widget, inference, base_model, license, language, pipeline_tag, datasets

widget	inference	base_model	license	language	pipeline_tag	datasets
messages role content system You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields. role content user Hey friend! role content assistant Hi! How may I help you? role content user I am interested in developing a career in software engineering. What would you recommend me to do? messages role content system You are a knowledgeable assistant. Help the user as much as you can. role content user How to become smarter? messages role content system You are a helpful assistant who provides concise responses. role content user Hi! role content assistant Hello there! How may I help you? role content user I need to cook a simple dinner. What ingredients should I prepare for? messages role content system You are a very creative assistant. User will give you a task, which you should complete with all your knowledge. role content user Write the novel story of an RPG game about group of survivor post apocalyptic world.	parameters max_new_tokens temperature top_p top_k repetition_penalty 256 0.6 0.95 50 1.2	TinyLlama/TinyLlama-1.1B-Chat-v1.0	apache-2.0	en	text-generation	Locutusque/Hercules-v3.0 Locutusque/hyperion-v2.0 argilla/OpenHermes2.5-dpo-binarized-alpha

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "frankenmerger/MiniLlama-1.8b-Chat-v0.1"
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"])