Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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from huggingface_hub import InferenceClient
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def respond(
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messages.append({"role": "user", "content": message})
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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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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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# 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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# 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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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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# 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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generated_text = response_output[0]['generated_text']
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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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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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"""
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