Instructions to use Ex0bit/GLM-4.7-PRISM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ex0bit/GLM-4.7-PRISM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ex0bit/GLM-4.7-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-PRISM") model = AutoModelForCausalLM.from_pretrained("Ex0bit/GLM-4.7-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-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-PRISM", filename="GLM-4.7-PRISM-IQ1_S.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-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-PRISM:IQ1_S # Run inference directly in the terminal: llama-cli -hf Ex0bit/GLM-4.7-PRISM:IQ1_S
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-PRISM:IQ1_S # Run inference directly in the terminal: llama-cli -hf Ex0bit/GLM-4.7-PRISM:IQ1_S
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-PRISM:IQ1_S # Run inference directly in the terminal: ./llama-cli -hf Ex0bit/GLM-4.7-PRISM:IQ1_S
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-PRISM:IQ1_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Ex0bit/GLM-4.7-PRISM:IQ1_S
Use Docker
docker model run hf.co/Ex0bit/GLM-4.7-PRISM:IQ1_S
- LM Studio
- Jan
- vLLM
How to use Ex0bit/GLM-4.7-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-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-PRISM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Ex0bit/GLM-4.7-PRISM:IQ1_S
- SGLang
How to use Ex0bit/GLM-4.7-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-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-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-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-PRISM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Ex0bit/GLM-4.7-PRISM with Ollama:
ollama run hf.co/Ex0bit/GLM-4.7-PRISM:IQ1_S
- Unsloth Studio new
How to use Ex0bit/GLM-4.7-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-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-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-PRISM to start chatting
- Pi new
How to use Ex0bit/GLM-4.7-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-PRISM:IQ1_S
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-PRISM:IQ1_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Ex0bit/GLM-4.7-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-PRISM:IQ1_S
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-PRISM:IQ1_S
Run Hermes
hermes
- Docker Model Runner
How to use Ex0bit/GLM-4.7-PRISM with Docker Model Runner:
docker model run hf.co/Ex0bit/GLM-4.7-PRISM:IQ1_S
- Lemonade
How to use Ex0bit/GLM-4.7-PRISM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ex0bit/GLM-4.7-PRISM:IQ1_S
Run and chat with the model
lemonade run user.GLM-4.7-PRISM-IQ1_S
List all available models
lemonade list
Feedback on refusal
The model stops refusing requests, so the main goal was achieved with success.
But in roleplay, the characters are much more likely to accept something out of character for them.
Appreciate the feedback @AliceThirty ! Great timing as the rigs are firing up for GLM-5-PRISM π. We offer customized fine-tune consulting and would love to hear more about your use case. In the meantime, give our latest role-play model a try: "Ex0bit/Step-3.5-Flash-PRISM." Feel free to reach out on Ko-fi (ko-fi.com/ex0bit) and let us know your thoughts!
Oh nice ! I was hoping you would "prism" this model, I'll try !
But I would like it if the full weights were available instead of only Q2/Q3/Q4 gguf, since I can run a Q6_K model fine on my hardware. I can do the quantization myself.