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
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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)
|