Instructions to use SubconsciousDev/glm-5.2-fp8-dflash-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SubconsciousDev/glm-5.2-fp8-dflash-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SubconsciousDev/glm-5.2-fp8-dflash-v1") model = AutoModel.from_pretrained("SubconsciousDev/glm-5.2-fp8-dflash-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
GLM-5.2 FP8 DFLASH v1
Overview
This is a DFLASH speculative draft model for GLM-5.2 FP8 serving. The checkpoint uses DFLASH block size 16 and is intended to be loaded as the draft model in SGLang speculative decoding.
Training data: SubconsciousDev/Subconscious-Dflash-Training-Dataset-short-glm52-v1.
SGLang Usage
Add these arguments to the SGLang launch command:
--speculative-algorithm DFLASH \
--speculative-draft-model-path SubconsciousDev/glm-5.2-fp8-dflash-v1 \
--speculative-num-draft-tokens 16 \
Evaluation
Concurrency is 1.
| Benchmark | EAGLE (5,1,6) Speedup | EAGLE (5,1,6) Accept Length | DFLASH Speedup | DFLASH Accept Length |
|---|---|---|---|---|
| GSM8K | 2.2x | 3.99 | 2.3x | 4.87 |
| MATH500 | 3.4x | 4.92 | 4.3x | 7.44 |
| MBPP | 3.5x | 5.21 | 4.9x | 9.03 |
| LiveCodeBench | 2.9x | 4.17 | 3.1x | 5.35 |
| MT-Bench | 2.5x | 3.71 | 2.4x | 3.96 |
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