kanana-2-3b-instruct-MLX-4bit

kanana-2-3b-instruct-MLX-4bit

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An MLX conversion of kakaocorp/kanana-2-3b-instruct so the model runs natively on Apple Silicon. Weights are quantized to MLX affine 4-bit (group_size=32).

This is an independent community conversion. It is not affiliated with or endorsed by Kakao Corp.

Changes from the original

This repository changes the file format only — the architecture is stock Qwen3ForCausalLM, which mlx-lm supports directly, so no modelling code was written or altered.

1. Converted the PyTorch/safetensors weights to MLX format with mlx-lm.
2. Applied MLX affine 4-bit quantization (group_size=32, not the mlx-lm default of 64 — see Quantization below).

No weights were retrained. Numerics are unchanged apart from the quantization noted above. Per Kanana Open License §3.1(iii), this section is the notice that the distributed files were modified by the maintainer of this repository.

Usage

pip install mlx-lm

mlx_lm.generate --model choipilkyu/kanana-2-3b-instruct-MLX-4bit --prompt "안녕하세요"

from mlx_lm import load, generate

model, tokenizer = load("choipilkyu/kanana-2-3b-instruct-MLX-4bit")
messages = [{"role": "user", "content": "안녕하세요"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))

Variants

There is no bf16 build here on purpose. The upstream repository is already bfloat16 safetensors and mlx-lm supports the architecture, so

from mlx_lm import load
model, tokenizer = load("kakaocorp/kanana-2-3b-instruct")   # works as-is

loads the original directly. A bf16 repo under this account would be a 5.9 GB copy of bytes you can already fetch, so the table above cites the upstream repo as the baseline instead. For most uses 8-bit is the right default: half the size, and the perplexity difference from the original is inside the measurement error.

Speed and memory

Measured on an Apple M1 Pro (16 GB), macOS 26.4, mlx 0.32.0, mlx-lm 0.31.3 with the command the upstream BENCHMARKS.md uses, averaged over three trials:

mlx_lm.benchmark --model choipilkyu/kanana-2-3b-instruct-MLX-4bit -p 2048 -g 128

Generation is roughly 2.4x faster at 4-bit than at bf16, on 2.3x less memory — that is the reason to quantize at all, and on a 16 GB machine the memory column is what decides whether the model runs beside anything else.

Prompt processing goes the other way, and that is expected. Prefill is compute-bound, so dequantizing weights costs more than the narrower memory traffic saves; generation is memory-bound, so the same trade runs in reverse. If your workload is dominated by long prompts rather than long completions, the quantized builds are not faster.

These numbers describe one machine. They are not comparable to figures measured on other hardware, and the ratios between variants transfer better than the absolute values.

Quantization

Perplexity measured with the official mlx_lm.perplexity tool: 60 samples of 1,024 tokens from allenai/tulu-3-sft-mixture, the same sample set for every variant (the loader shuffles unseeded, so the seed is fixed explicitly — otherwise each variant is scored on different text and the numbers are not comparable).

The 4-bit build uses group_size=32, not the mlx-lm default of 64. For this model the default is too coarse: perplexity degrades +33.4% at g64 against +15.9% at g32, for 0.2 GB more on disk.

That is a fact about this model, not a rule about small models. The same comparison, run the same way on three other Korean models, gives three different answers:

Model size predicts nothing here: the two extremes are a 3B and a 7.3B. Measure group size per model rather than defaulting either way — including defaulting to what worked here.

Mixed-precision recipes

mlx-lm ships four mixed_N_M recipes: most modules are quantized to N bits and a fixed 14% — v_proj/down_proj in the outer eighths of the stack and every third layer, plus lm_head — is promoted to M. All four were built at group_size=32 and scored on the same tokens as the table above.

What sets the floor is the low bits, not the boost. Nothing below 4-bit survives at this model size however generous the promotion is, because the promotion reaches only 14% of the modules — going from a 4-bit boost to a 6-bit one buys mixed_3_6 a halving of an already unusable number. Only mixed_4_6 is published.

That one does beat uniform 4-bit at the same group size: 4.982 against 5.104. Because both builds are scored on identical tokens the comparison can be paired, which takes token difficulty out of the variance — the mean difference over the 61,380 shared tokens is +0.0242 ± 0.0020 nats (t = 11.9), against 0.09 GB more on disk. Comparing the two published standard errors instead would overstate the uncertainty eightfold, since it treats the shared text as if each model had been scored on its own sample.

The recipe's advantage is also specific to this model. On Midm-2.0-Mini-Instruct the identical comparison runs the other way and just as decisively — mixed_4_6 loses to uniform 4-bit at t = -19 while being the larger file. Neither the recipe nor the group size transfers; both are worth an hour of measurement per model.

Caveat: this is an English corpus, and Kanana is a Korean-centric model. The numbers measure quantization damage, not Korean ability.

What is still missing, and what is planned

Perplexity is a proxy. It shows that a build has been damaged; it does not show what capability was lost, and it is measured here on English text for a model whose reason to exist is Korean.

The evaluation that would answer the real question is the benchmark set Kakao reports on their own card — KMMLU and HAE-RAE — run against each build and reported as retention against the upstream model. The standard way to do that is mlx_lm.evaluate, which wraps EleutherAI's lm-evaluation-harness; both Korean task suites are already in it.

That measurement is planned, and the numbers will be added here when they exist. They will not be estimated from the perplexity figures above — the two do not substitute for each other, and a 0.5% perplexity change is not evidence about a benchmark score in either direction.

License and use restrictions

The model weights are Kakao's, released under the Kanana Open License Agreement — a copy travels with this repository as LICENSE, together with the required NOTICE.

Use is subject to Kakao's Guidelines for Responsible AI (§2.2 of the agreement). By using these weights you accept those restrictions, and if you redistribute them you must pass the same obligation on to your recipients.

A separate commercial licence from Kakao is required (§4.1) before you:

• offer third parties access to the model through an API, cloud platform or other remote access service;
• ship it to third parties as part of a system-integration or on-premise solution; or
• embed it in an on-device product offered to third parties — worth reading carefully here, since an Apple Silicon build is an on-device artifact by nature.

Using it to build and run your own service does not require that licence (§4.2). This summary is not legal advice; read the agreement.

Kanana is licensed in accordance with the Kanana Open License Agreement. Copyright © KAKAO Corp. All Rights Reserved.