Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
kaitchup/Mayonnaise-4in1-022
macadeliccc/WestLake-7B-v2-laser-truthy-dpo
vanillaOVO/supermario_v2
FelixChao/WestSeverus-7B-DPO-v2
text-generation-inference
Instructions to use CultriX/Wernicke-7B-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CultriX/Wernicke-7B-v8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CultriX/Wernicke-7B-v8")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CultriX/Wernicke-7B-v8") model = AutoModelForCausalLM.from_pretrained("CultriX/Wernicke-7B-v8") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CultriX/Wernicke-7B-v8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CultriX/Wernicke-7B-v8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CultriX/Wernicke-7B-v8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CultriX/Wernicke-7B-v8
- SGLang
How to use CultriX/Wernicke-7B-v8 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 "CultriX/Wernicke-7B-v8" \ --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": "CultriX/Wernicke-7B-v8", "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 "CultriX/Wernicke-7B-v8" \ --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": "CultriX/Wernicke-7B-v8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CultriX/Wernicke-7B-v8 with Docker Model Runner:
docker model run hf.co/CultriX/Wernicke-7B-v8
Wernicke-7B-v8
Wernicke-7B-v8 is a merge of the following models using LazyMergekit:
- kaitchup/Mayonnaise-4in1-022
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- vanillaOVO/supermario_v2
- FelixChao/WestSeverus-7B-DPO-v2
🧩 Configuration
models:
- model: CultriX/Wernicke-7B-v1
# No parameters necessary for base model
- model: kaitchup/Mayonnaise-4in1-022
parameters:
density: 0.53
weight: 0.40
- model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
density: 0.53
weight: 0.25
- model: vanillaOVO/supermario_v2
parameters:
density: 0.53
weight: 0.25
- model: FelixChao/WestSeverus-7B-DPO-v2
parameters:
density: 0.53
weight: 0.20
merge_method: dare_ties
base_model: CultriX/Wernicke-7B-v1
parameters:
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/Wernicke-7B-v8"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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