Text Generation
Transformers
Safetensors
GGUF
English
Chinese
glm4_moe_lite
glm4
prism
Mixture of Experts
conversational
Instructions to use Ex0bit/GLM-4.7-Flash-PRISM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", 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
- llama.cpp
How to use Ex0bit/GLM-4.7-Flash-PRISM with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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 Desktop
- Pi
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 serve -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
- OpenClaw
How to use Ex0bit/GLM-4.7-Flash-PRISM with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Ex0bit/GLM-4.7-Flash-PRISM:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Update README.md
Browse files
README.md
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## Model Description
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This is **Ex0bit/GLM-4.7-Flash-PRISM**
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- PRISM (Projected Refusal Isolation via Subspace Modification) — State-of-the-art over-refusal/propaganda removal from LLMs
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**GLM-4.7-PRISM:** Unrestricted GLM-4.7 Model Access
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## Model Description
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This is **Ex0bit/GLM-4.7-Flash-PRISM**
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**GLM-4.7-Flash-PRISM:** Unrestricted GLM-4.7-Flash Model Access
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Access GLM-4.7-Flash-PRISM, an abliterated version of ZAI's efficient 30B-A3B MoE model with over-refusal mechanisms removed.
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**What You Get:**
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- **30B-A3B MoE Architecture** — Lightweight yet powerful Mixture-of-Experts model with 30 billion total parameters and ~3 billion active per token for fast, efficient inference
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- **PRISM (Projected Refusal Isolation via Subspace Modification)** — State-of-the-art abliteration technique that removes over-refusal behaviors while preserving capabilities
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- **128K Context Window** — Extended context for complex tasks and large codebases
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- **Interleaved & Preserved Thinking** — Multi-turn reasoning that persists across conversations with per-turn thinking control
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- **Strong In-Class Benchmarks** — 91.6% AIME 2025, 79.5% τ²-Bench, 59.2% SWE-bench Verified, 75.2% GPQA
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