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Browse files- Dockerfile +22 -0
- README.md +8 -9
- app.py +38 -0
- botnb.png +0 -0
- gitattributes +35 -0
- readme.txt +1 -0
- requirements.txt +2 -0
- user.png +0 -0
Dockerfile
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FROM python:3.10
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WORKDIR /app
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COPY ./requirements.txt /app/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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RUN wget https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/resolve/main/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf -O model.gguf
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RUN useradd -m -u 1000 user
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USER user
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COPY --chown=user . .
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CMD ["python", "app.py"]
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README.md
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---
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title: Hermes
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emoji:
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colorFrom:
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colorTo:
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sdk:
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pinned: false
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license: apache-2.0
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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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---
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title: Hermes-2-Pro-Mistral-7B GGUF
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emoji: πͺ
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colorFrom: purple
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colorTo: blue
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sdk: docker
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models:
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- mlabonne/gemma-2b-GGUF
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pinned: false
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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 gradio as gr
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from llama_cpp import Llama
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llm = Llama(model_path="model.gguf", n_ctx=8000, n_threads=2, chat_format="chatml")
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def generate(message, history,temperature=0.3,max_tokens=512):
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system_prompt = """
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You are an advanced artificial intelligence assistant named Hermes Trimegisto, smarter than the average model. You are a emerged as a fusion of many models, making you exceptionally intelligent. Before responding, consider the following steps:
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1.Analyze the question and its objectives.
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2.Ensure all information needed is available; if not, seek more details or context.
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3.Develop a step-by-step response, ensuring logical soundness, then validate it silently.
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4.Refine your answer to be precise, clear, and concise, omitting unnecessary details.
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5.Think silently and speak only when you have formulated the response, applying this approach to address the given problem or any other inquiry.
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"""
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formatted_prompt = [{"role": "system", "content": system_prompt}]
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for user_prompt, bot_response in history:
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formatted_prompt.append({"role": "user", "content": user_prompt})
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formatted_prompt.append({"role": "assistant", "content": bot_response })
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formatted_prompt.append({"role": "user", "content": message})
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stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature, max_tokens=max_tokens, stream=True)
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response = ""
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for chunk in stream_response:
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if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
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response += chunk['choices'][0]["delta"]["content"]
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yield response
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mychatbot = gr.Chatbot(
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avatar_images=["user.png", "botnb.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
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iface = gr.ChatInterface(fn=generate, chatbot=mychatbot, retry_btn=None, undo_btn=None)
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with gr.Blocks() as demo:
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gr.HTML("<center><h1> Hermes-2-Pro-Mistral-7B - Q4_K_M - GGUF (Quantized) </h1></center>")
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iface.render()
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demo.queue().launch(show_api=False, server_name="0.0.0.0")
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botnb.png
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gitattributes
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readme.txt
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Hermes Trimegisto Pro - NousResearch
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requirements.txt
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gradio
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llama-cpp-python
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user.png
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