Upload app.py.H2O_GGUF

app.py.H2O_GGUF

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+#app.py.chatbot
+#app.py Modif04
+#https://www.freddyboulton.com/blog/llama-cpp-python
+import gradio as gr
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="/home/user/app/h2o-danube3-500m-chat-Q4_K_M.gguf",
+    verbose=True
+)
+
+def predict(message, history):
+#    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+#    messages = [{"role": "assistant", "content": "You are a helpful assistant."}]
+#    messages = [{"role": "assistant", "content": "Bonjour, comment puis-je vous aider?"}]
+    messages = []
+    for user_message, bot_message in history:
+        if user_message:
+            messages.append({"role": "user", "content": user_message})
+        if bot_message:
+            messages.append({"role": "assistant", "content": bot_message})
+    messages.append({"role": "user", "content": message})
+    
+    response = ""
+    for chunk in llm.create_chat_completion(
+        stream=True,
+        messages=messages,
+    ):
+        part = chunk["choices"][0]["delta"].get("content", None)
+        if part:
+            response += part
+        yield response
+
+demo = gr.ChatInterface(predict)
+
+demo.launch()
+
+
+
+##app.py Modif03
+#import gradio as gr
+#from huggingface_hub import create_inference_endpoint, InferenceClient
+#from transformers import AutoModelForCausalLM, AutoTokenizer
+#
+##model_name = "MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf"
+##model = AutoModelForCausalLM.from_pretrained(model_name)
+##tokenizer = AutoTokenizer.from_pretrained(model_name)
+#
+##client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+##client = InferenceClient("MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf")
+##client = InferenceClient("/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf")
+#
+## Créez une instance Inference locale
+#endpoint = create_inference_endpoint(
+#    "Local-Endpoint-MisterAI-H2O",
+#    repository="MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf",
+##    model_path="/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf",
+#    framework="pytorch",
+#    task="text-generation",
+#    accelerator="cpu",
+#    vendor="local",
+#    region="local",
+#    type="unprotected",
+#    instance_size="small",
+#    instance_type="local",
+#    URL="http://0.0.0.0:6789"
+#)
+#
+#print(f"Endpoint créé à l'URL : {endpoint.url}")
+#
+#client = endpoint.client
+#
+#
+#
+#def respond(
+#    message,
+#    history: list[tuple[str, str]],
+#    system_message,
+#    max_tokens,
+#    temperature,
+#    top_p,
+#):
+#    messages = [{"role": "system", "content": system_message}]
+#
+#    for val in history:
+#        if val[0]:
+#            messages.append({"role": "user", "content": val[0]})
+#        if val[1]:
+#            messages.append({"role": "assistant", "content": val[1]})
+#
+#    messages.append({"role": "user", "content": message})
+#
+#    response = ""
+#
+#    for message in client.chat_completion(
+#        messages,
+#        max_tokens=max_tokens,
+#        stream=True,
+#        temperature=temperature,
+#        top_p=top_p,
+#    ):
+#        token = message.choices[0].delta.content
+#
+#        response += token
+#        yield response
+#
+#demo = gr.ChatInterface(
+#    respond,
+#    additional_inputs=[
+#        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#        gr.Slider(
+#            minimum=0.1,
+#            maximum=1.0,
+#            value=0.95,
+#            step=0.05,
+#            label="Top-p (nucleus sampling)",
+#        ),
+#    ],
+#)
+#
+#
+#if __name__ == "__main__":
+#    demo.launch()
+#
+#
+#
+#
+##app.py Modif01
+#import gradio as gr
+#from huggingface_hub import Inference, InferenceClient
+#from transformers import AutoModelForCausalLM, AutoTokenizer
+#
+##model_name = "MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf"
+##model = AutoModelForCausalLM.from_pretrained(model_name)
+##tokenizer = AutoTokenizer.from_pretrained(model_name)
+#
+##client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+##client = InferenceClient("MisterAI/H20GPT_h2o-danube3-500m-chat-Q4_K_M_gguf")
+##client = InferenceClient("/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf")
+#
+## Créez une instance Inference locale
+#inference = Inference(
+#    model_path="/home/user/app/H20GPT_h2o-danube3-500m-chat-Q4_K_M.gguf",
+#    device="cpu",  # Utilisez le CPU pour l'inference
+#    token=None,  # Pas de token nécessaire pour cette instance
+#)
+#
+#client = inference
+#
+#
+#
+#def respond(
+#    message,
+#    history: list[tuple[str, str]],
+#    system_message,
+#    max_tokens,
+#    temperature,
+#    top_p,
+#):
+#    messages = [{"role": "system", "content": system_message}]
+#
+#    for val in history:
+#        if val[0]:
+#            messages.append({"role": "user", "content": val[0]})
+#        if val[1]:
+#            messages.append({"role": "assistant", "content": val[1]})
+#
+#    messages.append({"role": "user", "content": message})
+#
+#    response = ""
+#
+#    for message in client.chat_completion(
+#        messages,
+#        max_tokens=max_tokens,
+#        stream=True,
+#        temperature=temperature,
+#        top_p=top_p,
+#    ):
+#        token = message.choices[0].delta.content
+#
+#        response += token
+#        yield response
+#
+#demo = gr.ChatInterface(
+#    respond,
+#    additional_inputs=[
+#        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#        gr.Slider(
+#            minimum=0.1,
+#            maximum=1.0,
+#            value=0.95,
+#            step=0.05,
+#            label="Top-p (nucleus sampling)",
+#        ),
+#    ],
+#)
+#
+#
+#if __name__ == "__main__":
+#    demo.launch()
+#
+#
+#
+#
+#
+##app.py ORIGINAL
+#import gradio as gr
+#from huggingface_hub import InferenceClient
+#
+#"""
+#For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+#"""
+#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+#
+#
+#def respond(
+#    message,
+#    history: list[tuple[str, str]],
+#    system_message,
+#    max_tokens,
+#    temperature,
+#    top_p,
+#):
+#    messages = [{"role": "system", "content": system_message}]
+#
+#    for val in history:
+#        if val[0]:
+#            messages.append({"role": "user", "content": val[0]})
+#        if val[1]:
+#            messages.append({"role": "assistant", "content": val[1]})
+#
+#    messages.append({"role": "user", "content": message})
+#
+#    response = ""
+#
+#    for message in client.chat_completion(
+#        messages,
+#        max_tokens=max_tokens,
+#        stream=True,
+#        temperature=temperature,
+#        top_p=top_p,
+#    ):
+#        token = message.choices[0].delta.content
+#
+#        response += token
+#        yield response
+#
+#"""
+#For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+#"""
+#demo = gr.ChatInterface(
+#    respond,
+#    additional_inputs=[
+#        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#        gr.Slider(
+#            minimum=0.1,
+#            maximum=1.0,
+#            value=0.95,
+#            step=0.05,
+#            label="Top-p (nucleus sampling)",
+#        ),
+#    ],
+#)
+#
+#
+#if __name__ == "__main__":
+#    demo.launch()
+