Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
Hypersniper/The_Philosopher_Zephyr_7B
sayhan/OpenHermes-2.5-Strix-Philosophy-Mistral-7B-LoRA
teknium/Mistral-Trismegistus-7B
text-generation-inference
Instructions to use Kukedlc/Trascendental-Bot-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kukedlc/Trascendental-Bot-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kukedlc/Trascendental-Bot-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kukedlc/Trascendental-Bot-7B") model = AutoModelForCausalLM.from_pretrained("Kukedlc/Trascendental-Bot-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kukedlc/Trascendental-Bot-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kukedlc/Trascendental-Bot-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kukedlc/Trascendental-Bot-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kukedlc/Trascendental-Bot-7B
- SGLang
How to use Kukedlc/Trascendental-Bot-7B 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 "Kukedlc/Trascendental-Bot-7B" \ --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": "Kukedlc/Trascendental-Bot-7B", "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 "Kukedlc/Trascendental-Bot-7B" \ --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": "Kukedlc/Trascendental-Bot-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kukedlc/Trascendental-Bot-7B with Docker Model Runner:
docker model run hf.co/Kukedlc/Trascendental-Bot-7B
Trascendental-Bot-7B
Trascendental-Bot-7B is a merge of the following models using LazyMergekit:
- Hypersniper/The_Philosopher_Zephyr_7B
- sayhan/OpenHermes-2.5-Strix-Philosophy-Mistral-7B-LoRA
- teknium/Mistral-Trismegistus-7B
🧩 Configuration
models:
- model: teknium/Mistral-Trismegistus-7B
# no parameters necessary for base model
- model: Hypersniper/The_Philosopher_Zephyr_7B
parameters:
density: 0.55
weight: 0.3
- model: sayhan/OpenHermes-2.5-Strix-Philosophy-Mistral-7B-LoRA
parameters:
density: 0.55
weight: 0.3
- model: teknium/Mistral-Trismegistus-7B
parameters:
density: 0.4
weight: 0.66
merge_method: dare_ties
base_model: teknium/Mistral-Trismegistus-7B
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/Trascendental-Bot-7B"
messages = [
{"role": "system", "content": "You are an expert assistant in mysticism and philosophy."},
{"role": "user", "content": "Create an innovative and disruptive theory that explains human consciousness. Give me an extensive and detailed answer."}
]
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=1024, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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