Uploaded model
- Developed by: suriya7
- License: apache-2.0
- Finetuned from model : unsloth/gemma-2b
This Gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
Requirements
pip install torch
pip install transformers
Inference In Notebook
import torch
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer
tokenizer = AutoTokenizer.from_pretrained("suriya7/Gemma-2b-SFT")
model = AutoModelForCausalLM.from_pretrained("suriya7/Gemma-2b-SFT")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
inputs = tokenizer(
[
alpaca_prompt.format(
"You are an AI assistant. Please ensure that the answers conclude with an end-of-sequence (EOS) token.", # instruction
"how to cook pizza?", # input goes here
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to(device)
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 250,do_sample=True,temperature=0.7,top_k=2,repetition_penalty=1.5, # Penalize repeated responses
eos_token_id=model.config.eos_token_id)
Recommended Prompt Template
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
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