poprawki
Browse files
app.py
CHANGED
@@ -2,29 +2,49 @@ import os
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import spaces
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import gradio as gr
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM,
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model_id = "meta-llama/Meta-Llama-3.1-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ.get("MY_API_LLAMA_3_1"))
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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token=os.environ.get("MY_API_LLAMA_3_1"),
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True
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)
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return pipeline("text-generation", model=model, tokenizer=tokenizer)
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@spaces.GPU(duration=60)
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def generate_response(chat, kwargs):
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if
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return output
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def function(prompt, history=[]):
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@@ -33,11 +53,11 @@ def function(prompt, history=[]):
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chat += f"[INST] {user_prompt} [/INST] {bot_response}</s> <s>"
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chat += f"[INST] {prompt} [/INST]"
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kwargs = dict(
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temperature=0.5,
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max_new_tokens=4096,
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top_p=0.95,
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repetition_penalty=1.0,
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do_sample=True,
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seed=1337
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)
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import spaces
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import gradio as gr
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
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from threading import Thread
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from queue import Queue
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model_id = "meta-llama/Meta-Llama-3.1-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ.get("MY_API_LLAMA_3_1"))
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model = None
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model_load_queue = Queue()
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def load_model():
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global model
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if model is None:
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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token=os.environ.get("MY_API_LLAMA_3_1"),
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True
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)
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model_load_queue.put(model)
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@spaces.GPU(duration=60)
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def generate_response(chat, kwargs):
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global model
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if model is None:
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Thread(target=load_model).start()
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model = model_load_queue.get()
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inputs = tokenizer(chat, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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generation_kwargs = dict(inputs, streamer=streamer, **kwargs)
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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output = ""
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for new_text in streamer:
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output += new_text
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if output.endswith("</s>"):
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output = output[:-4]
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break
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return output
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def function(prompt, history=[]):
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chat += f"[INST] {user_prompt} [/INST] {bot_response}</s> <s>"
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chat += f"[INST] {prompt} [/INST]"
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kwargs = dict(
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max_new_tokens=4096,
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do_sample=True,
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temperature=0.5,
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top_p=0.95,
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repetition_penalty=1.0,
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seed=1337
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)
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