Feature Extraction
Transformers
Safetensors
qwen3
speculative-decoding
dflash
eagle
draft-model
kimi-k2
specforge
custom_code
Instructions to use cm00cm/Kimi-K2.7-Code-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cm00cm/Kimi-K2.7-Code-DFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cm00cm/Kimi-K2.7-Code-DFlash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cm00cm/Kimi-K2.7-Code-DFlash", trust_remote_code=True) model = AutoModel.from_pretrained("cm00cm/Kimi-K2.7-Code-DFlash", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Kimi-K2.7-Code DFlash draft
DFlash speculative-decoding draft model for moonshotai/Kimi-K2.7-Code, trained with SpecForge (PR #593) on NVIDIA Nemotron-Post-Training-Dataset-v2 (stem+chat+math+code).
- 6-layer Qwen3-style draft (hidden 7168); consumes target hidden states at layers [1,12,24,35,47,58]; block_size 8.
- Target vocab/tokenizer: Kimi-K2.7-Code (vocab 163840, mask_token_id 163838).
- Checkpoint: epoch_2_step_160000 โ Work-in-progress snapshot (epoch_2_step_160000) โ training still running.
Load with trust_remote_code=True (model code in dflash.py). Intended as the draft in SGLang DFlash speculative decoding paired with the Kimi-K2.7-Code target.
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Model tree for cm00cm/Kimi-K2.7-Code-DFlash
Base model
moonshotai/Kimi-K2.7-Code