README.md

---
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
license: apache-2.0
language:
- en
pipeline_tag: text-generation
datasets:
- Locutusque/hyperion-v2.0
---


This model is frankenmerge from gemma-2b-it.
Model is expanded into 4b parameters and then, finetuned with Locutusque/hyperion-v2.0 (50k)


## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "frankenmerger/gemma-4b-instruct-v0.2"
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"])
```