Zacoda Lite 1.1
A small-footprint model built to genuinely reason, not just format nice-looking answers — a 4B model meant to run comfortably on modest hardware while still holding its own on real reasoning tasks.
Download
| Format | Precision | Size | Notes |
|---|---|---|---|
mlx/8bit/ |
MLX quantized | ~7.2 GB | Recommended for Mac — MLX runtime |
pytorch_8bit/ |
int8 weight-only (torchao) | ~9.9 GB | Recommended for transformers — needs pip install torchao |
pytorch/ |
safetensors (bf16) | ~14 GB | Full precision, for use with transformers / standard PyTorch workflows |
A 4-bit build didn't clear our own quality bar (garbled tokens and wrong arithmetic on repeated testing) — rather than ship something we don't trust, we held it back. The next series bakes in a lot more quantization-aware training from the start specifically so smaller quants hold up properly — expect a real 4-bit (and smaller) release then. No GGUF this round either — this checkpoint's per-layer embedding table isn't sized the way current llama.cpp Gemma4 support expects, so a converted file wouldn't actually load; not worth shipping something broken.
Benchmark — ZACBENCH-600
Our internal 6-category, 600-question benchmark (General Knowledge, Logic & Reasoning, Math, Coding, Instruction-Following, English). Full runs, thinking mode on, shown here against the rest of the current family for context.
| Category | Nano 1.1 (2B) | Lite 1.0 (4B) | Lite 1.1 (4B) | Plus 1.1 (12B)* |
|---|---|---|---|---|
| General Knowledge | 88% | 100% | 97% | 98% |
| Logic & Reasoning | 77% | 96% | 68% | 85% |
| Math | 27% | 89% | 31% | 41% |
| Coding | 52% | 14% | 64% | not scored* |
| Instruction-Following | 52% | 74% | 58% | 83% |
| English | 77% | 94% | 88% | not scored* |
| Overall | 62.2% (373/600) | 77.8% (467/600) | 67.7% (406/600) | partial |
*Plus 1.1 wasn't run through the full suite — Coding hit a non-terminating generation loop under greedy decoding that we haven't fixed yet, and English wasn't benchmarked at all, so its column isn't a fair apples-to-apples total. Shown anyway since the sections that do exist are genuinely good numbers.
Weaknesses
- Math is the clear weak spot at 31%. The most common pattern is the model working through the right method in its reasoning but slipping on the final arithmetic step — an execution error more than a conceptual one.
- Instruction-Following (58%) lags General Knowledge and English — multi-constraint prompts (exact formats, strict word counts) are where it trips up most.
- Logic (68%) is solid on its own but not a strength the way GK and English are.
- Worth being upfront about: Logic, Math, and Instruction-Following all sit meaningfully below Lite 1.0's numbers, even though Coding jumped a lot. Not hiding that — it's a real step back in a few areas alongside the real step forward in others, not an unqualified upgrade.
What's next
On Lite 1.1's regressions above trace back to a training data mix quality issue on our end for this release. We're sorry about that; it's not the bar we hold ourselves to, and we know it.
Lite 1.2 is being built on a reworked, quality-controlled data mix specifically to fix this — it's coming clean. Targeted for August 7. Zacagent Lite 1.2 (built for tool use and agentic tasks specifically) follows on August 10.
Usage
MLX
pip install mlx-lm
from huggingface_hub import snapshot_download
from mlx_lm import load, generate
# mlx_lm.load() doesn't take a `subfolder=` argument -- fetch the specific
# precision folder first, then load from the resulting local path.
path = snapshot_download("The-AI-makers88/Zacoda-Lite-1.1", allow_patterns=["mlx/8bit/*"])
model, tokenizer = load(f"{path}/mlx/8bit")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello!"}], add_generation_prompt=True
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))
PyTorch / transformers (8-bit, recommended)
pip install torchao
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("The-AI-makers88/Zacoda-Lite-1.1", subfolder="pytorch_8bit")
tokenizer = AutoTokenizer.from_pretrained("The-AI-makers88/Zacoda-Lite-1.1", subfolder="pytorch_8bit")
PyTorch / transformers (full bf16)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("The-AI-makers88/Zacoda-Lite-1.1", subfolder="pytorch")
tokenizer = AutoTokenizer.from_pretrained("The-AI-makers88/Zacoda-Lite-1.1", subfolder="pytorch")
Help spread the word
If this model's useful to you, sharing it around — a repost, a mention, a link in a Discord or forum — genuinely helps more than almost anything else at this stage. I'm a solo dev without any budget, so word of mouth is most of what we've got.
Made by Sean Zhang
Quantized