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Update app.py
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app.py
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@@ -4,23 +4,29 @@ from peft import PeftModel
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import torch
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import re
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#
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base_model = AutoModelForCausalLM.from_pretrained(
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torch_dtype=torch.float16,
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device_map="auto"
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model = PeftModel.from_pretrained(base_model, "Quanttum/crypto-llama-lora-final")
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_new_tokens=512,
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temperature=0.3,
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repetition_penalty=1.15,
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)
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@@ -35,17 +41,28 @@ TL;DR:"""
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output = pipe(prompt)[0]["generated_text"]
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response = output[len(prompt):].strip()
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return response
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chat,
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examples=[
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["Bitcoin ETF inflows hit $1.5B this week
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["SEC approves spot Ethereum ETF!"],
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["
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],
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cache_examples=True,
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)
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import torch
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import re
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# Public, ungated base model + your LoRA (works 100% in Spaces)
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base_name = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
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lora_name = "Quanttum/crypto-llama-lora-final"
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# Load base model (no auth needed)
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tokenizer = AutoTokenizer.from_pretrained(base_name)
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base_model = AutoModelForCausalLM.from_pretrained(
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base_name,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Load your LoRA on top (your fine-tuning!)
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model = PeftModel.from_pretrained(base_model, lora_name)
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# Pipeline for generation
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_new_tokens=512,
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temperature=0.3,
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top_p=0.9,
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repetition_penalty=1.15,
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)
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output = pipe(prompt)[0]["generated_text"]
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response = output[len(prompt):].strip()
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# Simple score extraction
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score_match = re.search(r"[-+]?\d*\.\d+", response[-20:])
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score = score_match.group(0) if score_match else "N/A"
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full_response = response + f"\n\nSentiment Score: {score}"
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return full_response
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# ChatGPT-style UI
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with gr.Blocks(theme=gr.themes.Soft(), title="Crypto News Summarizer") as demo:
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gr.Markdown("# π Crypto News Summarizer + Sentiment Analyzer\nFine-tuned Llama 3.1 8B by @Quanttum")
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chatbot = gr.ChatInterface(
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chat,
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examples=[
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["Bitcoin ETF inflows hit $1.5B this week as institutional adoption surges."],
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["Mt. Gox starts repaying creditors in BTC β 140k coins incoming."],
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["SEC approves spot Ethereum ETF!"],
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["China just banned all crypto trading again."],
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],
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title=None,
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description="Paste any crypto news or tweet β get instant TL;DR + sentiment score (-1.0 to +1.0)",
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cache_examples=True,
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)
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