Instructions to use adilkairolla/zeta-2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adilkairolla/zeta-2.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="adilkairolla/zeta-2.1-GGUF",
	filename="zeta-2.1-Q4_K_M.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 adilkairolla/zeta-2.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf adilkairolla/zeta-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf adilkairolla/zeta-2.1-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 adilkairolla/zeta-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf adilkairolla/zeta-2.1-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 adilkairolla/zeta-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf adilkairolla/zeta-2.1-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 adilkairolla/zeta-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf adilkairolla/zeta-2.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/adilkairolla/zeta-2.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use adilkairolla/zeta-2.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adilkairolla/zeta-2.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adilkairolla/zeta-2.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/adilkairolla/zeta-2.1-GGUF:Q4_K_M

Ollama
How to use adilkairolla/zeta-2.1-GGUF with Ollama:
```
ollama run hf.co/adilkairolla/zeta-2.1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use adilkairolla/zeta-2.1-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 adilkairolla/zeta-2.1-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 adilkairolla/zeta-2.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for adilkairolla/zeta-2.1-GGUF to start chatting

Docker Model Runner
How to use adilkairolla/zeta-2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/adilkairolla/zeta-2.1-GGUF:Q4_K_M
```

Lemonade

How to use adilkairolla/zeta-2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull adilkairolla/zeta-2.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.zeta-2.1-GGUF-Q4_K_M

List all available models

lemonade list

Zeta 2.1 — GGUF

GGUF quantizations of zed-industries/zeta-2.1, a code edit prediction (next-edit suggestion) model from Zed Industries.

These files were produced from the original BF16 safetensors using llama.cpp b9085 (convert_hf_to_gguf.py → llama-quantize). No fine-tuning or weight modification beyond format conversion and quantization.

Files

Quant	Size	Notes
`Q4_K_M`	4.8 GB	Smallest, recommended default for CPU / 8 GB-class GPUs.
`Q5_K_M`	5.5 GB	Quality / size sweet spot.
`Q8_0`	8.2 GB	Near-lossless vs. the original BF16 weights.
`f16`	16 GB	Reference. Useful as the source for further quantization.

Quickstart

Ollama

ollama pull hf.co/adilkairolla/zeta-2.1-GGUF:Q4_K_M

Replace the tag with Q5_K_M, Q8_0, or f16 for a different quant. Zeta is a code-edit-prediction model (not a chat model) — call it via /api/generate with the FIM prompt below, not via /api/chat.

LM Studio

Search for adilkairolla/zeta-2.1-GGUF in the model browser and pick a quant. LM Studio loads it as a base completion model.

llama.cpp

# One-shot completion (correct binary for non-chat models)
./llama-completion -m zeta-2.1-Q4_K_M.gguf -p "$(cat your-prompt.txt)" -n 256 -c 4096

llama-cpp-python

from llama_cpp import Llama
llm = Llama(model_path="zeta-2.1-Q4_K_M.gguf", n_ctx=4096)
out = llm(prompt, max_tokens=256, stop=["<|marker_2|>"], echo=False)
print(out["choices"][0]["text"])

Prompt format

Zeta uses a Suffix-Prefix-Middle (SPM) FIM format with numbered region markers. Quoting the upstream model card:

<[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:

<|marker_1|>
revised content for
the editable region
<|marker_2|>

See the upstream sample.prompt and sample.output for a real example.

Source & lineage

Quantized from: zed-industries/zeta-2.1 (BF16 safetensors)
Fine-tuned from: ByteDance-Seed/Seed-Coder-8B-Base
Architecture: Llama (32 layers, hidden 4096, 32 heads, 8 KV heads, vocab 155136, RoPE θ 500000, ctx 32768)
Conversion tool: llama.cpp b9085

License

Released under the Apache License 2.0, inherited from the upstream model. The only modification relative to upstream is the conversion to GGUF and quantization to the formats listed above.

All credit for the model itself goes to Zed Industries and ByteDance-Seed. This repo is an unaffiliated quantization mirror.

Downloads last month: 871

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for adilkairolla/zeta-2.1-GGUF

Base model

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

Finetuned

zed-industries/zeta-2.1

Quantized

(10)

this model

adilkairolla
/

zeta-2.1-GGUF

Zeta 2.1 — GGUF

Files

Quickstart

Ollama

LM Studio

llama.cpp

llama-cpp-python

Prompt format

Source & lineage

License

Model tree for adilkairolla/zeta-2.1-GGUF

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