Text Generation
Transformers
Safetensors
qwen3
feature-extraction
dflash
speculative-decoding
draft-model
block-diffusion
glm
custom_code
text-generation-inference
Instructions to use UCloud-org/GLM-5.2-FP8-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCloud-org/GLM-5.2-FP8-DFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UCloud-org/GLM-5.2-FP8-DFlash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UCloud-org/GLM-5.2-FP8-DFlash", trust_remote_code=True) model = AutoModel.from_pretrained("UCloud-org/GLM-5.2-FP8-DFlash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UCloud-org/GLM-5.2-FP8-DFlash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UCloud-org/GLM-5.2-FP8-DFlash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UCloud-org/GLM-5.2-FP8-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UCloud-org/GLM-5.2-FP8-DFlash
- SGLang
How to use UCloud-org/GLM-5.2-FP8-DFlash with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "UCloud-org/GLM-5.2-FP8-DFlash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UCloud-org/GLM-5.2-FP8-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "UCloud-org/GLM-5.2-FP8-DFlash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UCloud-org/GLM-5.2-FP8-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UCloud-org/GLM-5.2-FP8-DFlash with Docker Model Runner:
docker model run hf.co/UCloud-org/GLM-5.2-FP8-DFlash
File size: 1,220 Bytes
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"architectures": [
"DFlashDraftModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoModel": "dflash.DFlashDraftModel"
},
"block_size": 16,
"bos_token_id": null,
"dflash_config": {
"mask_token_id": 154821,
"target_layer_ids": [
1,
20,
38,
56,
75
]
},
"dtype": "bfloat16",
"eos_token_id": [
154820,
154827,
154829
],
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 6144,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 1048576,
"max_window_layers": 5,
"model_type": "qwen3",
"num_attention_heads": 64,
"num_hidden_layers": 5,
"num_key_value_heads": 64,
"num_target_layers": 78,
"pad_token_id": null,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 8000000,
"rope_type": "default"
},
"sliding_window": null,
"target_hidden_size": 6144,
"tie_word_embeddings": false,
"transformers_version": "5.8.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 154880
}
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