Spaces:
Runtime error
Runtime error
File size: 2,631 Bytes
5ab8047 bf44e50 3f3bd49 bf44e50 3f3bd49 bf44e50 3f3bd49 bf44e50 3f3bd49 2f11b13 bf44e50 3f3bd49 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, pipeline
import torch
import gradio as gr
# chatgpt-gpt4-prompts-bart-large-cnn-samsum
tokenizer = AutoTokenizer.from_pretrained(
"Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum")
model = AutoModelForSeq2SeqLM.from_pretrained(
"Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum", from_tf=True)
# zephyr
# pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto")
hf_model_id = "HuggingFaceH4/zephyr-7b-alpha"
model = AutoModelForCausalLM.from_pretrained(hf_model_id)
tokenizerZephyr = AutoTokenizer.from_pretrained(hf_model_id, legacy=False)
generation_config, unused_kwargs = GenerationConfig.from_pretrained(hf_model_id, max_new_tokens=200, temperature=0.7, return_unused_kwargs=True)
model.generation_config = generation_config
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizerZephyr,
)
pipe(prompt)
def useZephyr(prompt):
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate.",
},
{"role": "user", "content": prompt},
]
# https://huggingface.co/docs/transformers/main/en/chat_templating
outputs = pipe(prompt)
return outputs[0]["generated_text"]
def generatePrompt(prompt, max_new_tokens):
batch = tokenizer(prompt, return_tensors="pt")
generated_ids = model.generate(
batch["input_ids"], max_new_tokens=int(max_new_tokens))
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
prompt = output[0]
return useZephyr(prompt)
def generate_test(prompt):
batch = tokenizer(prompt, return_tensors="pt")
generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
return output[0]
def generate_prompt(prompt, max_new_tokens):
return generatePrompt(prompt, max_new_tokens)
#
# Interface
input_prompt = gr.Textbox(label="Prompt", value="photographer")
input_maxtokens = gr.Textbox(label="Max tokens", value="150")
output_component = gr.Textbox(label="Output")
examples = [["photographer"], ["developer"], ["teacher"], [
"human resources staff"], ["recipe for ham croquettes"]]
description = ""
PerfectGPT = gr.Interface(useZephyr, inputs=[input_prompt, input_maxtokens], outputs=output_component,
examples=examples, title="🗿 PerfectGPT v1 🗿", description=description)
PerfectGPT.launch() |