Instructions to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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 King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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 King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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 King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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 King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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": "King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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 King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-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 King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF to start chatting

Docker Model Runner
How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF

This model was converted to GGUF format from `PocketDoc/Dans-PersonalityEngine-V1.3.0-24b` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Dans-PersonalityEngine-V1.3.0-24b

Multilingual Support

Arabic  Chinese   English  French      German
Hindi   Japanese  Korean   Portuguese  Spanish

Key Details

BASE MODEL: mistralai/Mistral-Small-3.1-24B-Base-2503
LICENSE: apache-2.0
LANGUAGE: Multilingual with 10 supported languages
CONTEXT LENGTH: 32768 tokens, 131072 with degraded recall

Recommended Settings

TEMPERATURE: 1.0
TOP_P: 0.9

Prompting Format

The model uses the following format refered to as
"DanChat-2"

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-24b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-24b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-24b-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-24b-q4_k_m.gguf -c 2048

Downloads last month: 69

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

Finetuned

PocketDoc/Dans-PersonalityEngine-V1.3.0-24b

Quantized

(29)

this model

Datasets used to train King-Cane/Dans-PersonalityEngine-V1.3.0-24b-Q4_K_M-GGUF

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Collection

5 items • Updated Mar 21