Instructions to use Undi95/MXLewdMini-L2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/MXLewdMini-L2-13B with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/MXLewdMini-L2-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/MXLewdMini-L2-13B")
model = AutoModelForCausalLM.from_pretrained("Undi95/MXLewdMini-L2-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Undi95/MXLewdMini-L2-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/MXLewdMini-L2-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/MXLewdMini-L2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Undi95/MXLewdMini-L2-13B

SGLang

How to use Undi95/MXLewdMini-L2-13B 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 "Undi95/MXLewdMini-L2-13B" \
    --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": "Undi95/MXLewdMini-L2-13B",
		"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 "Undi95/MXLewdMini-L2-13B" \
        --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": "Undi95/MXLewdMini-L2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Undi95/MXLewdMini-L2-13B with Docker Model Runner:
```
docker model run hf.co/Undi95/MXLewdMini-L2-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merge:

[Xwin (0.66) + ReMM (0.33)] x [Xwin (0.33) + MLewd (0.66)]

The goal was to recreate https://huggingface.co/Undi95/MXLewd-L2-20B in 13B without using merge interlacing (will probably be a little less good).

Models used

Undi95/MLewd-L2-13B-v2-3
Undi95/ReMM-v2.1-L2-13B
Xwin-LM/Xwin-LM-13B-V0.1

One part is ReMM (0.33) and Xwin (0.66)

One part is Xwin (0.33) and MLewd (0.66)

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that completes the request.

### Instruction:
{prompt}

### Response:

Downloads last month: 9

Safetensors

Model size

13B params

Tensor type

F32

BF16

Model tree for Undi95/MXLewdMini-L2-13B

Quantizations

3 models