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gorkemgoknar
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Update app.py
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app.py
CHANGED
@@ -146,8 +146,8 @@ zephyr_model_path="./zephyr-7b-beta.Q5_K_M.gguf"
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#print("Downloading Yi-6B")
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#Yi-6B
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hf_hub_download(repo_id="TheBloke/Yi-6B-GGUF", local_dir=".", filename="yi-6b.Q5_K_M.gguf")
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yi_model_path="./yi-6b.Q5_K_M.gguf"
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from llama_cpp import Llama
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@@ -167,8 +167,8 @@ llm_mistral = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")
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print("Running LLM Zephyr")
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llm_zephyr = Llama(model_path=zephyr_model_path,n_gpu_layers=round(GPU_LAYERS/2),max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE)
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print("Running Yi LLM")
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llm_yi = Llama(model_path=yi_model_path,n_gpu_layers=round(GPU_LAYERS/2),max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE)
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# Mistral formatter
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@@ -744,7 +744,7 @@ EXAMPLES = [
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]
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MODELS = ["Mistral 7B Instruct","Zephyr 7B Beta"
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OTHER_HTML=f"""<div>
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<a style="display:inline-block" href='https://github.com/coqui-ai/TTS'><img src='https://img.shields.io/github/stars/coqui-ai/TTS?style=social' /></a>
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@@ -851,7 +851,6 @@ It relies on following models :
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Speech to Text : [Whisper-large-v2](https://sanchit-gandhi-whisper-large-v2.hf.space/) as an ASR model, to transcribe recorded audio to text. It is called through a [gradio client](https://www.gradio.app/docs/client).
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LLM Mistral : [Mistral-7b-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as the chat model.
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LLM Zephyr : [Zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) as the chat model. GGUF Q5_K_M quantized version used locally via llama_cpp from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF).
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LLM Yi : [Yi-6B](https://huggingface.co/01-ai/Yi-6B) as the chat model. GGUF Q5_K_M quantized version used locally via llama_cpp from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Yi-6B-GGUF).
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Text to Speech : [Coqui's XTTS V2](https://huggingface.co/spaces/coqui/xtts) as a Multilingual TTS model, to generate the chatbot answers. This time, the model is hosted locally.
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Note:
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#print("Downloading Yi-6B")
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#Yi-6B
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#hf_hub_download(repo_id="TheBloke/Yi-6B-GGUF", local_dir=".", filename="yi-6b.Q5_K_M.gguf")
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#yi_model_path="./yi-6b.Q5_K_M.gguf"
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from llama_cpp import Llama
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print("Running LLM Zephyr")
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llm_zephyr = Llama(model_path=zephyr_model_path,n_gpu_layers=round(GPU_LAYERS/2),max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE)
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#print("Running Yi LLM")
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#llm_yi = Llama(model_path=yi_model_path,n_gpu_layers=round(GPU_LAYERS/2),max_new_tokens=256, context_window=4096, n_ctx=4096,n_batch=128,verbose=LLAMA_VERBOSE)
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# Mistral formatter
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]
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MODELS = ["Mistral 7B Instruct","Zephyr 7B Beta"]
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OTHER_HTML=f"""<div>
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<a style="display:inline-block" href='https://github.com/coqui-ai/TTS'><img src='https://img.shields.io/github/stars/coqui-ai/TTS?style=social' /></a>
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Speech to Text : [Whisper-large-v2](https://sanchit-gandhi-whisper-large-v2.hf.space/) as an ASR model, to transcribe recorded audio to text. It is called through a [gradio client](https://www.gradio.app/docs/client).
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LLM Mistral : [Mistral-7b-instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) as the chat model.
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LLM Zephyr : [Zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) as the chat model. GGUF Q5_K_M quantized version used locally via llama_cpp from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF).
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Text to Speech : [Coqui's XTTS V2](https://huggingface.co/spaces/coqui/xtts) as a Multilingual TTS model, to generate the chatbot answers. This time, the model is hosted locally.
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Note:
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