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import gradio as gr
from huggingface_hub import InferenceClient
def client_fn(model):
if "Mixtral" in model:
return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
elif "Llama" in model:
return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
elif "Mistral" in model:
return InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
elif "Phi" in model:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
else:
return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
system_instructions1 = "[SYSTEM] Your task is to Answer the question. Keep conversation very short, clear and concise. The expectation is that you will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things.[QUESTION]"
def models(text, model="Mixtral 8x7B"):
client = client_fn(model)
generate_kwargs = dict(
max_new_tokens=300,
)
formatted_prompt = system_instructions1 + text + "[ANSWER]"
stream = client.text_generation(
formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
if "Phi" in model:
if not response.token.text == "<|assistant|>":
output += response.token.text
else:
if not response.token.text == "</s>":
output += response.token.text
return output
description="""# Chat GO
### Inspired from Google Go"""
demo = gr.Interface(description=description,fn=models, inputs=["text", gr.Dropdown([ 'Mixtral 8x7B','Llama 3 8B','Mistral 7B v0.3','Phi 3 mini', ], value="Mistral 7B v0.3", label="Select Model") ], outputs="text", live=True, batch=True, max_batch_size=1000)
demo.launch()
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