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"
| [gMASK]<sop> | |
| {%- if tools -%} | |
| <|system|> | |
| # Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools> | |
| {% for tool in tools %} | |
| {{ tool | tojson(ensure_ascii=False) }} | |
| {% endfor %} | |
| </tools> | |
| For each function call, output the function name and arguments within the following XML format: | |
| <tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%} | |
| {%- macro visible_text(content) -%} | |
| {%- if content is string -%} | |
| {{- content }} | |
| {%- elif content is iterable and content is not mapping -%} | |
| {%- for item in content -%} | |
| {%- if item is mapping and item.type == 'text' -%} | |
| {{- item.text }} | |
| {%- elif item is string -%} | |
| {{- item }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- else -%} | |
| {{- content }} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- set ns = namespace(last_user_index=-1) %} | |
| {%- for m in messages %} | |
| {%- if m.role == 'user' %} | |
| {% set ns.last_user_index = loop.index0 -%} | |
| {%- endif %} | |
| {%- endfor %} | |
| {% for m in messages %} | |
| {%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }} | |
| {%- elif m.role == 'assistant' -%} | |
| <|assistant|> | |
| {%- set reasoning_content = '' %} | |
| {%- set content = visible_text(m.content) %} | |
| {%- if m.reasoning_content is string %} | |
| {%- set reasoning_content = m.reasoning_content %} | |
| {%- else %} | |
| {%- if '</think>' in content %} | |
| {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %} | |
| {%- set content = content.split('</think>')[-1].lstrip('\n') %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%} | |
| {{ '<think>' + reasoning_content.strip() + '</think>'}} | |
| {%- else -%} | |
| {{ '</think>' }} | |
| {%- endif -%} | |
| {%- if content.strip() -%} | |
| {{ content.strip() }} | |
| {%- endif -%} | |
| {% if m.tool_calls %} | |
| {% for tc in m.tool_calls %} | |
| {%- if tc.function %} | |
| {%- set tc = tc.function %} | |
| {%- endif %} | |
| {{- '<tool_call>' + tc.name -}} | |
| {% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %} | |
| {% endif %} | |
| {%- elif m.role == 'tool' -%} | |
| {%- if m.content is string -%} | |
| {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} | |
| {{- '<|observation|>' }} | |
| {%- endif %} | |
| {{- '<tool_response>' }} | |
| {{- m.content }} | |
| {{- '</tool_response>' }} | |
| {%- else -%} | |
| <|observation|>{% for tr in m.content %} | |
| <tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%} | |
| {% endif -%} | |
| {%- elif m.role == 'system' -%} | |
| <|system|>{{ visible_text(m.content) }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| <|assistant|>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}} | |
| {%- endif -%} |