Ministral-3-3B-Instruct-2512

Ministral-3-3B-Instruct-2512 — LiteRT-LM (blockwise int4)

mistralai/Ministral-3-3B-Instruct-2512 converted to the LiteRT-LM (.litertlm) format for on-device inference with Google's LiteRT-LM runtime (the engine behind the official litert-community/* models).

Text-only conversion (the Ministral-3 text decoder; the Pixtral vision tower is dropped).

Usage

Run with the LiteRT-LM runtime:

# build litert-lm from https://github.com/google-ai-edge/litert-lm, then:
litert_lm_main \
  --model_path Ministral-3-3B-Instruct-2512_q4_block32_ekv4096.litertlm \
  --backend gpu \
  --input_prompt "Explain on-device AI in one sentence."

The .litertlm bundle carries the tokenizer and the prompt template (Ministral's native Mistral [INST] … [/INST] format, stop token </s>), so no separate tokenizer files are needed. This is a direct-answering instruct model (no <think> block) and terminates cleanly at </s>.

Run on Android

The easiest way to try this model on a phone is the official Google AI Edge Gallery app — it runs .litertlm models fully on-device and can import your own:

1. Install a recent Gallery (package com.google.ai.edge.gallery, APK from the repo's releases — 1.0.15+ supports .litertlm). Older 1.0.x builds (package com.google.aiedge.gallery) only accept the legacy MediaPipe .task format and reject .litertlm.
2. Download Ministral-3-3B-Instruct-2512_q4_block32_ekv4096.litertlm from this repo and push it to the device:

   adb push Ministral-3-3B-Instruct-2512_q4_block32_ekv4096.litertlm /sdcard/Download/
   

3. In the app, tap the + button (bottom-right), pick the file, and choose the GPU backend (CPU also works).
4. Chat. Nothing else to configure — the .litertlm bundle already carries the tokenizer and prompt template, so the model uses its native Mistral [INST] chat format automatically.

Device RAM (important — this is a ~2.7 GB / 3B model): GPU on Android needs roughly 2× the model size (the weights plus the ML Drift GPU weight cache), so GPU is only offered on ~12 GB+ devices. On an 8 GB phone (e.g. Pixel 8a) only CPU is selectable, and you must free RAM first (close apps / reboot → ~4 GB free) or the app is OOM-killed on load. For smaller phones, prefer a 1–2B model (e.g. a Qwen3-1.7B .litertlm), which runs comfortably and can use the GPU.

See the Gallery Importing Local Models guide for details. To embed the model in your own Android app instead, use the LiteRT-LM Kotlin API (Gradle artifact com.google.ai.edge.litertlm:litertlm-android, getting started).

Quality — GSM8K parity

Measured on GSM8K (n=100, greedy, 0-shot chain-of-thought asking for #### <n>, identical prompt and answer-extraction for both rows so the only variable is the on-device quantization:

LiteRT int4 is at parity: −4 pt vs bf16 with no reasoning collapse. The model also passes the local quality gate 8/8 (non-degenerate, clean stop at </s>). 85% is a strong on-device GSM8K for a 3B and far above a naive min-max int4 of the same model (blockwise-32 + OCTAV optimal-clipping is what preserves the accuracy).

Conversion

Converted with litert-torch using a blockwise int4 recipe (INT4 weights, block size 32, symmetric, OCTAV optimal-clipping) with the tied embedding/lm_head kept at INT8, KV cache 4096, and Ministral's native Mistral chat template. Ministral-3 is a standard dense decoder (Ministral3ForCausalLM, YaRN RoPE), so it rides the existing converter and runtime with no custom graph code; only the text decoder is exported (the vision tower is dropped first).

externalize_embedder=True (required for iPhone). This 3B's weights would otherwise be a single ~2.55 GiB TFLite section, which exceeds the ~2 GiB single-section mmap limit on iOS — engine creation fails with "Failed to map section: Cannot allocate memory". Externalizing the (tied) embedding into its own section drops the main weights section to ~1.8 GiB (and dedups the tied matrix, ~2.74 GB → 2.34 GB total), so the model loads on iPhone (Metal GPU) as well as Android/desktop. Same weights, so GSM8K parity is unchanged. Verified on-device: iPhone 17 Pro loads in ~7.6 s and decodes at ~17.6 tok/s on the Metal GPU (prefill 21.9 tok/s, TTFT 0.70 s, ~1.4 GB footprint) — the previously-failing ">2 GiB section / Cannot allocate memory" mmap error no longer occurs.

Template note (important for any Mistral/Ministral): the model must be exported with its native Mistral [INST] … [/INST] template and real EOS </s> — not ChatML. Mistral's tekken tokenizer has no <|im_end|> token, so under a ChatML template the int4 model never hits a registered stop token and runs away after the correct answer. With the Mistral template it stops cleanly.

Reproduce

Built with litert-torch and a blockwise-32 + OCTAV int4 recipe, forcing the simple Mistral [INST] chat template (the model's full jinja template doesn't render in the runtime's minimal jinja engine, so the structured [INST] prefixes are extracted instead):

EXTERNALIZE_EMBEDDER=1 CACHE=4096 python scripts/export_simple_template.py \
    src_models/ministral3-3b-text \
    out/ministral3-3b-boctav4 \
    templates/mistral_simple.jinja \
    BOCTAV4   # blockwise-32 int4 + OCTAV, int8 embeddings

The equivalent ai_edge_quantizer recipe is included as ministral3_int4_block32_octav.json. The text decoder is extracted from the multimodal checkpoint with scripts/extract_ministral3_text.py (drops the vision tower; loads with missing=0/unexpected=0).

Training data & PII

This is a weights-exact format conversion of mistralai/Ministral-3-3B-Instruct-2512; no new training was performed. Ministral was trained by Mistral AI on a large-scale, undisclosed proprietary mixture of web, code, and multilingual text. As the corpus is web-derived it may incidentally contain personal data/PII; none was deliberately collected and this format conversion adds none. Apply your own content/PII filtering before deployment. See the base model card for available details.

License

Apache-2.0, inherited from the base model mistralai/Ministral-3-3B-Instruct-2512.