Instructions to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF",
	filename="h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M
# Run inference directly in the terminal:
llama-cli -hf Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M
# Run inference directly in the terminal:
llama-cli -hf Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_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 Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_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 Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M

Use Docker

docker model run hf.co/Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M

Ollama
How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with Ollama:
```
ollama run hf.co/Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M
```

Unsloth Studio

How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-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 Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-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 Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF to start chatting

Docker Model Runner
How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M
```

Lemonade

How to use Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF:IQ2_M

Run and chat with the model

lemonade run user.h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF-IQ2_M

List all available models

lemonade list

Model Card for Model ID

Imatrix GGUF Quants for: DXP-Zero-V1.0-24b-Small-Instruct.

Recommended Settings

"temperature": 0.8, (Mistral Small 3.1 is sensitive to higher temperatures)
"top_p": 0.95/1,
"min_p": 0.025/0.03,
"repeat_penalty": 1.05/1.1,

IQ2_M: Usable, good for 10-16 GB RAM/VRAM

IQ3_XXS: Very usable, good for 12-20 GB RAM/VRAM

IQ3_M: Solid, good for 14-18 GB RAM/VRAM

IQ4_XS: It's all you need, if you have 16+ GB RAM/VRAM

The model might lack the necessary evil for making story twisty or dark adventure but it make ammend on creating coherent story in long context form.

Perfect for romance, adventure, sci-fi, and even general purpose.

So i was browsing for Mistral finetune and found this base model by ZeroAgency, and oh boy... It was perfect!

So here are few notable improvements i observed. Pros:

Increased output for storytelling or roleplay. Dynamic output (it can adjust how much output, i mean like when you go with shorter prompt it will do smaller outputs and so does with longer prompt more output too). Less repetitive (though it depends on your own prompt and settings). I have tested with 49444/65536 tokens no degradation although i notice it's actually learning the context better and it's impacting the output a lot. (what i don't like is, it's learning the previous context(of turns) too quickly and set it as new standards.).

This model was merged using the TIES merge method using ZeroAgency/Mistral-Small-3.1-24B-Instruct-2503-hf as a base. Models Merged:

PocketDoc/Dans-PersonalityEngine-V1.2.0-24b Gryphe/Pantheon-RP-1.8-24b-Small-3.1

Downloads last month: 15

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

Model tree for Pentium95/h34v7_DXP-Zero-V1.0-24b-Small-iMatrix-GGUF

Base model

h34v7/DXP-Zero-V1.0-24b-Small-Instruct

Quantized

(4)

this model