Instructions to use Heralax/MistralMakise-Merged-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Heralax/MistralMakise-Merged-13b with Transformers:

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

pipe = pipeline("text-generation", model="Heralax/MistralMakise-Merged-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Heralax/MistralMakise-Merged-13b")
model = AutoModelForCausalLM.from_pretrained("Heralax/MistralMakise-Merged-13b")

llama-cpp-python

How to use Heralax/MistralMakise-Merged-13b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Heralax/MistralMakise-Merged-13b",
	filename="mistralmakisemerged-q5km.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Heralax/MistralMakise-Merged-13b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Heralax/MistralMakise-Merged-13b
# Run inference directly in the terminal:
llama-cli -hf Heralax/MistralMakise-Merged-13b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Heralax/MistralMakise-Merged-13b
# Run inference directly in the terminal:
llama-cli -hf Heralax/MistralMakise-Merged-13b

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 Heralax/MistralMakise-Merged-13b
# Run inference directly in the terminal:
./llama-cli -hf Heralax/MistralMakise-Merged-13b

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 Heralax/MistralMakise-Merged-13b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Heralax/MistralMakise-Merged-13b

Use Docker

docker model run hf.co/Heralax/MistralMakise-Merged-13b

LM Studio
Jan

vLLM

How to use Heralax/MistralMakise-Merged-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Heralax/MistralMakise-Merged-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heralax/MistralMakise-Merged-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Heralax/MistralMakise-Merged-13b

SGLang

How to use Heralax/MistralMakise-Merged-13b 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 "Heralax/MistralMakise-Merged-13b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heralax/MistralMakise-Merged-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Heralax/MistralMakise-Merged-13b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heralax/MistralMakise-Merged-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Heralax/MistralMakise-Merged-13b with Ollama:
```
ollama run hf.co/Heralax/MistralMakise-Merged-13b
```

Unsloth Studio new

How to use Heralax/MistralMakise-Merged-13b 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 Heralax/MistralMakise-Merged-13b 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 Heralax/MistralMakise-Merged-13b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Heralax/MistralMakise-Merged-13b to start chatting

Docker Model Runner
How to use Heralax/MistralMakise-Merged-13b with Docker Model Runner:
```
docker model run hf.co/Heralax/MistralMakise-Merged-13b
```

Lemonade

How to use Heralax/MistralMakise-Merged-13b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Heralax/MistralMakise-Merged-13b

Run and chat with the model

lemonade run user.MistralMakise-Merged-13b-{{QUANT_TAG}}

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The same dataset and training settings as MythoMakise, but with the ReMM mistral model as a base. See that model card for more info on prompt formats and such.

Downloads last month: 42

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for Heralax/MistralMakise-Merged-13b

Quantizations

3 models