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") - 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
Best model I've tried so far
I've tested like 10 or 15 different models, but this is the first one that generates the most naturally sounding and coherent texts.
I think its power is in its ability go generate casual language and not the pompous overly-prosaic / overly-philosophical tone that the most famous models seem to generate when asked to create stories or chats.
Am I correct in my understanding that it has a 4K context?
I've tested like 10 or 15 different models, but this is the first one that generates the most naturally sounding and coherent texts.
I think its power is in its ability go generate casual language and not the pompous overly-prosaic / overly-philosophical tone that the most famous models seem to generate when asked to create stories or chats.Am I correct in my understanding that it has a 4K context?
Hi! Yes, it's based on Llama 2 so it have 4k context
Ah, interesting - and sorry for being a noob (just starting out in this field) but - is there any chance for a similar model with a bigger context? e.g. is the L3 one expected to have a bigger context? or is there one with a different base and a larger context but similar outputs?
The thing is - I'm trying to write a long story so maybe there's some technique I'm missing regarding how to continue generating it once the maximum context has been reached ? Is there some sort of a "rolling" mechanism I can employ where the initial prompt is maintained and only middle prompts are lost along the way as it generates more content?
Ah, interesting - and sorry for being a noob (just starting out in this field) but - is there any chance for a similar model with a bigger context? e.g. is the L3 one expected to have a bigger context? or is there one with a different base and a larger context but similar outputs?
Sadly this is an old model from when I was merging more than finetuning, this model come from a big merge, and not a dataset that I could train on Llama3 or other LLM with bigger context. So I can't recreate it.
It was more a big experimentation of when I tried frankenmerge to have bigger model back then.