metadata
library_name: transformers
tags:
- trl
- sft
license: apache-2.0
datasets:
- gokaygokay/prompt-enhancement-75k
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-135M-Instruct
pipeline_tag: text-generation
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "gokaygokay/SmolLM2-135M-Instruct-Prompt-Enhance"
tokenizer_id = "HuggingFaceTB/SmolLM2-135M-Instruct"
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id )
model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
# Model response generation functions
def generate_response(model, tokenizer, instruction, device="cpu"):
"""Generate a response from the model based on an instruction."""
messages = [{"role": "user", "content": instruction}]
input_text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(
inputs, max_new_tokens=256, repetition_penalty=1.2
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
def print_response(response):
"""Print the model's response."""
print(f"Model response:")
print(response.split("assistant\n")[-1])
print("-" * 100)
prompt = "cat"
response = generate_response(model, tokenizer, prompt, device)
print_response(response)