Instructions to use Elfrino/PsyMedLewd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elfrino/PsyMedLewd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Elfrino/PsyMedLewd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Elfrino/PsyMedLewd") model = AutoModelForCausalLM.from_pretrained("Elfrino/PsyMedLewd") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Elfrino/PsyMedLewd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Elfrino/PsyMedLewd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Elfrino/PsyMedLewd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Elfrino/PsyMedLewd
- SGLang
How to use Elfrino/PsyMedLewd 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 "Elfrino/PsyMedLewd" \ --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": "Elfrino/PsyMedLewd", "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 "Elfrino/PsyMedLewd" \ --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": "Elfrino/PsyMedLewd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Elfrino/PsyMedLewd with Docker Model Runner:
docker model run hf.co/Elfrino/PsyMedLewd
merge
PsyMedLewd. A merge of two of my favourite models for scifi stories:
Currently testing more merge iterations of these two models.
RECOMMENDED SETTINGS FOR ALL PsyMedLewd VERSIONS
(based on KoboldCPP):
Temperature - 1.3
Max Ctx. Tokens - 4096
Top p Sampling - 0.99
Repetition Penalty - 1.09
Amount to Gen. - 512
Prompt template: Alpaca or ChatML
First iteration. More to come..
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This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: Undi95/PsyMedRP-v1-20B
layer_range: [0, 62] # PsyMedRP has 62 layers
- model: Undi95/MXLewd-L2-20B
layer_range: [0, 62] # MXLewd has 62 layers
merge_method: slerp # Or use another method like weight_average if needed
base_model: Undi95/MXLewd-L2-20B # Can use either as the base model
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1] # Tune these for desired effect
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # Default averaging weight
dtype: bfloat16 # Use preferred dtype, like fp16 or float32 if needed
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