Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
edit-prediction
next-edit-suggestion
Instructions to use zed-industries/zeta-2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zed-industries/zeta-2.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zed-industries/zeta-2.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zed-industries/zeta-2.1") model = AutoModelForCausalLM.from_pretrained("zed-industries/zeta-2.1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zed-industries/zeta-2.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zed-industries/zeta-2.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zed-industries/zeta-2.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zed-industries/zeta-2.1
- SGLang
How to use zed-industries/zeta-2.1 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 "zed-industries/zeta-2.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zed-industries/zeta-2.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "zed-industries/zeta-2.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zed-industries/zeta-2.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zed-industries/zeta-2.1 with Docker Model Runner:
docker model run hf.co/zed-industries/zeta-2.1
| <|marker_1|> return; | |
| }; | |
| let path = project.read(cx).path_for_entry(*active_entry_id, cx); | |
| if let Some(path) = path { | |
| if let Some(ix) = project_state | |
| .recent_paths | |
| .iter() | |
| .position(|probe| probe == &path) | |
| { | |
| project_state.recent_paths.remove(ix); | |
| } | |
| project_state.recent_paths.push_front(path); | |
| } | |
| } | |
| project::Event::DiskBasedDiagnosticsFinished<|user_cursor|> { .. } => { | |
| if cx.has_flag::<EditPredictionJumpsFeatureFlag>() { | |
| <|marker_2|> |