Instructions to use SubconsciousDev/glm-5.2-fp8-dflash-v2 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-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SubconsciousDev/glm-5.2-fp8-dflash-v2") model = AutoModel.from_pretrained("SubconsciousDev/glm-5.2-fp8-dflash-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - dflash | |
| - speculative-decoding | |
| - sglang | |
| - glm-5.2 | |
| # GLM-5.2 FP8 DFLASH v2 | |
| ## Overview | |
| This is a DFLASH speculative draft model for GLM-5.2 FP8 serving. The checkpoint | |
| uses DFLASH block size 12 and is intended to be loaded as the draft model in | |
| SGLang speculative decoding. | |
| This model is fine-tuned on top of `SubconsciousDev/glm-5.2-fp8-dflash-v1` | |
| using `SubconsciousDev/Subconscious-Dflash-Training-Dataset-mix-glm52-25k`. | |
| ## SGLang Usage | |
| Add these arguments to the SGLang launch command: | |
| ```bash | |
| --speculative-algorithm DFLASH \ | |
| --speculative-draft-model-path SubconsciousDev/glm-5.2-fp8-dflash-v2 \ | |
| --speculative-num-draft-tokens 12 \ | |
| --speculative-draft-kv-cache-dtype bfloat16 | |
| ``` | |