macbert4csc-base-chinese: Self-Hosting & Deployment Guide
macbert4csc-base-chinese is an open-weight text generation model.
What is macbert4csc-base-chinese?
macbert4csc-base-chinese is an open-weight text generation model, published by shibing624 with open weights at shibing624/macbert4csc-base-chinese. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly. At import it had 542,883 downloads on Hugging Face in the previous 30 days, under the apache-2.0 licence.
How to run macbert4csc-base-chinese yourself
Open weights (apache-2.0), 102M parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
macbert4csc-base-chinese VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 0.2 GB | Estimate | 102M params × 2 bytes |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy macbert4csc-base-chinese on your hardware
| Hardware | BF16 (full precision) |
|---|---|
| NVIDIA H200141 GB | 1× |
| NVIDIA H10080 GB | 1× |
| NVIDIA A100 80GB80 GB | 1× |
| NVIDIA L40S48 GB | 1× |
GPUs needed per variant.
| Hardware | BF16 (full precision) |
|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | Fits |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | Fits |
| Azure ND H100 v58× H100 · 640 GB | Fits |
Whole-instance GPU memory; public instance specs.
Serve it yourself
pip install -U vllm
vllm serve shibing624/macbert4csc-base-chinesepip install 'sglang[all]'
python -m sglang.launch_server --model-path shibing624/macbert4csc-base-chineseCommands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsWhen to Use — When NOT to Use
Use it if...
- You want to run macbert4csc-base-chinese on your own hardware and keep every prompt inside your network
- The weights are published openly, so you can pin one checkpoint and keep it reproducible
- You want to fine-tune or quantise the model rather than accept a hosted configuration
- You are comparing self-hosting cost against a managed endpoint before committing
Avoid if...
- LLM API does not route this model today, so there is no endpoint here to call
- You have no GPU capacity: the VRAM figures below are the floor, not a suggestion
- You want zero operations — serving, scaling and upgrades are yours to run
- You need image, audio or video input; this checkpoint is text in, text out
Get one key to every model
Swap your API key. Keep your code.