Spaces:
Running
Running
File size: 2,755 Bytes
a498df4 4d1b6b3 a498df4 4d1b6b3 a498df4 3327888 a498df4 4d1b6b3 |
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import Trainer, TrainingArguments
model_name = "HuggingFaceH4/zephyr-7b-beta"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
dataset = load_dataset("json", data_files="data.json", split = "train")
# Tokenize the dataset
def preprocess_function(examples):
inputs = [example['input'] for example in examples]
targets = [examples['output'] for example in examples]
model_inputs = tokenizer(inputs, padding=True, truncation=True)
labels = tokenizer(targets, padding=True, truncation=True).input_ids
model_inputs['labels'] = labels
return model_inputs
tokenized_datasets = dataset.map(preprocess_function, batched = True)
training_args = TrainingArguments(
output_dir = "./results",
evaluation_strategy = "epoch",
learning_rate = 2e-5,
per_device_train_batch_size = 3,
weight_decay = 0.01,
)
trainer = Trainer(
model = model,
args = training_args,
train_dataset = tokenized_datasets["train"],
eval_dataset = tokenized_datasets["validation"],
)
# Start fine-tuning
trainer.train()
trainer.evaluate()
model.save_pretrained("./fine_tuned_model")
tokenizer.save_pretrained("./fine_tuned_model")
client = InferenceClient("./fine_tuned_model")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
# gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
# gr.Slider(
# minimum=0.1,
# maximum=1.0,
# value=0.95,
# step=0.05,
# label="Top-p (nucleus sampling)",
# ),
],
)
if __name__ == "__main__":
demo.launch() |