Update app.py
Browse files
app.py
CHANGED
@@ -4,7 +4,8 @@ from threading import Thread
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from transformers import StoppingCriteria, StoppingCriteriaList
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import torch
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import spaces
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import os
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model_name = "microsoft/Phi-3-medium-128k-instruct"
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, trust_remote_code=True)
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@@ -17,22 +18,22 @@ class StopOnTokens(StoppingCriteria):
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if input_ids[0][-1] == stop_id:
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return True
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return False
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history_transformer_format = history + [[message, ""]]
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stop = StopOnTokens()
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messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) for item in history_transformer_format])
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#messages = "".join(["".join(["<user>"+item[0], "<output>"+item[1]]) for item in history_transformer_format])
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model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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model_inputs,
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streamer=streamer,
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max_new_tokens=
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do_sample=True,
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top_p=
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top_k=
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temperature=
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stopping_criteria=StoppingCriteriaList([stop])
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)
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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@@ -43,5 +44,14 @@ def predict(message, history):
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partial_message += new_token
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yield partial_message
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demo = gr.ChatInterface(
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from transformers import StoppingCriteria, StoppingCriteriaList
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import torch
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import spaces
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import os
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model_name = "microsoft/Phi-3-medium-128k-instruct"
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, trust_remote_code=True)
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if input_ids[0][-1] == stop_id:
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return True
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return False
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@spaces.GPU(duration=120)
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def predict(message, history, temperature, max_tokens, top_p, top_k):
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history_transformer_format = history + [[message, ""]]
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stop = StopOnTokens()
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messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) for item in history_transformer_format])
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model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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model_inputs,
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streamer=streamer,
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max_new_tokens=max_tokens,
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do_sample=True,
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top_p=top_p,
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top_k=top_k,
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temperature=temperature,
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stopping_criteria=StoppingCriteriaList([stop])
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)
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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partial_message += new_token
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yield partial_message
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demo = gr.ChatInterface(
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fn=predict,
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title="Phi-3-medium-128k-instruct",
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additional_inputs=[
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gr.Slider(0.1, 0.9, value=0.7, label="Temperature"),
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gr.Slider(512, 8192, value=4096, label="Max Tokens"),
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gr.Slider(0.1, 0.9, value=0.7, label="top_p"),
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gr.Slider(10, 90, value=40, label="top_k"),
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]
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)
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demo.launch(share=True)
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