(env: LLAMA_ARG_ROPE_SCALE) | +| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | +| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | +| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `-gan, --grp-attn-n N` | group-attention factor (default: 1)
(env: LLAMA_ARG_GRP_ATTN_N) | +| `-gaw, --grp-attn-w N` | group-attention width (default: 512.0)
(env: LLAMA_ARG_GRP_ATTN_W) | +| `-dkvc, --dump-kv-cache` | verbose print of the KV cache | +| `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | +| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | +| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | +| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | +| `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | +| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_NO_MMAP) | +| `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | +| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM
(env: LLAMA_ARG_N_GPU_LAYERS) | +| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(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
(env: LLAMA_ARG_TENSOR_SPLIT) | +| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)
(env: LLAMA_ARG_MAIN_GPU) | +| `--check-tensors` | check model tensor data for invalid values (default: false) | +| `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false | +| `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) | +| `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) | +| `--control-vector FNAME` | add a control vector
note: this argument can be repeated to add multiple control vectors | +| `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE
note: this argument can be repeated to add multiple scaled control vectors | +| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive | +| `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)
(env: LLAMA_ARG_MODEL) | +| `-mu, --model-url MODEL_URL` | model download url (default: unused)
(env: LLAMA_ARG_MODEL_URL) | +| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)
(env: LLAMA_ARG_HF_REPO) | +| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)
(env: LLAMA_ARG_HF_FILE) | +| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | +| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) | +| `--log-disable` | Log disable | +| `--log-file FNAME` | Log to file | +| `--log-colors` | Enable colored logging
(env: LLAMA_LOG_COLORS) | +| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) | +| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.
(env: LLAMA_LOG_VERBOSITY) | +| `--log-prefix` | Enable prefx in log messages
(env: LLAMA_LOG_PREFIX) | +| `--log-timestamps` | Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS) | + + +**Sampling params** + +| Argument | Explanation | +| -------- | ----------- | | `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) | | `-s, --seed SEED` | RNG seed (default: 4294967295, use random seed for 4294967295) | | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) | @@ -72,54 +121,28 @@ The project is under active development, and we are [looking for feedback and co | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | | `--grammar-file FNAME` | file to read grammar from | | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | -| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model | -| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N | -| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model) | -| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N | -| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size) | -| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation) | -| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0) | -| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0) | -| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0) | -| `-gan, --grp-attn-n N` | group-attention factor (default: 1) | -| `-gaw, --grp-attn-w N` | group-attention width (default: 512.0) | -| `-dkvc, --dump-kv-cache` | verbose print of the KV cache | -| `-nkvo, --no-kv-offload` | disable KV offload | -| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16) | -| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16) | -| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | -| `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | + + +**Example-specific params** + +| Argument | Explanation | +| -------- | ----------- | +| `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | +| `-sp, --special` | special tokens output enabled (default: false) | +| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | +| `--pooling {none,mean,cls,last}` | pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING) | | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | | `-nocb, --no-cont-batching` | disable continuous batching
(env: LLAMA_ARG_NO_CONT_BATCHING) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing | -| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock) | -| `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437 | -| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM
(env: LLAMA_ARG_N_GPU_LAYERS) | -| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs | -| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 | -| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) | -| `--check-tensors` | check model tensor data for invalid values (default: false) | -| `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false | -| `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) | -| `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) | -| `--control-vector FNAME` | add a control vector
note: this argument can be repeated to add multiple control vectors | -| `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE
note: this argument can be repeated to add multiple scaled control vectors | -| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive | -| `-a, --alias STRING` | set alias for model name (to be used by REST API) | -| `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)
(env: LLAMA_ARG_MODEL) | -| `-mu, --model-url MODEL_URL` | model download url (default: unused)
(env: LLAMA_ARG_MODEL_URL) | -| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)
(env: LLAMA_ARG_HF_REPO) | -| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)
(env: LLAMA_ARG_HF_FILE) | -| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | +| `-a, --alias STRING` | set alias for model name (to be used by REST API)
(env: LLAMA_ARG_ALIAS) | | `--host HOST` | ip address to listen (default: 127.0.0.1)
(env: LLAMA_ARG_HOST) | | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | -| `--path PATH` | path to serve static files from (default: ) | +| `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | | `--api-key KEY` | API key to use for authentication (default: none)
(env: LLAMA_API_KEY) | | `--api-key-file FNAME` | path to file containing API keys (default: none) | -| `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key | -| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate | -| `-to, --timeout N` | server read/write timeout in seconds (default: 600) | +| `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key
(env: LLAMA_ARG_SSL_KEY_FILE) | +| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE) | +| `-to, --timeout N` | server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT) | | `--threads-http N` | number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP) | | `-spf, --system-prompt-file FNAME` | set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | @@ -128,14 +151,6 @@ The project is under active development, and we are [looking for feedback and co | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted:
https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
(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)
| | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | -| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) | -| `--log-disable` | Log disable | -| `--log-file FNAME` | Log to file | -| `--log-colors` | Enable colored logging
(env: LLAMA_LOG_COLORS) | -| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) | -| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.
(env: LLAMA_LOG_VERBOSITY) | -| `--log-prefix` | Enable prefx in log messages
(env: LLAMA_LOG_PREFIX) | -| `--log-timestamps` | Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS) | Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e5275a5149551..61ff09bb2b40f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2356,6 +2356,10 @@ int main(int argc, char ** argv) { svr.reset(new httplib::Server()); } #else + if (params.ssl_file_key != "" && params.ssl_file_cert != "") { + LOG_ERR("Server is built without SSL support\n"); + return 1; + } svr.reset(new httplib::Server()); #endif