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Browse files- app.py +45 -19
- requirements.txt +5 -1
app.py
CHANGED
@@ -2,40 +2,66 @@ import gradio as gr
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from huggingface_hub import InferenceClient
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"""
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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history: list[tuple[str, str]],
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system_message,
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max_tokens,
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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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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response += token
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yield response
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from huggingface_hub import InferenceClient
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"""
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Copied from inference in colab notebook
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"""
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from transformers import AutoTokenizer , AutoModelForSeq2SeqLM , TextIteratorStreamer
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from threading import Thread
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# Load model and tokenizer globally to avoid reloading for every request
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base_model = "Helsinki-NLP/europarl"
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model_path = "Mat17892/t5small_enfr_opus"
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True, legacy=False)
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# Load the base model (e.g., LLaMA)
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base_model = AutoModelForSeq2SeqLM.from_pretrained(base_model)
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# Load LoRA adapter
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from peft import PeftModel
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model = PeftModel.from_pretrained(base_model, model_path)
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def respond(
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message: str,
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history: list[tuple[str, str]],
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system_message: str,
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max_tokens: int,
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temperature: float,
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top_p: float,
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):
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# Combine system message and history into a single prompt
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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# Tokenize the messages
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize = True,
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add_generation_prompt = True, # Must add for generation
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return_tensors = "pt",
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)
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# Generate tokens incrementally
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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generation_kwargs = {
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"input_ids": inputs,
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"max_new_tokens": max_tokens,
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"temperature": temperature,
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"top_p": top_p,
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"do_sample": True,
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"streamer": streamer,
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}
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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# Yield responses as they are generated
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response = ""
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for token in streamer:
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response += token
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yield response
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requirements.txt
CHANGED
@@ -1 +1,5 @@
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huggingface_hub==0.25.2
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huggingface_hub==0.25.2
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transformers
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accelerate
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peft
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