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
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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
```
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