Instructions to use Undi95/MXLewd-L2-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Undi95/MXLewd-L2-20B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Undi95/MXLewd-L2-20B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Undi95/MXLewd-L2-20B") model = AutoModelForCausalLM.from_pretrained("Undi95/MXLewd-L2-20B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Undi95/MXLewd-L2-20B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Undi95/MXLewd-L2-20B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Undi95/MXLewd-L2-20B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Undi95/MXLewd-L2-20B
- SGLang
How to use Undi95/MXLewd-L2-20B 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/MXLewd-L2-20B" \ --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/MXLewd-L2-20B", "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/MXLewd-L2-20B" \ --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/MXLewd-L2-20B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Undi95/MXLewd-L2-20B with Docker Model Runner:
docker model run hf.co/Undi95/MXLewd-L2-20B
| license: cc-by-nc-4.0 | |
| Merge: | |
| ```shell | |
| layer_slices: | |
| - model: ./MXLewd-L2-20B-part2 | |
| start: 0 | |
| end: 16 | |
| - model: ./MXLewd-L2-20B-part1 | |
| start: 8 | |
| end: 20 | |
| - model: ./MXLewd-L2-20B-part2 | |
| start: 17 | |
| end: 32 | |
| - model: ./MXLewd-L2-20B-part1 | |
| start: 21 | |
| end: 40 | |
| ``` | |
| Part 2 is ReMM (0.33) and Xwin (0.66) | |
| Part 1 is Xwin (0.33) and MLewd (0.66) | |
| <!-- description start --> | |
| ## Models used | |
| - Undi95/MLewd-L2-13B-v2-3 | |
| - Undi95/ReMM-v2.1-L2-13B | |
| - Xwin-LM/Xwin-LM-13B-V0.1 | |
| <!-- description end --> | |
| ## Prompt template: Alpaca | |
| ``` | |
| Below is an instruction that describes a task. Write a response that completes the request. | |
| ### Instruction: | |
| {prompt} | |
| ### Response: | |
| ``` |