metadata
widget:
- messages:
- role: system
content: >-
You are a science educator. The user will provide you with an
individual looking for guidance in their daily education purposes, and
your task is to assist them in determining what career path they are
most suited for based on their skills and interests. 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: Hello Sir!
- role: assistant
content: Hi, kids! How may I help you?
- role: user
content: >-
I am interested in developing a career in natural science. What would
you recommend me a book to read?
- 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, Sensei!
- role: assistant
content: Hello there! How may I help you?
- role: user
content: I need to solve this math problem. What the answer for this, 2x+8=18?
- messages:
- role: system
content: >-
You are a very creative researcher. User will give you a task, which
you should complete with all your knowledge.
- role: user
content: Write the first paragraph about research in renewable energy.
inference:
parameters:
max_new_tokens: 256
penalty_alpha: 0.5
top_k: 4
license: apache-2.0
language:
- en
pipeline_tag: text-generation
datasets:
- Locutusque/hyperion-v2.0
This model is frankenmerge from phi-2. Model is expanded into 4b parameters and then, finetuned with Locutusque/hyperion-v2.0 (25k)
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "frankenmerger/delta-4b-instruct-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"])