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
metadata
license: cc-by-nc-4.0
Merge:
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)
Models used
- Undi95/MLewd-L2-13B-v2-3
- Undi95/ReMM-v2.1-L2-13B
- Xwin-LM/Xwin-LM-13B-V0.1
Prompt template: Alpaca
Below is an instruction that describes a task. Write a response that completes the request.
### Instruction:
{prompt}
### Response:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 51.29 |
| ARC (25-shot) | 63.23 |
| HellaSwag (10-shot) | 85.33 |
| MMLU (5-shot) | 57.36 |
| TruthfulQA (0-shot) | 51.65 |
| Winogrande (5-shot) | 76.09 |
| GSM8K (5-shot) | 10.92 |
| DROP (3-shot) | 14.46 |