Text Generation
Transformers
Safetensors
English
Chinese
glm_moe_dsa
jang
fp8
vllm
glm
glm-moe-dsa
Mixture of Experts
hopper
h200
mtp
conversational
Instructions to use JANGQ-AI/GLM-5.3-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JANGQ-AI/GLM-5.3-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JANGQ-AI/GLM-5.3-FP8") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JANGQ-AI/GLM-5.3-FP8") model = AutoModelForCausalLM.from_pretrained("JANGQ-AI/GLM-5.3-FP8", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JANGQ-AI/GLM-5.3-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JANGQ-AI/GLM-5.3-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JANGQ-AI/GLM-5.3-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JANGQ-AI/GLM-5.3-FP8
- SGLang
How to use JANGQ-AI/GLM-5.3-FP8 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 "JANGQ-AI/GLM-5.3-FP8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JANGQ-AI/GLM-5.3-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "JANGQ-AI/GLM-5.3-FP8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JANGQ-AI/GLM-5.3-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JANGQ-AI/GLM-5.3-FP8 with Docker Model Runner:
docker model run hf.co/JANGQ-AI/GLM-5.3-FP8
Add files using upload-large-folder tool
Browse files- .gitattributes +1 -0
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|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0513a1fe9a754dff6f4f31e102a8de95e592bb061d9ef0b2a42a145655e0b8e1
|
| 3 |
+
size 2760987544
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d
|
| 3 |
+
size 20217442
|