Instructions to use vilarin/zeta-2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vilarin/zeta-2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vilarin/zeta-2-GGUF", dtype="auto")

llama-cpp-python

How to use vilarin/zeta-2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vilarin/zeta-2-GGUF",
	filename="zeta-2.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use vilarin/zeta-2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vilarin/zeta-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vilarin/zeta-2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vilarin/zeta-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf vilarin/zeta-2-GGUF: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 vilarin/zeta-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vilarin/zeta-2-GGUF: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 vilarin/zeta-2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vilarin/zeta-2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vilarin/zeta-2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use vilarin/zeta-2-GGUF with Ollama:
```
ollama run hf.co/vilarin/zeta-2-GGUF:Q4_K_M
```

Unsloth Studio

How to use vilarin/zeta-2-GGUF 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 vilarin/zeta-2-GGUF 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 vilarin/zeta-2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vilarin/zeta-2-GGUF to start chatting

Docker Model Runner
How to use vilarin/zeta-2-GGUF with Docker Model Runner:
```
docker model run hf.co/vilarin/zeta-2-GGUF:Q4_K_M
```

Lemonade

How to use vilarin/zeta-2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vilarin/zeta-2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.zeta-2-GGUF-Q4_K_M

List all available models

lemonade list

Zeta 2

Zeta 2 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: 0225-s3-seed

Prompt format

The model uses a SPM (suffix-prefix-middle) style prompt with git-merge-style markers for the editable region:

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
<<<<<<< CURRENT
code that
needs to<|user_cursor|>
be rewritten
=======
<[fim-middle]>

Expected output (should be generated by the model, without backticks):

revised content for
the editable region
>>>>>>> UPDATED

Here is a real-world example:

Downloads last month: 41

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for vilarin/zeta-2-GGUF

Base model

ByteDance-Seed/Seed-Coder-8B-Base

Finetuned

zed-industries/zeta-2

Quantized

(14)

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