Feature Extraction
Transformers
Safetensors
qwen3
speculative-decoding
dflash
draft-model
vllm
math
custom_code
Instructions to use noctuashap/Confucius3-Math-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noctuashap/Confucius3-Math-DFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noctuashap/Confucius3-Math-DFlash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("noctuashap/Confucius3-Math-DFlash", trust_remote_code=True) model = AutoModel.from_pretrained("noctuashap/Confucius3-Math-DFlash", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,100 Bytes
e7549f0 | 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 | {
"architectures": [
"DFlashDraftModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoModel": "dflash.DFlashDraftModel"
},
"block_size": 16,
"bos_token_id": 151646,
"dflash_config": {
"mask_token_id": 151665,
"target_layer_ids": [
1,
12,
23,
34,
45
]
},
"dtype": "bfloat16",
"eos_token_id": 151643,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 5,
"model_type": "qwen3",
"num_attention_heads": 40,
"num_hidden_layers": 5,
"num_key_value_heads": 8,
"num_target_layers": 48,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}
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