Feature Extraction
Transformers
Safetensors
qwen3_5
matilda
jev
fp4
quantized
maincode
8-bit precision
Instructions to use Maincode/matilda-jev-fp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-fp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-fp4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/matilda-jev-fp4") model = AutoModel.from_pretrained("Maincode/matilda-jev-fp4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download comparison.json from Maincode/matilda-jev-fp4: direct link, hf CLI and curl.
- Browser
- Download file 8.16 kB
-
https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/comparison.json
- Command line
-
hf download hf://Maincode/matilda-jev-fp4/comparison.json
-
curl -L -o comparison.json https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/comparison.json
8.16 kB
| { | |
| "complete": true, | |
| "completed_utc": "2026-10-06T16:58:57.206667+00:00", | |
| "model": "Maincode/matilda-jev-fp4", | |
| "summary": { | |
| "BF16": { | |
| "Decision Index": 62.43, | |
| "Raw": 71.42, | |
| "Breadth": 61.15 | |
| }, | |
| "FP4": { | |
| "Decision Index": 61.77, | |
| "Raw": 70.9, | |
| "Breadth": 60.48 | |
| } | |
| }, | |
| "areas": [ | |
| { | |
| "area": "Knowledge & Reasoning", | |
| "BF16": 47.37, | |
| "FP4": 46.45, | |
| "delta_pp": -0.92 | |
| }, | |
| { | |
| "area": "Language Understanding", | |
| "BF16": 70.38, | |
| "FP4": 69.24, | |
| "delta_pp": -1.14 | |
| }, | |
| { | |
| "area": "Retrieval & Classification", | |
| "BF16": 64.98, | |
| "FP4": 64.41, | |
| "delta_pp": -0.57 | |
| }, | |
| { | |
| "area": "Tools & Automation", | |
| "BF16": 80.89, | |
| "FP4": 80.76, | |
| "delta_pp": -0.13 | |
| }, | |
| { | |
| "area": "Arts & Human Taste", | |
| "BF16": 41.96, | |
| "FP4": 42.05, | |
| "delta_pp": 0.09 | |
| } | |
| ], | |
| "benchmarks": [ | |
| { | |
| "id": "1", | |
| "benchmark": "BFCL", | |
| "metric": "case exact accuracy", | |
| "BF16": 96.93, | |
| "FP4": 96.69, | |
| "delta_pp": -0.24 | |
| }, | |
| { | |
| "id": "2", | |
| "benchmark": "ToolRet", | |
| "metric": "nDCG@10", | |
| "BF16": 67.36, | |
| "FP4": 67.6, | |
| "delta_pp": 0.24 | |
| }, | |
| { | |
| "id": "3", | |
| "benchmark": "API-Bank", | |
| "metric": "accuracy", | |
| "BF16": 84.84, | |
| "FP4": 84.65, | |
| "delta_pp": -0.19 | |
| }, | |
| { | |
| "id": "4", | |
| "benchmark": "BANKING77", | |
| "metric": "macro-F1", | |
| "BF16": 90.09, | |
| "FP4": 89.53, | |
| "delta_pp": -0.56 | |
| }, | |
| { | |
| "id": "5", | |
| "benchmark": "CLINC150+OOS", | |
| "metric": "macro-F1", | |
| "BF16": 91.07, | |
| "FP4": 91.2, | |
| "delta_pp": 0.13 | |
| }, | |
| { | |
| "id": "6", | |
| "benchmark": "RouterBench", | |
| "metric": "selected quality (quality objective)", | |
| "BF16": 79.62, | |
| "FP4": 79.63, | |
| "delta_pp": 0.01 | |
| }, | |
| { | |
| "id": "9", | |
| "benchmark": "Home appliance simulator", | |
| "metric": "case exact accuracy", | |
| "BF16": 76.14, | |
| "FP4": 76.14, | |
| "delta_pp": 0.0 | |
| }, | |
| { | |
| "id": "10", | |
| "benchmark": "SGD/SGD-X", | |
| "metric": "macro-F1", | |
| "BF16": 51.69, | |
| "FP4": 44.19, | |
| "delta_pp": -7.5 | |
| }, | |
| { | |
| "id": "11", | |
| "benchmark": "ContractNLI", | |
| "metric": "macro-F1", | |
| "BF16": 83.0, | |
| "FP4": 83.23, | |
| "delta_pp": 0.23 | |
| }, | |
| { | |
| "id": "12", | |
| "benchmark": "ANLI", | |
| "metric": "macro-F1", | |
| "BF16": 74.9, | |
| "FP4": 73.72, | |
| "delta_pp": -1.18 | |
| }, | |
| { | |
| "id": "20", | |
| "benchmark": "BPoMP", | |
| "metric": "accuracy", | |
| "BF16": 94.86, | |
| "FP4": 94.46, | |
| "delta_pp": -0.4 | |
| }, | |
| { | |
| "id": "21", | |
| "benchmark": "Humicroedit", | |
| "metric": "accuracy", | |
| "BF16": 62.56, | |
| "FP4": 61.91, | |
| "delta_pp": -0.65 | |
| }, | |
| { | |
| "id": "22", | |
| "benchmark": "POP909-CL", | |
| "metric": "accuracy", | |
| "BF16": 50.0, | |
| "FP4": 42.2, | |
| "delta_pp": -7.8 | |
| }, | |
| { | |
| "id": "23", | |
| "benchmark": "cfcolor", | |
| "metric": "accuracy", | |
| "BF16": 65.18, | |
| "FP4": 65.4, | |
| "delta_pp": 0.22 | |
| }, | |
| { | |
| "id": "24", | |
| "benchmark": "MMLU", | |
| "metric": "accuracy", | |
| "BF16": 88.89, | |
| "FP4": 88.23, | |
| "delta_pp": -0.66 | |
| }, | |
| { | |
| "id": "25", | |
| "benchmark": "GPQA Diamond", | |
| "metric": "accuracy", | |
| "BF16": 51.02, | |
| "FP4": 48.47, | |
| "delta_pp": -2.55 | |
| }, | |
| { | |
| "id": "26", | |
| "benchmark": "ARC-Easy", | |
| "metric": "accuracy", | |
| "BF16": 98.95, | |
| "FP4": 98.95, | |
| "delta_pp": 0.0 | |
| }, | |
| { | |
| "id": "27", | |
| "benchmark": "ARC-Challenge", | |
| "metric": "accuracy", | |
| "BF16": 96.93, | |
| "FP4": 97.01, | |
| "delta_pp": 0.08 | |
| }, | |
| { | |
| "id": "28", | |
| "benchmark": "WinoGrande", | |
| "metric": "accuracy", | |
| "BF16": 86.74, | |
| "FP4": 86.03, | |
| "delta_pp": -0.71 | |
| }, | |
| { | |
| "id": "29", | |
| "benchmark": "HellaSwag", | |
| "metric": "accuracy", | |
| "BF16": 95.7, | |
| "FP4": 95.2, | |
| "delta_pp": -0.5 | |
| }, | |
| { | |
| "id": "30", | |
| "benchmark": "GSM8K", | |
| "metric": "accuracy", | |
| "BF16": 79.45, | |
| "FP4": 79.53, | |
| "delta_pp": 0.08 | |
| }, | |
| { | |
| "id": "31", | |
| "benchmark": "ChessBench", | |
| "metric": "accuracy", | |
| "BF16": 21.62, | |
| "FP4": 20.7, | |
| "delta_pp": -0.92 | |
| }, | |
| { | |
| "id": "32", | |
| "benchmark": "MuSR", | |
| "metric": "accuracy", | |
| "BF16": 67.69, | |
| "FP4": 67.42, | |
| "delta_pp": -0.27 | |
| }, | |
| { | |
| "id": "33", | |
| "benchmark": "SATA-Bench", | |
| "metric": "case exact accuracy", | |
| "BF16": 23.58, | |
| "FP4": 25.58, | |
| "delta_pp": 2.0 | |
| }, | |
| { | |
| "id": "34", | |
| "benchmark": "SimpleBench", | |
| "metric": "accuracy", | |
| "BF16": 40.0, | |
| "FP4": 30.0, | |
| "delta_pp": -10.0 | |
| }, | |
| { | |
| "id": "36", | |
| "benchmark": "BRIGHT", | |
| "metric": "nDCG@10", | |
| "BF16": 49.33, | |
| "FP4": 48.51, | |
| "delta_pp": -0.82 | |
| }, | |
| { | |
| "id": "37", | |
| "benchmark": "Amazon ESCI", | |
| "metric": "macro-F1", | |
| "BF16": 57.65, | |
| "FP4": 58.0, | |
| "delta_pp": 0.35 | |
| }, | |
| { | |
| "id": "38", | |
| "benchmark": "ACOS", | |
| "metric": "per-review F1", | |
| "BF16": 43.59, | |
| "FP4": 39.29, | |
| "delta_pp": -4.3 | |
| }, | |
| { | |
| "id": "39", | |
| "benchmark": "FinEntity", | |
| "metric": "macro-F1", | |
| "BF16": 94.07, | |
| "FP4": 94.0, | |
| "delta_pp": -0.07 | |
| }, | |
| { | |
| "id": "40", | |
| "benchmark": "iSarcasmEval", | |
| "metric": "Sarcasm F1 \u00b7 track A, English", | |
| "BF16": 64.5, | |
| "FP4": 63.64, | |
| "delta_pp": -0.86 | |
| }, | |
| { | |
| "id": "41", | |
| "benchmark": "VAST", | |
| "metric": "macro-F1", | |
| "BF16": 80.0, | |
| "FP4": 79.3, | |
| "delta_pp": -0.7 | |
| }, | |
| { | |
| "id": "42", | |
| "benchmark": "NLI4CT", | |
| "metric": "macro-F1", | |
| "BF16": 84.49, | |
| "FP4": 84.11, | |
| "delta_pp": -0.38 | |
| }, | |
| { | |
| "id": "43", | |
| "benchmark": "CRUXEval", | |
| "metric": "accuracy", | |
| "BF16": 80.88, | |
| "FP4": 78.95, | |
| "delta_pp": -1.93 | |
| }, | |
| { | |
| "id": "44", | |
| "benchmark": "CLadder", | |
| "metric": "accuracy", | |
| "BF16": 77.4, | |
| "FP4": 77.88, | |
| "delta_pp": 0.48 | |
| }, | |
| { | |
| "id": "45", | |
| "benchmark": "HLE", | |
| "metric": "accuracy", | |
| "BF16": 17.37, | |
| "FP4": 15.77, | |
| "delta_pp": -1.6 | |
| }, | |
| { | |
| "id": "48", | |
| "benchmark": "ForecastBench", | |
| "metric": "Brier (lower is better)", | |
| "BF16": 17.99, | |
| "FP4": 17.66, | |
| "delta_pp": -0.33 | |
| }, | |
| { | |
| "id": "50", | |
| "benchmark": "Habermas Machine", | |
| "metric": "accuracy", | |
| "BF16": 40.99, | |
| "FP4": 44.21, | |
| "delta_pp": 3.22 | |
| }, | |
| { | |
| "id": "56", | |
| "benchmark": "PhishNChips phishing decisions", | |
| "metric": "accuracy", | |
| "BF16": 70.6, | |
| "FP4": 69.2, | |
| "delta_pp": -1.4 | |
| }, | |
| { | |
| "id": "57", | |
| "benchmark": "MMLU-Pro", | |
| "metric": "accuracy", | |
| "BF16": 83.6, | |
| "FP4": 81.7, | |
| "delta_pp": -1.9 | |
| }, | |
| { | |
| "id": "58", | |
| "benchmark": "BBH fixed-option tasks", | |
| "metric": "accuracy", | |
| "BF16": 79.84, | |
| "FP4": 79.54, | |
| "delta_pp": -0.3 | |
| }, | |
| { | |
| "id": "59", | |
| "benchmark": "RAGTruth response-level hallucination", | |
| "metric": "F1 on hallucinated class", | |
| "BF16": 84.16, | |
| "FP4": 84.05, | |
| "delta_pp": -0.11 | |
| }, | |
| { | |
| "id": "61", | |
| "benchmark": "HoVer claim verification", | |
| "metric": "accuracy", | |
| "BF16": 86.2, | |
| "FP4": 86.32, | |
| "delta_pp": 0.12 | |
| }, | |
| { | |
| "id": "62", | |
| "benchmark": "When2Call MCQ", | |
| "metric": "accuracy", | |
| "BF16": 86.42, | |
| "FP4": 86.12, | |
| "delta_pp": -0.3 | |
| }, | |
| { | |
| "id": "64", | |
| "benchmark": "New Yorker caption matching", | |
| "metric": "accuracy", | |
| "BF16": 68.75, | |
| "FP4": 70.83, | |
| "delta_pp": 2.08 | |
| } | |
| ], | |
| "source_DI_reused": true, | |
| "requests": 150317, | |
| "format": "FP4 weights, BF16 activations/GEMM (W4A16)", | |
| "benchmark_exposed_diagnostic": true, | |
| "serving_changed": false, | |
| "paid_api_calls": 0 | |
| } | |