Text Generation
Transformers
Safetensors
glm4
mergekit
Merge
rolelplay
creative_writing
conversational
Instructions to use Delta-Vector/Plesio-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delta-Vector/Plesio-32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Delta-Vector/Plesio-32B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Plesio-32B") model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Plesio-32B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Delta-Vector/Plesio-32B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Delta-Vector/Plesio-32B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Delta-Vector/Plesio-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Delta-Vector/Plesio-32B
- SGLang
How to use Delta-Vector/Plesio-32B 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 "Delta-Vector/Plesio-32B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Delta-Vector/Plesio-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Delta-Vector/Plesio-32B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Delta-Vector/Plesio-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Delta-Vector/Plesio-32B with Docker Model Runner:
docker model run hf.co/Delta-Vector/Plesio-32B
metadata
base_model:
- Delta-Vector/Rei-V1-32B-Base
- allura-org/GLM4-32B-Neon-v2
library_name: transformers
tags:
- mergekit
- merge
- rolelplay
- creative_writing
Plesio-32B
Created by
Delta-Vector
→
Model Information
Plesio-32B
Another Series of Merges! Since i could never beat Archaeo-32B-KTO! This time starting off with a GLM merge between Rei and Neon (thanks auri!!!)
Using the Oh-so-great 0.2 Slerp merge weight with Neon as the Base.
Support me on Ko-Fi: https://ko-fi.com/deltavector
Quantized Versions
Available Downloads
- GGUF FormatFor use with LLama.cpp & Forks(Coming Soon!)
- EXL2 FormatFor use with TabbyAPI (Coming Soon!)
- EXL3 FormatFor use with TabbyAPI (Slower on Ampere))
Prompting
Model has been tuned with the GLM-4 formatting.
Samplers
For testing of this model, I used Temp=1, 0.1 Min-P.
See Merging Config
https://files.catbox.moe/j9kyfy.yml
Credits
Thank you to Lucy Knada, Auri, Ateron, Alicat, Intervitens, Cgato, Kubernetes Bad and the rest of Anthracite.