library_name: mlx
language:
- en
- fr
- es
- de
- it
- pt
- nl
- zh
- ja
- ko
- ar
license: apache-2.0
inference: false
extra_gated_description: >-
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our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- mistral-common
- mlx
- ministral
- ministral-3
- vision-language
- multimodal
- quantized
- edge
- 6-bit
- base-model
pipeline_tag: image-text-to-text
base_model: mistralai/Ministral-3-3B-Base-2512
mlx-community/Ministral-3-3B-Base-2512-6bit
This is, Ministral 3 3B Base 2512 is a vision-language model: a text backbone paired with a vision encoder, supporting image understanding alongside text. This is the base pre-trained checkpoint — not instruction- or chat-tuned. For chat/instruction-following use cases, use the Instruct variant instead; this base checkpoint is intended for custom post-training/fine-tuning.
Community note. Structural check confirms the vision tower and multimodal projector were carried over intact (not dropped, which is a real failure mode for text-only conversion tools on vision-language models). Functional check confirms both text-only and image+text generation produce coherent output. Converted and verified by a single maintainer running local MLX tooling -- not independently reviewed by anyone else; please open a discussion if you hit anything unexpected.
This is an MLX conversion of mistralai/Ministral-3-3B-Base-2512,
converted with mlx-vlm. Refer to the
original model card for the full
description, capabilities, and license terms.
Heads up
- Base model, not instruct-tuned — expect raw completion behavior, not chat-following. Don't expect it to follow instructions well.
- Vision retained at full precision — only the language backbone is quantized; the vision tower and multimodal projector are untouched bf16, per mlx-vlm's standard policy of not quantizing multimodal modules.
- Output size on disk: 3.66GB
Provenance
- Source:
mistralai/Ministral-3-3B-Base-2512(BF16) - Language model layers: 6-bit affine quantization, group_size=64
- Vision tower + multimodal projector: kept at full precision (not quantized)
- Blended average: 7.537 bits per weight across all parameters
Ministral 3 family
| Model | Type | mlx-community (4-bit) |
|---|---|---|
| Ministral 3 3B Base 2512 | Base pre-trained | mlx-community/Ministral-3-3B-Base-2512-4bit |
| Ministral 3 3B Instruct 2512 | Instruct post-trained | mlx-community/Ministral-3-3B-Instruct-2512-4bit |
| Ministral 3 3B Reasoning 2512 | Reasoning capable | mlx-community/Ministral-3-3B-Reasoning-2512-4bit |
| Ministral 3 8B Base 2512 | Base pre-trained | mlx-community/Ministral-3-8B-Base-2512-4bit |
| Ministral 3 8B Instruct 2512 | Instruct post-trained | mlx-community/Ministral-3-8B-Instruct-2512-4bit |
| Ministral 3 8B Reasoning 2512 | Reasoning capable | mlx-community/Ministral-3-8B-Reasoning-2512-4bit |
| Ministral 3 14B Base 2512 | Base pre-trained | mlx-community/Ministral-3-14B-Base-2512-4bit |
| Ministral 3 14B Instruct 2512 | Instruct post-trained | mlx-community/Ministral-3-14B-Instruct-2512-4bit |
| Ministral 3 14B Reasoning 2512 | Reasoning capable | mlx-community/Ministral-3-14B-Reasoning-2512-4bit |
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/Ministral-3-3B-Base-2512-6bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
For text-only prompts, omit --image.
License
Apache 2.0 — see the original model card for the full license text and any usage terms.