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
File size: 658 Bytes
c69aaec | 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 | {
"complete": true,
"device": "AMD Instinct MI355X",
"tests": [
{
"shape": [
128,
128
],
"bit_identical_to_ModelOpt": true,
"GEMM_bit_identical": true
},
{
"shape": [
6144,
5120
],
"bit_identical_to_ModelOpt": true,
"GEMM_bit_identical": true
},
{
"shape": [
5120,
17408
],
"bit_identical_to_ModelOpt": true,
"GEMM_bit_identical": true
}
],
"all_fp4_codes_and_finite_positive_scale_codes_checked": true,
"runtime_compute": "FP4 weight storage; one-matrix Triton dequantization followed by BF16 GEMM"
}
|