NobodyWho/Mistral_Ministral-3-8B-Instruct-GGUF

Overview

GGUF quantization of Mistral AI's Ministral 3 8B Instruct (Ministral-3-8B-Instruct-2512), prepared for NobodyWho: it works with NobodyWho out of the box, with Mistral's recommended sampling metadata embedded in every quant, and is verified with NobodyWho's test suite. Ministral 3 8B is a recent, edge-focused multimodal model (8.4B language model + 0.4B vision encoder) with best-in-class agentic tool calling, released under the Apache 2.0 license.

Model Capabilities

• Text generation — instruction-following chat
• Tool calling — native function calling with JSON output and grammar constraints
• Vision — image understanding via the companion mmproj-BF16.gguf projection model
• Long context — 256k tokens
• Multilingual — dozens of languages

Available Quantizations

Verified with NobodyWho's tool-calling suite across BF16 / Q8_0 / Q4_K_M (14/14 each, June 2026); vision and multilingual verified. The upstream GGUF has no general.sampling.* metadata, so the -vendor-sampling files embed Mistral's recommended sampler (see INJECTION.md).

Quick Start

Using the NobodyWho library:

from nobodywho import Chat

chat = Chat("huggingface:NobodyWho/Mistral_Ministral-3-8B-Instruct-GGUF/Ministral-3-8B-Instruct-2512-Q4_K_M-vendor-sampling.gguf")
response = chat.ask("What is the capital of Denmark?").completed()
print(response)  # The capital of Denmark is Copenhagen.

Vision

from nobodywho import Model, Chat, Prompt, Image, Text

model = Model(
    "huggingface:NobodyWho/Mistral_Ministral-3-8B-Instruct-GGUF/Ministral-3-8B-Instruct-2512-Q4_K_M-vendor-sampling.gguf",
    projection_model_path="huggingface:NobodyWho/Mistral_Ministral-3-8B-Instruct-GGUF/mmproj-BF16.gguf",
)
chat = Chat(model=model, system_prompt="You are a helpful assistant.")
response = chat.ask(Prompt([
    Text("What is in this image?"),
    Image("./photo.png"),
])).completed()
print(response)

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="NobodyWho/Mistral_Ministral-3-8B-Instruct-GGUF",
    filename="Ministral-3-8B-Instruct-2512-Q4_K_M-vendor-sampling.gguf",
)

Model Specifications

• Parameters: 8.4B language model + 0.4B vision encoder
• Context length: 262,144 tokens (256K)
• License: Apache 2.0
• Base model: mistralai/Ministral-3-8B-Instruct-2512
• Architecture: mistral3 (vision-capable)

Licensing / Credits

Licensed under Apache 2.0 (unchanged from upstream). All model credit belongs to Mistral AI. GGUF quantizations provided by unsloth.