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Duplicate from FredZhang7/chat-test
Browse filesCo-authored-by: Fred Zhang <FredZhang7@users.noreply.huggingface.co>
- .gitattributes +35 -0
- README.md +14 -0
- app.py +33 -0
- ultrachat-13B-test.py +27 -0
- ultrachat-13b-performance.md +7 -0
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README.md
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---
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title: Chat Test
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emoji: π
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 3.36.1
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app_file: app.py
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pinned: false
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license: apache-2.0
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duplicated_from: FredZhang7/chat-test
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import os
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os.system("pip install --upgrade torch transformers sentencepiece scipy cpm_kernels accelerate bitsandbytes")
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True)
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# model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("openchat/openchat_v2_w")
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model = AutoModelForCausalLM.from_pretrained("openchat/openchat_v2_w", load_in_8bit_fp32_cpu_offload=True, load_in_8bit=True)
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model.float()
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model = model.eval()
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model_path = model.config._dict['model_name_or_path']
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model_size_gb = os.path.getsize(model_path) / (1024 * 1024 * 1024)
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print(f"The model '{model_name}' is taking approximately {model_size_gb:.2f} GB of disk space.")
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# with gr.Blocks() as demo:
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# chatbot = gr.Chatbot()
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# msg = gr.Textbox()
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# clear = gr.ClearButton([msg, chatbot])
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# def respond(message, chat_history):
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# response, chat_history = model.chat(tokenizer, message, history=chat_history, temperature=0.7, repetition_penalty=1.2, max_length=128)
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# chat_history.append((message, response))
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# return "", chat_history
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# msg.submit(respond, [msg, chatbot], [msg, chatbot])
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# if __name__ == "__main__":
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# demo.launch()
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ultrachat-13B-test.py
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import os
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os.system("pip install llama-cpp-python")
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from gradio import Interface, Textbox, Slider
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import requests
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from llama_cpp import Llama
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url = "https://huggingface.co/TheBloke/UltraLM-13B-GGML/resolve/main/ultralm-13b.ggmlv3.q4_0.bin"
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response = requests.get(url)
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with open("ultralm-13b.ggmlv3.q4_0.bin", "wb") as f:
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f.write(response.content)
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# os.system("pwd")
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# os.system("ls")
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llm = Llama(model_path="ultralm-13b.ggmlv3.q4_0.bin", n_ctx=2048)
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def generate_text(prompt, temperature, max_length):
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prompt_template=f'''USER: {prompt}
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ASSISTANT:'''
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output = llm.create_completion(prompt_template, temperature=temperature, max_tokens=max_length, echo=True, stop=["USER:"])
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print(output)
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return output
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Interface(fn=generate_text, inputs=[Textbox(type="text", lines=10), Slider(minimum=0, maximum=2, step=0.1, value=0.7), Slider(minimum=1, maximum=2048, step=2, value=256)], outputs=Textbox(type="text", lines=20), title="UltraChat 13B Text Generation", description="Enter a prompt to generate text.").launch()
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ultrachat-13b-performance.md
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Default Config, with temperature=0.7 and max_tokens=256
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| Response Time | Prompt |
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|----------------|------------------------|
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| 350+ seconds | "Hi" |
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| 1000+ seconds | an inappropriate word |
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| N/A | https://flowgpt.com/prompt/af5bcff8-899e-4d79-a968-c4a3b38c5954?isModal=true |
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