Instructions to use ysn-rfd/TinyMistral-248M-v2.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ysn-rfd/TinyMistral-248M-v2.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ysn-rfd/TinyMistral-248M-v2.5-GGUF",
	filename="tinymistral-248m-v2.5-q6_k.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 ysn-rfd/TinyMistral-248M-v2.5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K

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 ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K

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 ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K

Use Docker

docker model run hf.co/ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use ysn-rfd/TinyMistral-248M-v2.5-GGUF with Ollama:
```
ollama run hf.co/ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K
```

Unsloth Studio

How to use ysn-rfd/TinyMistral-248M-v2.5-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 ysn-rfd/TinyMistral-248M-v2.5-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 ysn-rfd/TinyMistral-248M-v2.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ysn-rfd/TinyMistral-248M-v2.5-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use ysn-rfd/TinyMistral-248M-v2.5-GGUF with Docker Model Runner:
```
docker model run hf.co/ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K
```

Lemonade

How to use ysn-rfd/TinyMistral-248M-v2.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ysn-rfd/TinyMistral-248M-v2.5-GGUF:Q6_K

Run and chat with the model

lemonade run user.TinyMistral-248M-v2.5-GGUF-Q6_K

List all available models

lemonade list

ysn-rfd/TinyMistral-248M-v2.5-GGUF

This model was converted to GGUF format from Locutusque/TinyMistral-248M-v2.5 using llama.cpp via the ggml.ai's all-gguf-same-where space. Refer to the original model card for more details on the model.

✅ Quantized Models Download List

🔍 Recommended Quantizations

✨ General CPU Use: Q4_K_M (Best balance of speed/quality)
📱 ARM Devices: Q4_0 (Optimized for ARM CPUs)
🏆 Maximum Quality: Q8_0 (Near-original quality)

📦 Full Quantization Options

🚀 Download	🔢 Type	📝 Notes
Download		Basic quantization
Download		Small size
Download		Balanced quality
Download		Better quality
Download		Fast on ARM
Download		Fast, recommended
Download	⭐	Best balance
Download		Good quality
Download		Balanced
Download		High quality
Download	🏆	Very good quality
Download	⚡	Fast, best quality
Download		Maximum accuracy

💡 Tip: Use F16 for maximum precision when quality is critical

🚀 Applications and Tools for Locally Quantized LLMs

🖥️ Desktop Applications

Application	Description	Download Link
Llama.cpp	A fast and efficient inference engine for GGUF models.	GitHub Repository
Ollama	A streamlined solution for running LLMs locally.	Website
AnythingLLM	An AI-powered knowledge management tool.	GitHub Repository
Open WebUI	A user-friendly web interface for running local LLMs.	GitHub Repository
GPT4All	A user-friendly desktop application supporting various LLMs, compatible with GGUF models.	GitHub Repository
LM Studio	A desktop application designed to run and manage local LLMs, supporting GGUF format.	Website
GPT4All Chat	A chat application compatible with GGUF models for local, offline interactions.	GitHub Repository

📱 Mobile Applications

Application	Description	Download Link
ChatterUI	A simple and lightweight LLM app for mobile devices.	GitHub Repository
Maid	Mobile Artificial Intelligence Distribution for running AI models on mobile devices.	GitHub Repository
PocketPal AI	A mobile AI assistant powered by local models.	GitHub Repository
Layla	A flexible platform for running various AI models on mobile devices.	Website

🎨 Image Generation Applications

Application	Description	Download Link
Stable Diffusion	An open-source AI model for generating images from text.	GitHub Repository
Stable Diffusion WebUI	A web application providing access to Stable Diffusion models via a browser interface.	GitHub Repository
Local Dream	Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference.	GitHub Repository
Stable-Diffusion-Android (SDAI)	An open-source AI art application for Android devices, enabling digital art creation.	GitHub Repository

Downloads last month: 15

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

Inference Providers NEW

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Model tree for ysn-rfd/TinyMistral-248M-v2.5-GGUF

Base model

Locutusque/TinyMistral-248M-v2.5

Quantized

(6)

this model

Datasets used to train ysn-rfd/TinyMistral-248M-v2.5-GGUF

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

24.570
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

27.490
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

23.150
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

46.720
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

47.830
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

13.360
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

3.180