---
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

```python
!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"])
```