Instructions to use bouroo/zeta-2.1-mlx-mxfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bouroo/zeta-2.1-mlx-mxfp4 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("bouroo/zeta-2.1-mlx-mxfp4") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use bouroo/zeta-2.1-mlx-mxfp4 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "bouroo/zeta-2.1-mlx-mxfp4" --prompt "Once upon a time"
- Atomic Chat
bouroo/zeta-2.1-mlx-mxfp4
A native MLX MXFP4 quantization of zed-industries/zeta-2.1, an 8B code edit-prediction / autocomplete model.
Quality note: Uniform MXFP4 quantization is provided as requested. In local
mlx_lmsmoke tests, the model loaded and ran but the canonical FIM prompt sometimes collapsed to repeated<|marker_1|>tokens. For reliable edit-prediction quality, use the MXFP8 variant or the original bf16 model. This artifact may still be useful for general code completion or experimentation.
Quantization details
| Property | Value |
|---|---|
| Method | MLX MXFP4 |
| Bits | 4 |
| Group size | 32 |
| Measured BPW | 4.250 |
| Size on disk | 4.1 GB |
| Reference signal | bf16 |
MXFP4 is a block floating-point format supported by MLX on Apple Silicon. All eligible linear weights use MXFP4; the config records the quantization parameters.
About the base model
Zeta 2.1 is a code edit-prediction model (next-edit suggestion) finetuned from ByteDance-Seed/Seed-Coder-8B-Base. It uses an SPM-style, multi-marker FIM format rather than a chat template.
Prompt format
<[fim-suffix]>{code after cursor}
<[fim-prefix]><filename>{file_path}
{code before cursor}<|marker_1|><|marker_2|>
<[fim-middle]>
See examples/zeta_fim.py for a standard-library client targeting LM Studio.
Usage
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tokenizer = load("bouroo/zeta-2.1-mlx-mxfp4")
prompt = "<[fim-suffix]>\n return total\n<[fim-prefix]><filename>app.py\ndef sum_list(items):\n<|marker_1|><|marker_2|>\n<[fim-middle]>"
out = generate(model, tokenizer, prompt=prompt, max_tokens=128,
sampler=make_sampler(temp=0.2, top_p=0.95))
LM Studio
lms get https://huggingface.co/bouroo/zeta-2.1-mlx-mxfp4
lms load zeta-2.1-mlx-mxfp4
LM Studio's built-in autocomplete uses a simple FIM template and cannot format Zeta's multi-marker edit-prediction prompt. Use the client in examples/ or a compatible editor integration.
Verification
The model was converted with mlx_lm and confirmed loadable with local mlx_lm. See the quality note above for the observed uniform-MXFP4 FIM behavior.
Quantized and published under Apache-2.0, inherited from the base model.
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Model tree for bouroo/zeta-2.1-mlx-mxfp4
Base model
ByteDance-Seed/Seed-Coder-8B-Base