llama : streamline embeddings from "non-embedding" models (#8087)
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4 changed files with 36 additions and 10 deletions
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@ -472,6 +472,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
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else { invalid_param = true; }
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return true;
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}
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if (arg == "--attention") {
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CHECK_ARG
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std::string value(argv[i]);
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/**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
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else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
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else { invalid_param = true; }
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return true;
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}
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if (arg == "--defrag-thold" || arg == "-dt") {
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CHECK_ARG
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params.defrag_thold = std::stof(argv[i]);
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@ -1468,8 +1476,10 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
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"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
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options.push_back({ "embedding" });
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options.push_back({ "embedding", " --pooling {none,mean,cls}",
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options.push_back({ "embedding", " --pooling {none,mean,cls,last}",
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"pooling type for embeddings, use model default if unspecified" });
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options.push_back({ "embedding", " --attention {causal,non-causal}",
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"attention type for embeddings, use model default if unspecified" });
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options.push_back({ "context hacking" });
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options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
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@ -2175,6 +2185,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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cparams.yarn_beta_slow = params.yarn_beta_slow;
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cparams.yarn_orig_ctx = params.yarn_orig_ctx;
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cparams.pooling_type = params.pooling_type;
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cparams.attention_type = params.attention_type;
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cparams.defrag_thold = params.defrag_thold;
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cparams.cb_eval = params.cb_eval;
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cparams.cb_eval_user_data = params.cb_eval_user_data;
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