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gemma-4b-instruct-v0.2 - GGUF
- Model creator: https://huggingface.co/frankenmerger/
- Original model: https://huggingface.co/frankenmerger/gemma-4b-instruct-v0.2/
Original model description:
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
!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"])
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