GLM 4.7 Flash
GLM 4.7 Flash from zai-org. Source-based hardware guidance from its published configuration.
At a glance
- Parameters
- 31.22B
- Architecture
- glm4_moe_lite
- Context length
- 202,752
- License
- mit
- Disk space
- 58.2 GB
- Software
- vLLM, Transformers, vLLM
Will it run on your machine?
Save your machine to see a personalized rating and its reasoning.
Add my hardwareWays to run it
vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
vllm serve zai-org/GLM-4.7-Flash --tensor-parallel-size 4 --speculative-config.method mtp --speculative-config.num_speculative_tokens 1 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flashTransformers · See official guide
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "zai-org/GLM-4.7-Flash"
messages = [{"role": "user", "content": "hello"}]
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=MODEL_PATH,
torch_dtype=torch.bfloat16,
device_map="auto",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:])
print(output_text)vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
vllm serve zai-org/GLM-4.7-FP8 --tensor-parallel-size 4 --speculative-config.method mtp --speculative-config.num_speculative_tokens 1 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-fp8Explore its uses
Keep exploring
Bonsai 2 · 27B
A compressed 27B-class reasoning model with publisher-provided GGUF packs and a dedicated llama.cpp fork for CUDA, Metal, and CPU.
DeepSeek V4.1 Flash
A multimodal reasoning model with a compressed key-value cache, published as open weights.
Qwen3.8-Flash-Next
An experimental open-weight multimodal model with sparse attention and 262K native context.