Instructions to use batiai/Mistral-Small-4-119B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/Mistral-Small-4-119B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/Mistral-Small-4-119B-GGUF",
	filename="Mistral-Small-4-119B-Q3_K_M-00001-of-00002.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use batiai/Mistral-Small-4-119B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use batiai/Mistral-Small-4-119B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/Mistral-Small-4-119B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "batiai/Mistral-Small-4-119B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

Ollama
How to use batiai/Mistral-Small-4-119B-GGUF with Ollama:
```
ollama run hf.co/batiai/Mistral-Small-4-119B-GGUF:Q4_K_M
```

Unsloth Studio

How to use batiai/Mistral-Small-4-119B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/Mistral-Small-4-119B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/Mistral-Small-4-119B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/Mistral-Small-4-119B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use batiai/Mistral-Small-4-119B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Mistral-Small-4-119B-GGUF:Q4_K_M
```

Lemonade

How to use batiai/Mistral-Small-4-119B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Mistral-Small-4-119B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Small-4-119B-GGUF-Q4_K_M

List all available models

lemonade list

Mistral Small 4 119B-A6B GGUF — Quantized by BatiAI

Mistral's unified open-weight model — reasoning + multimodal + agentic coding in one. 119B Mixture-of-Experts with only 6B active per token, so it runs at small-model speed while keeping frontier quality. Apache 2.0 (fully commercial-friendly, no gating).

Quantized directly from official Mistral weights — not a re-quant of someone else's GGUF. Signed with BatiAI metadata for BatiFlow.

Quick Start

ollama run batiai/mistral-small-4:q4

Available Quantizations

Quant	Size	RAM target	Recommended For
Q3_K_M	54GB	64GB Mac	Compact
Q4_K_M	68GB	96GB Mac	Recommended (balance)
Q5_K_M	79GB	128GB Mac	Max quality

119B total params → these are for 64GB+ Macs (M-series Max/Ultra). The 6B active means inference is fast despite the size. IQ3/IQ4 (smaller, imatrix) can be added on request.

RAM Requirements

Your Mac RAM	Q3 (54GB)	Q4 (68GB)	Q5 (79GB)
64GB	✅ tight	❌	❌
96GB	✅	✅	❌ tight
128GB	✅	✅	✅
192GB+	✅	✅	✅ comfortable

Why Mistral Small 4?

One model, three jobs — reasoning, multimodal understanding, and agentic coding unified (no model-switching).
6B active / 119B total MoE — frontier-class capability at the inference speed/cost of a small model.
Apache 2.0 — no license friction, no gating. Build commercial products freely.
Native llama.cpp support — Mistral ships official GGUF tooling; arch (mistral3) is mainstream.

Why BatiAI Quantization?

Original-source — quantized straight from Mistral's official weights, not a copy of a third-party GGUF.
BatiAI-signed — general.author: BatiAI, general.url: https://flow.bati.ai.
Mac-tuned selection — quant sizes chosen for real Apple Silicon RAM tiers.

Technical Details

Original Model: mistralai/Mistral-Small-4-119B-2603
Architecture: mistral3 MoE, 119B total / ~6B active per token
License: Apache 2.0
Quantized with: llama.cpp (Q8_0 intermediate → K-quants, --allow-requantize)
Quantized by: BatiAI
Note: YaRN config verified clean (no yarn_log_multiplier bug, unlike the earlier Medium 3.5 release).

About BatiFlow

BatiFlow — free, on-device AI automation for Mac. 5MB app, 100% local, unlimited. 60+ tools.

License

Quantized from mistralai/Mistral-Small-4-119B-2603. License: Apache 2.0.

Benchmarks

Mac 하드웨어 실측 벤치 대기 중 (bench.sh). 측정 후 자동 갱신.

Downloads last month: 184

GGUF

Model size

119B params

Architecture

mistral4

Hardware compatibility

3-bit

4-bit

5-bit

Model tree for batiai/Mistral-Small-4-119B-GGUF

Base model

mistralai/Mistral-Small-4-119B-2603

Quantized

(31)

this model