Instructions to use 0xSero/GLM-4.7-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/GLM-4.7-Flash with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="0xSero/GLM-4.7-Flash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xSero/GLM-4.7-Flash")
model = AutoModelForCausalLM.from_pretrained("0xSero/GLM-4.7-Flash", 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 0xSero/GLM-4.7-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/GLM-4.7-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xSero/GLM-4.7-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/GLM-4.7-Flash

SGLang

How to use 0xSero/GLM-4.7-Flash 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 "0xSero/GLM-4.7-Flash" \
    --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": "0xSero/GLM-4.7-Flash",
		"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 "0xSero/GLM-4.7-Flash" \
        --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": "0xSero/GLM-4.7-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/GLM-4.7-Flash with Docker Model Runner:
```
docker model run hf.co/0xSero/GLM-4.7-Flash
```

0xSero commited on May 30

Commit

304cbb9

verified ·

1 Parent(s): 20084ba

Standardize model card (template rollout)

Browse files

Files changed (1) hide show

README.md +44 -18

README.md CHANGED Viewed

@@ -1,27 +1,53 @@
 ---
-base_model: zai-org/GLM-4.7-Flash
 ---
 > [!TIP]
-> Support this work: **[donate.sybilsolutions.ai](https://donate.sybilsolutions.ai)**
->
-> REAP surfaces: [GLM](https://huggingface.co/spaces/0xSero/reap-glm-family) | [MiniMax](https://huggingface.co/spaces/0xSero/reap-minimax-family) | [Qwen](https://huggingface.co/spaces/0xSero/reap-qwen-family) | [Gemma](https://huggingface.co/spaces/0xSero/reap-gemma-family) | [Paper](https://arxiv.org/abs/2510.13999) | [Code](https://github.com/CerebrasResearch/reap) | [PR17](https://github.com/CerebrasResearch/reap/pull/17) | [Cerebras Collection](https://huggingface.co/collections/cerebras/cerebras-reap)
-# glm-4.7-flash-sero
-<!-- SERO_MANAGED_TOP_LINKS_START -->
-## Support and links
-- Donate: https://donate.sybilsolutions.ai
-- X: https://x.com/0xsero
-- GitHub: https://github.com/0xsero
-<!-- SERO_MANAGED_TOP_LINKS_END -->
-## Sponsors
-Thank you for the kind sponsors, wouldn't be possible without them:
-- Nvidia
-- TNG Technology
-- Lambda
-- Prime Intellect
-- HotAisle

 ---
+base_model:
+- zai-org/GLM-4.7-Flash
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- glm
 ---
 > [!TIP]
+> **[Support this work →](https://donate.sybilsolutions.ai)** · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/cerebras-reap)
+# GLM-4.7-Flash
+REAP-pruned [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash).
+## At a glance
+| | |
+|---|---|
+| Base model | [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) |
+| Format | BF16 |
+| Total params | **30B** |
+| Active / token | — |
+| Experts / layer | 64 |
+| Layers | 47 |
+| Hidden size | 2048 |
+| Context | 202,752 |
+| On-disk size | 120 GB |
+## Which variant should I pick?
+| Variant | Format | Link |
+|---|---|---|
+| `GLM-4.7-Flash` **(this)** | BF16 | [link](https://huggingface.co/0xSero/GLM-4.7-Flash) |
+| `GLM-4.7-Flash-DPO` | DPO | [link](https://huggingface.co/0xSero/GLM-4.7-Flash-DPO) |
+| `GLM-4.7-Flash-SFT` | SFT | [link](https://huggingface.co/0xSero/GLM-4.7-Flash-SFT) |
+| `GLM-4.7-Flash-Tools` | Tools | [link](https://huggingface.co/0xSero/GLM-4.7-Flash-Tools) |
+## License & citation
+License inherited from the base model.
+```bibtex
+@misc{lasby2025reap,
+  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
+  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
+  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
+}
+```
+## Sponsors
+Made possible by **NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle**.