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
| base_model: ByteDance-Seed/Seed-Coder-8B-Base | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - edit-prediction | |
| - next-edit-suggestion | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # Zeta 2.1 | |
| Zeta 2.1 is a code edit prediction (also known as next-edit suggestion) model finetuned from `ByteDance-Seed/Seed-Coder-8B-Base`. | |
| Given code context, edits history and an editable region around the cursor, it predicts the rewritten content for that region. | |
| - **Developed by:** Zed Industries | |
| - **License:** Apache-2.0 | |
| - **Fine-tuned from:** ByteDance-Seed/Seed-Coder-8B-Base | |
| - **Model version:** 0323-multi-region-filtered-r3 | |
| ## Prompt format | |
| The model uses a SPM (suffix-prefix-middle) style prompt with numbered multi-region markers for editable regions: | |
| Here is a minimal example: | |
| ``` | |
| <[fim-suffix]> | |
| code after editable region | |
| <[fim-prefix]><filename>related/file.py | |
| related file content | |
| <filename>edit_history | |
| --- a/some_file.py | |
| +++ b/some_file.py | |
| -old | |
| +new | |
| <filename>path/to/target_file.py | |
| code before editable region | |
| <|marker_1|> | |
| code that | |
| needs to<|user_cursor|> | |
| be rewritten | |
| <|marker_2|> | |
| <[fim-middle]> | |
| ``` | |
| Expected output (should be generated by the model, without backticks): | |
| ``` | |
| <|marker_1|> | |
| revised content for | |
| the editable region | |
| <|marker_2|> | |
| ``` | |
| Here is a real-world example: | |
| - [Sample prompt input](./sample.prompt) | |
| - [Sample model output](./sample.output) | |