metadata
widget:
- messages:
- role: system
content: >-
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: user
content: Hey friend!
- role: assistant
content: Hi! How may I help you?
- role: user
content: >-
I am interested in developing a career in software engineering. What
would you recommend me to do?
- messages:
- role: system
content: You are a knowledgeable assistant. Help the user as much as you can.
- role: user
content: How to become smarter?
- messages:
- role: system
content: You are a helpful assistant who provides concise responses.
- role: user
content: Hi!
- role: assistant
content: Hello there! How may I help you?
- role: user
content: I need to cook a simple dinner. What ingredients should I prepare for?
- messages:
- role: system
content: >-
You are a very creative assistant. User will give you a task, which
you should complete with all your knowledge.
- role: user
content: >-
Write the novel story of an RPG game about group of survivor post
apocalyptic world.
inference:
parameters:
max_new_tokens: 256
temperature: 0.6
top_p: 0.95
top_k: 50
repetition_penalty: 1.2
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
license: apache-2.0
language:
- en
pipeline_tag: text-generation
datasets:
- 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"])