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import gradio as gr
import os, gc, torch
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 1024
title = "RWKV-4-Pile-7B-Instruct-test4-20230326"
os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
from rwkv.model import RWKV
model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-pile-7b", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cuda fp16i8 *20 -> cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "20B_tokenizer.json")
def generate_prompt(instruction, input=None):
if input:
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Input:
{input}
# Response:
"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Response:
"""
def evaluate(
instruction,
input=None,
token_count=200,
temperature=1.0,
top_p=0.7,
**kwargs,
):
prompt = generate_prompt(instruction, input)
return prompt
g = gr.Interface(
fn=evaluate,
inputs=[
gr.components.Textbox(lines=2, label="Instruction", value="Tell me about alpacas."),
gr.components.Textbox(lines=2, label="Input", placeholder="none"),
gr.components.Slider(minimum=10, maximum=250, step=10, value=200),
gr.components.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0),
gr.components.Slider(minimum=0, maximum=1, step=0.05, value=0.7),
],
outputs=[
gr.inputs.Textbox(
lines=5,
label="Output",
)
],
title="🐦Raven-RWKV 7B",
description="Raven-RWKV 7B is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and more.",
)
g.queue(concurrency_count=1, max_size=10)
g.launch(share=False)
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