schuler commited on
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cc932be
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1 Parent(s): 8b4e5de

Update app.py

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  1. app.py +39 -6
app.py CHANGED
@@ -1,10 +1,21 @@
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  import gradio as gr
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- from huggingface_hub import InferenceClient
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- """
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- 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
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- """
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- client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
 
 
 
 
 
 
 
 
 
 
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  def respond(
@@ -25,8 +36,29 @@ def respond(
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  messages.append({"role": "user", "content": message})
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- response = ""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  for message in client.chat_completion(
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  messages,
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  max_tokens=max_tokens,
@@ -38,6 +70,7 @@ def respond(
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  response += token
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  yield response
 
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  """
 
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  import gradio as gr
 
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+ import os
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+ from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, pipeline
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+ import torch
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+
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+ # Define the model repository
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+ REPO_NAME = 'schuler/experimental-JP47D20'
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+ # REPO_NAME = 'schuler/experimental-JP47D21-KPhi-3-micro-4k-instruct'
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+
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+ # How to cache?
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+ def load_model(repo_name):
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+ tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
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+ generator_conf = GenerationConfig.from_pretrained(repo_name)
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+ model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True, torch_dtype=torch.bfloat16)
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+ return tokenizer, generator_conf, model
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+
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+ tokenizer, generator_conf, model = load_model(REPO_NAME)
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  def respond(
 
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  messages.append({"role": "user", "content": message})
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+ for message in messages:
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+ role = "<|assistant|>" if message['role'] == 'assistant' else "<|user|>"
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+ prompt += f"\n{role}\n{message['content']}\n<|end|>\n"
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+ # prompt += f"\n<|user|>\n{user_text}\n<|end|><|assistant|>\n"
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+
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+ # Generate the response
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+ response_output = generator(
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+ prompt,
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+ generation_config=generator_conf,
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+ max_new_tokens=64,
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+ do_sample=True,
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+ top_p=0.25,
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+ repetition_penalty=1.2
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+ )
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+
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+ generated_text = response_output[0]['generated_text']
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+
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+ # st.session_state.last_response = generated_text
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+ # Extract the assistant's response
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+ yield generated_text[len(prompt):].strip()
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+
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+ """
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  for message in client.chat_completion(
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  messages,
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  max_tokens=max_tokens,
 
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  response += token
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  yield response
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+ """
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  """