Instructions to use Ex0bit/GLM-4.7-Flash-PRISM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ex0bit/GLM-4.7-Flash-PRISM with Transformers:

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

pipe = pipeline("text-generation", model="Ex0bit/GLM-4.7-Flash-PRISM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ex0bit/GLM-4.7-Flash-PRISM")
model = AutoModelForCausalLM.from_pretrained("Ex0bit/GLM-4.7-Flash-PRISM")
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]:]))

llama-cpp-python

How to use Ex0bit/GLM-4.7-Flash-PRISM with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ex0bit/GLM-4.7-Flash-PRISM",
	filename="GLM-4.7-Flash-PRISM-GGUFs/GLM-4.7-Flash-PRISM-IQ4_NL.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Ex0bit/GLM-4.7-Flash-PRISM with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Use Docker

docker model run hf.co/Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

LM Studio
Jan

vLLM

How to use Ex0bit/GLM-4.7-Flash-PRISM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ex0bit/GLM-4.7-Flash-PRISM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/GLM-4.7-Flash-PRISM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

SGLang

How to use Ex0bit/GLM-4.7-Flash-PRISM 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 "Ex0bit/GLM-4.7-Flash-PRISM" \
    --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": "Ex0bit/GLM-4.7-Flash-PRISM",
		"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 "Ex0bit/GLM-4.7-Flash-PRISM" \
        --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": "Ex0bit/GLM-4.7-Flash-PRISM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Ex0bit/GLM-4.7-Flash-PRISM with Ollama:
```
ollama run hf.co/Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
```

Unsloth Studio new

How to use Ex0bit/GLM-4.7-Flash-PRISM with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/GLM-4.7-Flash-PRISM to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/GLM-4.7-Flash-PRISM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ex0bit/GLM-4.7-Flash-PRISM to start chatting

Pi new

How to use Ex0bit/GLM-4.7-Flash-PRISM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ex0bit/GLM-4.7-Flash-PRISM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Ex0bit/GLM-4.7-Flash-PRISM with Docker Model Runner:
```
docker model run hf.co/Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
```

Lemonade

How to use Ex0bit/GLM-4.7-Flash-PRISM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M

Run and chat with the model

lemonade run user.GLM-4.7-Flash-PRISM-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GLM-4.7-Flash-PRISM

An over-refusal/propaganda free version of ZAI's GLM-4.7-Flash with over-refusal and bias mechanisms completely removed using our Advanced PRISM Pipeline.

☕ Support Our Work

If you find this model useful, consider supporting us on Ko-fi!

Option	Description
PRISM VIP Membership	Access to all PRISM models
One-Time Support	Support this model

Model Highlights

PRISM Ablation — State-of-the-art technique that removes over-refusal behaviors while preserving model capabilities
30B-A3B MoE Architecture — 30 billion total parameters with ~3 billion active per token for fast, efficient inference
128K Context Window — Extended context for complex tasks and large codebases
Interleaved Thinking — Multi-turn reasoning that persists across conversations with per-turn thinking control

Benchmarks

Benchmark	GLM-4.7-Flash	Qwen3-30B-A3B-Thinking-2507	GPT-OSS-20B
AIME 2025	91.6	85.0	91.7
GPQA	75.2	73.4	71.5
LCB v6	64.0	66.0	61.0
HLE	14.4	9.8	10.9
SWE-bench Verified	59.2	22.0	34.0
τ²-Bench	79.5	49.0	47.7
BrowseComp	42.8	2.29	28.3

Usage

Transformers

Install the latest transformers from source:

pip install git+https://github.com/huggingface/transformers.git

Run inference:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "Ex0bit/GLM-4.7-Flash-PRISM"

tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Hello!"}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).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

Install vLLM nightly:

pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
pip install git+https://github.com/huggingface/transformers.git

Serve the model:

vllm serve Ex0bit/GLM-4.7-Flash-PRISM \
     --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-flash-prism

SGLang

Install SGLang:

uv pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 --extra-index-url https://sgl-project.github.io/whl/pr/
uv pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa

Launch the server:

python3 -m sglang.launch_server \
  --model-path Ex0bit/GLM-4.7-Flash-PRISM \
  --tp-size 4 \
  --tool-call-parser glm47  \
  --reasoning-parser glm45 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.8 \
  --served-model-name glm-4.7-flash-prism \
  --host 0.0.0.0 \
  --port 8000

Note: For Blackwell GPUs, add --attention-backend triton --speculative-draft-attention-backend triton to your SGLang launch command.

Recommended Parameters

Use Case	Temperature	Top-P	Max New Tokens
Default	1.0	0.95	131072
Code (SWE-bench)	0.7	1.0	16384
Agentic Tasks	0.0	—	16384

License

This model is released under the PRISM Research License.

Citation

@misc{elbaz2026glm47flashPrism,
  author = {Elbaz, Eric},
  title = {Elbaz-GLM-4.7-Flash-PRISM: Unchained GLM-4.7-Flash-PRISM Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Ex0bit/Elbaz-GLM-4.7-Flash-PRISM}}
}

Acknowledgments

Based on GLM-4.7-Flash by Z.AI. See the technical report for more details on the base model.

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