darknoon commited on
Commit
d10e0fb
·
1 Parent(s): 697a1f0

minor changes, based on https://huggingface.co/spaces/huggingface-projects/gemma-2-9b-it

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Files changed (1) hide show
  1. app.py +3 -2
app.py CHANGED
@@ -12,10 +12,11 @@ model_path = "facebook/chameleon-7b"
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  # model = ChameleonForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
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  # processor = ChameleonProcessor.from_pretrained(model_path)
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  model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
 
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  processor = ChameleonProcessor.from_pretrained(model_path)
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  tokenizer = processor.tokenizer
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- @spaces.GPU
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  def respond(
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  message,
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  history: list[tuple[str, str]],
@@ -40,11 +41,11 @@ def respond(
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  image = Image.open(requests.get("https://uploads4.wikiart.org/images/paul-klee/death-for-the-idea-1915.jpg!Large.jpg", stream=True).raw)
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  inputs = processor(prompt, images=[image], return_tensors="pt").to(model.device, dtype=torch.bfloat16)
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- # out = model.generate(**inputs, max_new_tokens=40, do_sample=False)
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  streamer = TextIteratorStreamer(tokenizer)
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  generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
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  thread = Thread(target=model.generate, kwargs=generation_kwargs)
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  thread.start()
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  # model = ChameleonForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
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  # processor = ChameleonProcessor.from_pretrained(model_path)
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  model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
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+ model.eval()
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  processor = ChameleonProcessor.from_pretrained(model_path)
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  tokenizer = processor.tokenizer
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+ @spaces.GPU(duration=90)
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  def respond(
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  message,
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  history: list[tuple[str, str]],
 
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  image = Image.open(requests.get("https://uploads4.wikiart.org/images/paul-klee/death-for-the-idea-1915.jpg!Large.jpg", stream=True).raw)
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  inputs = processor(prompt, images=[image], return_tensors="pt").to(model.device, dtype=torch.bfloat16)
 
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  streamer = TextIteratorStreamer(tokenizer)
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  generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
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+ # launch generation in the background
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  thread = Thread(target=model.generate, kwargs=generation_kwargs)
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  thread.start()
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