Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
mistralai/Mistral-7B-v0.1
Kukedlc/neuronal-7b-Mlab
mlabonne/Monarch-7B
text-generation-inference
Instructions to use Kukedlc/Triunvirato-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kukedlc/Triunvirato-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kukedlc/Triunvirato-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kukedlc/Triunvirato-7b") model = AutoModelForCausalLM.from_pretrained("Kukedlc/Triunvirato-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Kukedlc/Triunvirato-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kukedlc/Triunvirato-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/Triunvirato-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kukedlc/Triunvirato-7b
- SGLang
How to use Kukedlc/Triunvirato-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/Triunvirato-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/Triunvirato-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/Triunvirato-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/Triunvirato-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kukedlc/Triunvirato-7b with Docker Model Runner:
docker model run hf.co/Kukedlc/Triunvirato-7b
Triunvirato-7b
Trinity-7b is a merge of the following models using LazyMergekit:
๐งฉ Configuration
models:
- model: mistralai/Mistral-7B-v0.1
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: Kukedlc/neuronal-7b-Mlab
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: mlabonne/Monarch-7B
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: true
int8_mask: true
dtype: float16
๐ป Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/Triunvirato-7b"
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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