MODEL: "mychen76/tinyllama-colorist-v2" - is a finetuned TinyLlama model using color dataset.
MOTIVATION: A fun experimental model for using TinyLlama as Llama2 replacement for resource constraint environment.
PROMPT FORMAT: "<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant:""
MODEL USAGE:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
def print_color_space(hex_color):
def hex_to_rgb(hex_color):
hex_color = hex_color.lstrip('#')
return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
r, g, b = hex_to_rgb(hex_color)
print(f'{hex_color}: \033[48;2;{r};{g};{b}m \033[0m')
tokenizer = AutoTokenizer.from_pretrained(model_id_colorist_final)
pipe = pipeline(
"text-generation",
model=model_id_colorist_final,
torch_dtype=torch.float16,
device_map="auto",
)
from time import perf_counter
start_time = perf_counter()
prompt = formatted_prompt('give me a pure brown color')
sequences = pipe(
prompt,
do_sample=True,
temperature=0.1,
top_p=0.9,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=12
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
output_time = perf_counter() - start_time
print(f"Time taken for inference: {round(output_time,2)} seconds")
Result: #807070
Result: <|im_start|>user
give me a pure brown color<|im_end|>
<|im_start|>assistant: #807070<|im_end>
Time taken for inference: 0.19 seconds
Dataset: "burkelibbey/colors"