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Update app.py
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app.py
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@@ -12,7 +12,7 @@ HF_TOKEN = os.environ.get("HF_TOKEN", None)
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DESCRIPTION = '''
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<div>
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<h1 style="text-align: center;">Meta Llama3 8B</h1>
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<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/
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<p>🔎 For more details about the Llama3 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
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<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
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</div>
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@@ -49,9 +49,12 @@ h1 {
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"""
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained("
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@spaces.GPU(duration=120)
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def chat_llama3_8b(message: str,
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@@ -84,6 +87,7 @@ def chat_llama3_8b(message: str,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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temperature=temperature,
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)
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# This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
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if temperature == 0:
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@@ -126,7 +130,6 @@ with gr.Blocks(fill_height=True, css=css) as demo:
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render=False ),
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],
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examples=[
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["The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1."],
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['How to setup a human base on Mars? Give short answer.'],
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['Explain theory of relativity to me like I’m 8 years old.'],
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['What is 9,000 * 9,000?'],
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DESCRIPTION = '''
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<div>
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<h1 style="text-align: center;">Meta Llama3 8B</h1>
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<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
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<p>🔎 For more details about the Llama3 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
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<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
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</div>
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"""
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto") # to("cuda:0")
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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@spaces.GPU(duration=120)
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def chat_llama3_8b(message: str,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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temperature=temperature,
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eos_token_id=terminators,
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)
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# This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
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if temperature == 0:
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render=False ),
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],
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examples=[
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['How to setup a human base on Mars? Give short answer.'],
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['Explain theory of relativity to me like I’m 8 years old.'],
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['What is 9,000 * 9,000?'],
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