Update contextual help dev
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pudepiedj
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7a4dcff667
2 changed files with 98 additions and 68 deletions
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@ -2,6 +2,18 @@
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import os
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import re
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import collections
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import re
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def replace_dashes_with_underscores(filename):
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with open(filename, 'r') as file:
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content = file.read()
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# Match '-' surrounded by word characters on both sides and replace with '_'
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replaced_content = re.sub(r'(\w)-(\w)', r'\1_\2', content)
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with open(filename, 'w') as file:
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file.write(replaced_content)
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def find_arguments(directory):
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arguments = {}
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@ -14,9 +26,8 @@ def find_arguments(directory):
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with open(filepath, 'r') as file:
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content = file.read()
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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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# Search for the expression "params." excluding prefixes and read the attribute without trailing detritus
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matches = re.findall(r'(?:^|\s)params\.(.*?)(?=[\). <,;}]|\Z)', content)
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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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@ -25,28 +36,34 @@ def find_arguments(directory):
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return arguments
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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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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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def output_results(result):
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sorted_result = collections.OrderedDict(sorted(result.items()))
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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 filename, arguments in sorted_result.items():
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print(f"Filename: \033[32m{filename.split('/')[-1]}\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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return sorted_result
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with open("help_list.txt", "r") as helpfile:
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def find_parameters(file, sorted_result):
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with open(file, "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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for filename, arguments in sorted_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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# need to try to avoid spurious matches
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argument1 = "--" + argument + " "
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if argument1 in line:
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parameters.append(line)
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# need to try to avoid spurious matches
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argument2 = "params." + argument.split('n_')[-1]
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if argument2 in line:
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parameters.append(line)
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argument3 = "params." + argument
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if argument3 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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@ -54,4 +71,17 @@ if __name__ == '__main__':
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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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print(f" help: \033[33m{parameter:<30}\033[0m")
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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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if __name__ == '__main__':
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# First we alter all the hyphenated help words in help-file.txt to underscores
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replace_dashes_with_underscores('help_list.txt')
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# Call the find function and output the result
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result = find_arguments(directory)
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sorted = output_results(result)
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# analyse the files and what they contain
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find_parameters("help_list.txt", sorted)
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104
help_list.txt
104
help_list.txt
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@ -1,104 +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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--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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--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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-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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--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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--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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-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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--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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--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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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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--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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--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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--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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--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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-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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--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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-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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-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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-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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-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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--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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-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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