Text Classification
cortiq
Laya
English
cmf
jev
decision-making
structured-output
tool-selection
semantic-routing
on-device
metal
vulkan
intent-classification
resonance-routing
Instructions to use infosave/cmf-decision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- cortiq
How to use infosave/cmf-decision with cortiq:
# one Rust binary, no additional dependencies cargo install cortiq-cli # or a prebuilt binary from github.com/infosave2007/cmf/releases hf download infosave/cmf-decision --include "*.cmf" --local-dir . ls *.cmf # some repos ship more than one quantization
cortiq run FILE.cmf --prompt "What is the capital of France?"
cortiq serve FILE.cmf --port 8080 # OpenAI-compatible server
- Laya
How to use infosave/cmf-decision with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 4,817 Bytes
8f78c5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | {
"schema": "cortiq-model-card-comparison/1",
"model_file_sha256": "ed9b8ec2bbfe9e9fd30f14a5eaf82314f38bc7e7510a39772baa2de3801d79b1",
"model_bytes": 304520292,
"runtime": "cortiq 0.7.8",
"oracle_in_hybrid_benchmark": "deepseek/deepseek-v4.1-flash",
"benchmarks": [
{
"id": "banking77",
"name": "BANKING77",
"n": 3080,
"labels": 77,
"local_correct": 2875,
"local_accuracy_pct": 93.34,
"jev_correct": 2636,
"jev_accuracy_pct": 85.58,
"local_wilson95_pct": [
92.41,
94.17
],
"jev_wilson95_pct": [
84.3,
86.78
],
"mcnemar_p": 9.72e-33,
"accepted": 2791,
"accepted_correct": 2714,
"accepted_accuracy_pct": 97.24,
"coverage_pct": 90.62,
"local_p50_ms": 3.92,
"local_p95_ms": 5.27,
"cortiq_https_p50_ms": 78.4,
"cortiq_https_p95_ms": 141.6,
"jev_http_p50_ms": 348.33,
"jev_http_p95_ms": 426.03,
"jev_latency_n": 3080,
"jev_latency_concurrency": 4,
"hybrid_correct": 2893,
"hybrid_accuracy_pct": 93.93,
"hybrid_oracle_calls": 282,
"hybrid_api_usd_per_million": 3.01,
"jev_api_usd_per_million": 183.69
},
{
"id": "clinc150",
"name": "CLINC150",
"n": 4500,
"labels": 150,
"local_correct": 4328,
"local_accuracy_pct": 96.18,
"jev_correct": 4354,
"jev_accuracy_pct": 96.76,
"local_wilson95_pct": [
95.58,
96.7
],
"jev_wilson95_pct": [
96.2,
97.23
],
"mcnemar_p": 0.099,
"accepted": 4145,
"accepted_correct": 4091,
"accepted_accuracy_pct": 98.7,
"coverage_pct": 92.11,
"local_p50_ms": 3.79,
"local_p95_ms": 4.57,
"cortiq_https_p50_ms": 78.5,
"cortiq_https_p95_ms": 134.6,
"jev_http_p50_ms": 391.51,
"jev_http_p95_ms": 528.49,
"jev_latency_n": 200,
"jev_latency_concurrency": 1,
"hybrid_correct": 4386,
"hybrid_accuracy_pct": 97.47,
"hybrid_oracle_calls": 351,
"hybrid_api_usd_per_million": 3.53,
"jev_api_usd_per_million": 271.61
},
{
"id": "massive",
"name": "MASSIVE",
"n": 2974,
"labels": 60,
"local_correct": 2562,
"local_accuracy_pct": 86.15,
"jev_correct": 2551,
"jev_accuracy_pct": 85.78,
"local_wilson95_pct": [
84.86,
87.34
],
"jev_wilson95_pct": [
84.48,
86.99
],
"mcnemar_p": 0.607,
"accepted": 1615,
"accepted_correct": 1581,
"accepted_accuracy_pct": 97.89,
"coverage_pct": 54.3,
"local_p50_ms": 3.0,
"local_p95_ms": 3.58,
"cortiq_https_p50_ms": 70.4,
"cortiq_https_p95_ms": 117.9,
"jev_http_p50_ms": 346.37,
"jev_http_p95_ms": 492.93,
"jev_latency_n": 200,
"jev_latency_concurrency": 1,
"hybrid_correct": 2617,
"hybrid_accuracy_pct": 88.0,
"hybrid_oracle_calls": 1335,
"hybrid_api_usd_per_million": 8.48,
"jev_api_usd_per_million": 110.86
}
],
"notes": [
"Local accuracy is top-1 over every test row before abstention; gate accuracy and coverage are separate.",
"Local CPU latency: full text to decision, Apple M4, one CPU thread, 50 warmups, all test rows.",
"Cortiq HTTPS latency: same client Mac to deployed server, sequential, no oracle, all test rows.",
"Jev latency includes network; BANKING77 uses four workers, CLINC150 and MASSIVE serial samples of 200.",
"Different hardware and concurrency: this is not a hardware-normalized throughput comparison.",
"Hybrid costs: served cache/learning run with stored DeepSeek answers replayed and 24 live MASSIVE calls; not a timing run.",
"Per-million fees extrapolate recorded costs, excluding hardware, electricity and operations.",
"Reused public benchmarks, unequal supervision: CMF train+dev vs two examples per label for Jev.",
"Current model uses BAAI bge-small-en-v1.5 compressed by Cortiq NVG, not the separate Embryo experiment."
],
"sources": [
{
"path_in_project": "artifacts/decision-v4-20260926/gates-release.json",
"sha256": "47d3039559e0f87dee0c6dae9adc3bbd63acc52b05d4859f2de82cf7a0a9ba03"
},
{
"path_in_project": "reports/decision-v4-20260926/live/live.json",
"sha256": "c8769af100e3138291675ab3eed20caa3ecaa780b6b81f4cecbde96dc29f87f5"
},
{
"path_in_project": "reports/decision-v4-20260926/article/stored.json",
"sha256": "4d6918a2d98f16c8b4cdda7759fef98e6343f091fa7723e8ea938d9fb696db47"
},
{
"path_in_project": "reports/decision-v4-20260926/eval/stream-final.json",
"sha256": "0bb19de37cee315a5a5ad4563abcf2944a80c49ef05984554a4fc31012702f50"
}
]
}
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