Automatic helper dev
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pudepiedj
parent
297b7b6301
commit
739d6d3022
4 changed files with 147 additions and 15 deletions
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@ -79,6 +79,7 @@ struct gpt_params {
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std::string model_draft = ""; // draft model for speculative decoding
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std::string model_alias = "unknown"; // model alias
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std::string prompt = "";
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std::string prompt_file = ""; // store the external prompt file
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std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
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std::string input_prefix = ""; // string to prefix user inputs with
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std::string input_suffix = ""; // string to suffix user inputs with
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@ -130,7 +130,7 @@ int main() {
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if (x != 0) {
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for (const auto& kvp : bitdict) {
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if ((x & std::stoi(kvp.first)) != 0) {
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printf("Appcode %3d %s ", x, kvp.first.c_str());
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printf("appcode %3d %s ", x, kvp.first.c_str());
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for (const auto& element : kvp.second) {
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printf(" %5s", element.c_str());
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}
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@ -1,22 +1,27 @@
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# search the specified directory for files that include argv[i] == '-f' or '--file' arguments
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import os
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import re
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def find_arguments(directory):
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arguments = {}
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# Get a list of all .cpp files in the specified directory
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cpp_files = [filename for filename in os.listdir(directory) if filename.endswith('.cpp')]
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# Read each .cpp file and search for the specified expressions
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for filename in cpp_files:
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with open(os.path.join(directory, filename), 'r') as file:
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# Use os.walk() to traverse through files in directory and subdirectories
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for root, dirs, files in os.walk(directory):
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for file in files:
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if file.endswith('.cpp'):
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filepath = os.path.join(root, file)
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with open(filepath, 'r') as file:
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content = file.read()
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# Search for the expressions using regular expressions
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matches = re.findall(r'argv\s*\[\s*i\s*\]\s*==\s*([\'"])(?P<arg>-[a-zA-Z]+|\-\-[a-zA-Z]+[a-zA-Z0-9-]*)\1', content)
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# Search for the expression "params." and read the attribute without trailing detritus
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matches = re.findall(r'params\.(.*?)(?=[\). <,;}])', content)
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# Add the found arguments to the dictionary
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arguments[filename] = [match[1] for match in matches]
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# Remove duplicates from matches list
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arguments_list = list(set([match.strip() for match in matches]))
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# Add the matches to the dictionary
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arguments[filepath] = arguments_list
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return arguments
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@ -24,7 +29,29 @@ def find_arguments(directory):
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# Specify the directory you want to search for cpp files
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directory = '/Users/edsilm2/llama.cpp/examples'
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# Call the function and print the result
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result = find_arguments(directory)
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for filename, arguments in result.items():
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print(filename, arguments)
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if __name__ == '__main__':
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# Call the find function and print the result
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result = find_arguments(directory)
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all_of_them = set()
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for filename, arguments in result.items():
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print(f"Filename: \033[32m{filename}\033[0m, arguments: {arguments}\n")
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for argument in arguments:
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if argument not in all_of_them:
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all_of_them.add("".join(argument))
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print(f"\033[32mAll of them: \033[0m{sorted(all_of_them)}.")
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with open("help_list.txt", "r") as helpfile:
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lines = helpfile.read().split("\n")
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for filename, arguments in result.items():
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parameters = []
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for line in lines:
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for argument in arguments:
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if argument in line:
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parameters.append(line)
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all_parameters = set(parameters)
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print(f"\n\nFilename: \033[32m{filename.split('/')[-1]}\033[0m\n\n command-line arguments available and gpt-params functions implemented:\n")
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if not all_parameters:
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print(f" \033[032mNone\033[0m\n")
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else:
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for parameter in all_parameters:
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print(f" help: \033[33m{parameter:<30}\033[0m")
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104
help_list.txt
Normal file
104
help_list.txt
Normal file
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@ -0,0 +1,104 @@
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-h, --helpshow this help message and exit
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-i, --interactive run in interactive mode
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--interactive-first run in interactive mode and wait for input right away
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-ins, --instructrun in instruction mode (use with Alpaca models)
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--multiline-input allows you to write or paste multiple lines without ending each in '\\'
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-r PROMPT, --reverse-prompt PROMPT
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halt generation at PROMPT, return control in interactive mode
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(can be specified more than once for multiple prompts).
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--color colorise output to distinguish prompt and user input from generations
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-s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)
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-t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads);
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-tb N, --threads-batch N
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number of threads to use during batch and prompt processing (default: same as --threads)
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-p PROMPT, --prompt PROMPT
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prompt to start generation with (default: empty)
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-e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)
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--prompt-cache FNAME file to cache prompt state for faster startup (default: none)
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--prompt-cache-all if specified, saves user input and generations to cache as well.
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not supported with --interactive or other interactive options
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--prompt-cache-ro if specified, uses the prompt cache but does not update it.
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--random-prompt start with a randomized prompt.
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--in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string
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--in-prefix STRING string to prefix user inputs with (default: empty)
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--in-suffix STRING string to suffix after user inputs with (default: empty)
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-f FNAME, --file FNAME
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prompt file to start generation.
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-n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
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-c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
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-b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
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--top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
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--top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
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--tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
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--typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
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--repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
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--repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
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--presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
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--frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
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--mirostat N use Mirostat sampling.
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Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
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(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
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--mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
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--mirostat-ent NMirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
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-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS
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modifies the likelihood of token appearing in the completion,
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i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
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or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'
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--grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)
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--grammar-file FNAME file to read grammar from
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--cfg-negative-prompt PROMPT
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negative prompt to use for guidance. (default: empty)
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--cfg-negative-prompt-file FNAME
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negative prompt file to use for guidance. (default: empty)
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--cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
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--rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale
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--rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
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--rope-freq-scale N RoPE frequency linear scaling factor (default: loaded from model)
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--ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)
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--no-penalize-nldo not penalize newline token
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--memory-f32 use f32 instead of f16 for memory key+value (default: disabled)
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not recommended: doubles context memory required and no measurable increase in quality
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--temp N temperature (default: %.1f)\n", (double)params.temp);
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--logits-all return logits for all tokens in the batch (default: disabled)
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--hellaswag compute HellaSwag score over random tasks from datafile supplied with -f
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--hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
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--keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
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--draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
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--chunks Nmax number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
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-np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
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-ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
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-cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)
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if (llama_mlock_supported()) {
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--mlock force system to keep model in RAM rather than swapping or compressing
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}
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if (llama_mmap_supported()) {
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--no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)
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}
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--numa attempt optimizations that help on some NUMA systems
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if run without this previously, it is recommended to drop the system page cache before using this
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see https://github.com/ggerganov/llama.cpp/issues/1437
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#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
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-ngl N, --n-gpu-layers N
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number of layers to store in VRAM
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-ngld N, --n-gpu-layers-draft N
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number of layers to store in VRAM for the draft model
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-ts SPLIT --tensor-split SPLIT
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how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1
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-mg i, --main-gpu i the GPU to use for scratch and small tensors
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#ifdef GGML_USE_CUBLAS
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-nommq, --no-mul-mat-q
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use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.
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Not recommended since this is both slower and uses more VRAM.
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#endif // GGML_USE_CUBLAS
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#endif
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--verbose-promptprint prompt before generation
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fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles
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--lora FNAME apply LoRA adapter (implies --no-mmap)
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--lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)
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--lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter
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-m FNAME, --model FNAME
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model path (default: %s)\n", params.model.c_str());
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-md FNAME, --model-draft FNAME
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draft model for speculative decoding (default: %s)\n", params.model.c_str());
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-ld LOGDIR, --logdir LOGDIR
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path under which to save YAML logs (no logging if unset)
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