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import gradio as gr
from huggingface_hub import InferenceClient
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co./docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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()
from datasets import load_dataset
ds = load_dataset("AI-MO/NuminaMath-CoT")
from datasets import load_dataset
from datasets import load_dataset
import gradio as gr
def show_data():
# 加载数据集
dataset = load_dataset("AI-MO/NuminaMath-CoT")
train_data = dataset["train"][:5]
return train_data
# 使用 Gradio 界面显示测试数据
demo = gr.Interface(fn=show_data, inputs=None, outputs="text", title="数据集测试")
demo.launch()
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
# 加载预训练模型和分词器
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
model = AutoModelForCausalLM.from_pretrained(model_name)
# 数据集预处理
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# 微调训练参数
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=4,
num_train_epochs=3,
weight_decay=0.01,
)
# 微调
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
)
trainer.train()
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