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).

2026-09-21: chat template updated to accept the 0.18 content-parts form (string form unchanged); weights, tokenizer and executor metadata byte-identical.

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 model.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

Update (July 2026): Google AI Edge Gallery v1.0.16+ can import litert-lm models directly from Hugging Face inside the app (tap +) — no computer or adb needed. The manual steps below are only required on older builds or for sideloading a local file.

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 model.litertlm from this repo and push it to the device:

   adb push model.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).

Run on desktop (LiteRT-LM CLI)

The same .litertlm bundle runs on macOS / Linux / Windows with the official LiteRT-LM CLI — including as a local OpenAI-compatible API server:

pip install litert-lm
litert-lm import --from-huggingface-repo mlboydaisuke/Ministral-3-3B-Instruct-2512-LiteRT model.litertlm ministral-3-3b-instruct-2512-litert
litert-lm run ministral-3-3b-instruct-2512-litert     # interactive chat in the terminal
litert-lm serve           # local OpenAI-compatible API server

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).

2026-08-29 — default system prompt restored (weights unchanged)

The upstream chat template emits a default system turn whenever the caller sends no system message — for this model: You are Ministral-3-3B-Instruct-2512, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris. You power an AI assistant called Le Chat. Your knowledge base was last updated on 2023-10…. The converter's template probe renders the template with a system message already present, so that block was never seen and never reached the bundle: with no system message the model was running without the default system turn it was tuned with. The chat template in model.litertlm now emits the block exactly once when no system message is given. In model.litertlm, the block is not emitted when you pass a system message. The restored block adds 523 prefill tokens to a conversation that sends no system message, so time-to-first-token grows by that much; per-token speed is unchanged.

Metadata-only change: every section of the bundle except the LlmMetadata block is byte-identical to the previous file (verified by sha256 per section), so the weights, the graph and the tokenizer are unchanged and the speed and memory numbers on this card still describe exactly this file per token — only the file's own sha256 differs. What changed is the input: with no system message, the prompt now renders byte-identical to the upstream chat template's output, verified on the LiteRT-LM runtime. A system message you pass yourself renders as before. Greedy decoding can turn on a single token, so an individual answer can differ from the previous file. Unless a row says otherwise, the accuracy figures on this card were measured on the previous file and have not been re-measured on this one. If you downloaded before 2026-08-29, re-download.

Known runtime issues

From the second turn of a conversation on, LiteRT-LM puts the previous answer's stop token into the KV cache with a zero embedding, because this file holds the embedding table in its own section and the decoder takes embeddings, so later answers in a conversation can differ from the answer the model gives to the same messages in one prompt (read in the source of LiteRT-LM 0.16.0 to 0.18.1; measured with 0.18.1 on WeVisOne-VL-2B and Qwen2.5-Coder-1.5B-Instruct, not on this file). One question per conversation avoids it, as does a new conversation that starts with the earlier messages and the new question.

From a conversation preface (the system message, initial messages, tools and extra context a conversation is created with) that has tools and no messages, LiteRT-LM renders this file's chat template with an empty message list, and the template reads messages[0] without checking that the list is non-empty, so the render fails with Failed to apply template: undefined value. This happens in two places: when the preface is prefilled at creation (prefillPrefaceOnInit = true in Kotlin, prefillPrefaceOnInit: true on the web, SetPrefillPrefaceOnInit(true) in C++; the default is false), where creating the conversation fails with that error, and in the preface render (renderPrefaceIntoString() in Kotlin and Swift, RenderPrefaceIntoString() in C++, litert_lm_conversation_render_preface_to_string in the C API). The same happens with extra context (such as enable_thinking) and no messages, and in the preface render with an empty preface (run in the runtime on one Qwen3 template; the other templates rendered with minijinja 2.14 and jinja2). Read in the source of LiteRT-LM 0.17.0, 0.18.1 and main; measured with LiteRT-LM 0.18.1 (the litert-lm package from PyPI) through its C API on this file's template text. The default path, where the first message is rendered together with the preface, is not affected: the Python API and the CLI of 0.18.1 expose neither the flag nor the preface render, and Google AI Edge Gallery sets neither (its source as read on 2026-10-25). A system message or any initial message in the conversation config avoids it.

License

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

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Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.