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"model_dir": "models\\v6_code_aware_50k_oss_clean_benign_code",
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"local_repo_repo": {
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"threshold": 0.5,
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"score_p50": 0.003904,
"score_p90": 0.15528,
"score_p95": 0.195039,
"score_p99": 0.231913,
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"local_repo_utils": {
"n": 114,
"threshold": 0.5,
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"score_mean": 0.017779,
"score_p50": 0.000741,
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"local_repo_vesuvius": {
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"threshold": 0.5,
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"flagged": 40,
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"score_p99": 0.915992,
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"python_stdlib": {
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}
},
"flagged_examples": [
{
"score": 0.955582,
"source": "local_project_code",
"path": "C:\\GitHub\\Safety DS\\scripts\\build_malware_code_pool.py",
"preview": "def download_vxunderground( spec: dict, builder: PoolBuilder, chunk_cfg: dict, insecure: bool ) -> None: repo = spec[\"repo\"] cache = ROOT / spec.get(\"cache_dir\", \"data/external/vxunderground\") cache.mkdir(parents=True, exist_ok=True) for su"
},
{
"score": 0.586343,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\convert_hf_to_gguf.py",
"preview": "def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # SwigLU activation assert self.hparams[\"activation_function\"] == \"swiglu\" # ALiBi position embedding assert self.hparams[\"position_embedding_type\"] == \"alibi\" # Embeddi"
},
{
"score": 0.863325,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\arg.cpp",
"preview": "_ARG_NO_KV_OFFLOAD\")); add_opt(common_arg( {\"-nr\", \"--no-repack\"}, \"disable weight repacking\", [](common_params & params) { params.no_extra_bufts = true; } ).set_env(\"LLAMA_ARG_NO_REPACK\")); add_opt(common_arg( {\"--no-host\"}, \"bypass host b"
},
{
"score": 0.991824,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\arg.cpp",
"preview": "nd_dev_t> devices; for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { auto * dev = ggml_backend_dev_get(i); if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { devices.push_back(dev); } } printf(\"Available devices:\\n\"); f"
},
{
"score": 0.60719,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\arg.cpp",
"preview": "n_ubatch = 1024; params.n_batch = 1024; params.n_ctx = 0; params.n_cache_reuse = 256; } ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {\"--fim-qwen-7b-spec\"}, string_format(\"use Qwen 2.5 Coder 7B + 0.5B draft for speculative d"
},
{
"score": 0.827293,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\arg.cpp",
"preview": "wen 3 Coder 30B A3B Instruct (note: can download weights from the internet)\"), [](common_params & params) { params.model.hf_repo = \"ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF\"; params.model.hf_file = \"qwen3-coder-30b-a3b-instruct-q8_0."
},
{
"score": 0.534571,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\base64.hpp",
"preview": "; return 62; } else if (c == '_') { alphabet = alphabet::url_filename_safe; return 63; } } throw base64_error(\"invalid base64 character.\"); } }; #endif // !PUBLIC_DOMAIN_BASE64_HPP_"
},
{
"score": 0.551723,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\chat-parser.cpp",
"preview": "n_regex preamble_regex(\"<\\\\|channel\\\\|>commentary\"); static const common_regex tool_call1_regex(recipient + \"<\\\\|channel\\\\|>(analysis|commentary)\" + constraint + \"?\"); static const common_regex tool_call2_regex(\"<\\\\|channel\\\\|>(analysis|com"
},
{
"score": 0.536037,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\chat-parser.cpp",
"preview": "case COMMON_CHAT_FORMAT_DEEPSEEK_R1: common_chat_parse_deepseek_r1(builder); break; case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1: common_chat_parse_deepseek_v3_1(builder); break; case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: common_chat_parse_function"
},
{
"score": 0.616117,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\chat-parser.cpp",
"preview": "common_chat_parse_kimi_k2(builder); break; case COMMON_CHAT_FORMAT_QWEN3_CODER_XML: common_chat_parse_qwen3_coder_xml(builder); break; case COMMON_CHAT_FORMAT_APRIEL_1_5: common_chat_parse_apriel_1_5(builder); break; case COMMON_CHAT_FORMAT"
},
{
"score": 0.609806,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\chat.cpp",
"preview": "msg_new.tool_calls.size() < msg_prv.tool_calls.size()) { throw std::runtime_error(\"Invalid diff: now finding less tool calls!\"); } if (!msg_prv.tool_calls.empty()) { const auto idx = msg_prv.tool_calls.size() - 1; const auto & pref = msg_pr"
},
{
"score": 0.876555,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\chat.cpp",
"preview": "y v3.1 Llama 3.1\"; case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1: return \"DeepSeek V3.1\"; case COMMON_CHAT_FORMAT_HERMES_2_PRO: return \"Hermes 2 Pro\"; case COMMON_CHAT_FORMAT_COMMAND_R7B: return \"Command R7B\"; case COMMON_CHAT_FORMAT_GRANITE: retur"
},
{
"score": 0.610185,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\chat.h",
"preview": "OMMON_CHAT_FORMAT_GRANITE, COMMON_CHAT_FORMAT_GPT_OSS, COMMON_CHAT_FORMAT_SEED_OSS, COMMON_CHAT_FORMAT_NEMOTRON_V2, COMMON_CHAT_FORMAT_APERTUS, COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS, COMMON_CHAT_FORMAT_GLM_4_5, COMMON_CHAT_FORMAT_MINIMAX_"
},
{
"score": 0.864733,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\common.cpp",
"preview": "d-%H_%M_%S\", std::localtime(&as_time_t)); const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>( current_time.time_since_epoch() % 1000000000).count(); char timestamp_ns[11]; snprintf(timestamp_ns, 11, \"%09\" PRId64, ns); r"
},
{
"score": 0.972733,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\common.cpp",
"preview": "ARATOR; } return p; }; if (getenv(\"LLAMA_CACHE\")) { cache_directory = std::getenv(\"LLAMA_CACHE\"); } else { #if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__) if (std::getenv(\"XDG_CACHE_HOME\")) { cache_di"
},
{
"score": 0.526275,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\common.cpp",
"preview": "mmon_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P); get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY), sparams.xtc_probability, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_"
},
{
"score": 0.539756,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\download.cpp",
"preview": "l_successful = common_pull_file(cli, parts.path, path_temporary, supports_ranges, existing_size, total_size); if (!was_pull_successful) { if (i + 1 < max_attempts) { const int exponential_backoff_delay = std::pow(retry_delay_seconds, i) * 1"
},
{
"score": 0.530205,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\download.cpp",
"preview": "size_t len) { buf.insert(buf.end(), data, data + len); return params.max_size == 0 || buf.size() <= static_cast<size_t>(params.max_size); }, nullptr ); if (!res) { throw std::runtime_error(\"error: cannot make GET request\"); } return { res->"
},
{
"score": 0.882664,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\download.cpp",
"preview": "local_path, token, false)) { throw std::runtime_error(\"Failed to download Docker Model\"); } LOG_INF(\"%s: Downloaded Docker Model to: %s\\n\", __func__, local_path.c_str()); return local_path; } catch (const std::exception & e) { LOG_ERR(\"%s: "
},
{
"score": 0.765199,
"source": "local_repo_llama_cpp",
"path": "C:\\lora_training\\llama.cpp\\common\\json-partial.cpp",
"preview": "last_non_sp_char == 'E' || last_non_sp_char == '-'; }; std::string closing; for (size_t i = err_loc.stack.size(); i > 0; i--) { auto & el = err_loc.stack[i - 1]; if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) { closing += \"}\"; } else if ("
}
]
} |