Instructions to use unsloth/GLM-4.7-Flash-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/GLM-4.7-Flash-FP8-Dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/GLM-4.7-Flash-FP8-Dynamic") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/GLM-4.7-Flash-FP8-Dynamic") model = AutoModelForCausalLM.from_pretrained("unsloth/GLM-4.7-Flash-FP8-Dynamic") 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 unsloth/GLM-4.7-Flash-FP8-Dynamic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/GLM-4.7-Flash-FP8-Dynamic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/GLM-4.7-Flash-FP8-Dynamic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/GLM-4.7-Flash-FP8-Dynamic
- SGLang
How to use unsloth/GLM-4.7-Flash-FP8-Dynamic 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 "unsloth/GLM-4.7-Flash-FP8-Dynamic" \ --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": "unsloth/GLM-4.7-Flash-FP8-Dynamic", "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 "unsloth/GLM-4.7-Flash-FP8-Dynamic" \ --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": "unsloth/GLM-4.7-Flash-FP8-Dynamic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use unsloth/GLM-4.7-Flash-FP8-Dynamic 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 unsloth/GLM-4.7-Flash-FP8-Dynamic 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 unsloth/GLM-4.7-Flash-FP8-Dynamic to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/GLM-4.7-Flash-FP8-Dynamic to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/GLM-4.7-Flash-FP8-Dynamic", max_seq_length=2048, ) - Docker Model Runner
How to use unsloth/GLM-4.7-Flash-FP8-Dynamic with Docker Model Runner:
docker model run hf.co/unsloth/GLM-4.7-Flash-FP8-Dynamic
Recommended VLLM nightly has no wheels with x86_64 (intel/amd), only aarch64 (arm64)
Trying to install VLLM nightly as recommended for running this FP8 Dynamic, but getting 'requirements are unsatisfiable' output from uv, below.
On my x86_64 server, uv won't install that nightly.
I'll check in VLLM issues but flagging here in case I'm missing something obvious about your VLLM instructions.
uv pip install --upgrade --force-reinstall vllm --prerelease=allow --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly/
Using Python 3.11.11 environment at: vllm
× No solution found when resolving dependencies:
╰─▶ Because only vllm==0.16.0rc1.dev109+gd95b4be47 is available and vllm==0.16.0rc1.dev109+gd95b4be47 has no wheels with a matching platform tag (e.g.,
manylinux_2_39_x86_64), we can conclude that all versions of vllm cannot be used.
And because you require vllm, we can conclude that your requirements are unsatisfiable.
hint: `vllm` was found on https://wheels.vllm.ai/nightly/, but not at the requested version (all of:
vllm<0.16.0rc1.dev109+gd95b4be47
vllm>0.16.0rc1.dev109+gd95b4be47
). A compatible version may be available on a subsequent index (e.g., https://pypi.org/simple). By default, uv will only consider versions that are
published on the first index that contains a given package, to avoid dependency confusion attacks. If all indexes are equally trusted, use `--index-strategy
unsafe-best-match` to consider all versions from all indexes, regardless of the order in which they were defined.
hint: Wheels are available for `vllm` (v0.16.0rc1.dev109+gd95b4be47) on the following platform: `manylinux_2_31_aarch64`