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---
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"])
``` |