----------------------------------------------- - Update August 14th 2026 - -----------------------------------------------
New chat template! Yes, a new chat template that completely fixes previous issues for llama.cpp users and retains higher performance than the originally made template for Ollama/Yollama users!
Fixed both F16 and F32 MMPROJ Multi-Modal Vision projectors. (Re-Uploaded with working projectors)
Uploaded custom, high-quality, new "GH05T" edition Quant files for this model!
(This is a brand new method I have been developing for some time, I sincerely hope you enjoy it!)
Custom GH05T Quants Added:
These offer significantly higher performance and quality of response over previously uploaded release files while remaining
similar or smaller in size!
IQ2_M
Q2_K_L
IQ3_M
Q3_K_L
IQ4_XS
Q4_K_M
Q5_K_XL
Q6_K_L
Q8_0_L
F16
New JINJA Tokenizer Chat-Template: (This template features a sliding context window of TWENTY-NINE (29) messages. This can be adjusted per-individual requirements simply by altering the number 29 in the template higher or lower in numerical value)
jinja
1{%- set ns = namespace(remMessage=false, hasSys=false, injSystem=true) -%}
2{%- for msg in messages -%}
3 {%- if msg.role == "system" -%}
4 {%- set ns.hasSys = true -%}
5 {%- endif -%}
6{%- endfor -%}
7{%- for msg in messages -%}
8 {{- bos_token }}
9 {%- if ns.injSystem -%}
10 [SYSTEM_PROMPT]
11 {%- if ns.hasSys -%}
12 {{ msg.content }}
13 {%- else -%}
14 'Follow instructions the user provides... (System Prompt)'
15 {%- endif -%}
16 [/SYSTEM_PROMPT]
17 {%- set ns.injSystem = false -%}
18 {%- endif -%}
19 {%- if (messages|length - loop.index0) < 29 -%}
20 {%- set ns.remMessage = true -%}
21 {%- endif -%}
22 {%- if ns.remMessage -%}
23 {%- if msg.role == "user" -%}
24 {{- '[INST]' }}
25 {%- if msg.content is string %}
26 {{ msg.content }}
27 {%- else %}
28 {%- for block in msg.content %}
29 {%- if block.type == 'text' %}
30 {{- block.text }}
31 {%- elif block.type in ['image', 'image_url'] %}
32 {{- '[IMG]' }}
33 {%- endif %}
34 {%- endfor %}
35 {%- endif %}
36 {{- '[/INST]' }}
37 {%- elif msg.role == "assistant" -%}
38 {{ msg.content }}
39 {%- endif -%}
40 {%- endif -%}
41 {{- eos_token }}
42{%- endfor -%}
------------------------------------------------ - Model Details and Specifications: - ------------------------------------------------
Ministral-3 3B Instruct 2512 (GGUF)
This release contains:
GGUF converted and Quantized model files
(Compatible with:)
-------------------------- ---- Original Info ---- --------------------------
(Crossposted from the link in the above section: "Model Details"):
Ministral 3 14B Instruct 2512 BF16
The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities.
This model is the instruct post-trained version, fine-tuned for instruction tasks, making it ideal for chat and instruction based use cases.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 14B can even be deployed locally, capable of fitting in 32GB of VRAM in BF16, and less than 24GB of RAM/VRAM when quantized.
We provide a no-loss FP8 version here, you can find other formats and quantizations in the Ministral 3 - Additional Checkpoints collection.
Key Features
Ministral 3 14B consists of two main architectural components:
13.5B Language Model
0.4B Vision Encoder
The Ministral 3 14B Instruct model offers the following capabilities:
Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
System Prompt: Maintains strong adherence and support for system prompts.
Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
Large Context Window: Supports a 256k context window.
Use Cases
Private AI deployments where advanced capabilities meet practical hardware constraints:
Private/custom chat and AI assistant deployments in constrained environments
Advanced local agentic use cases
Fine-tuning and specialization
And more...
Bringing advanced AI capabilities to most environments.
You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.
