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import gradio as gr | |
from huggingface_hub import hf_hub_download | |
from llama_cpp import Llama | |
import psutil | |
## Download the GGUF model | |
model_name = "jackangel/LLama_3_Instruct_SPFx_Docs_Unsloth" | |
model_file = "Llama_3_Instruct_SPFx_Docs_Unsloth.Q4_K_M.gguf" # this is the specific model file we'll use in this example. It's a 4-bit quant, but other levels of quantization are available in the model repo if preferred | |
model_path = hf_hub_download(model_name, filename=model_file) | |
_ = psutil.cpu_count(logical=False) - 1 | |
cpu_count: int = int(_) if _ else 1 | |
## Instantiate model from downloaded file | |
llm = Llama( | |
model_path=model_path, | |
n_ctx=1024, # Context length to use | |
n_threads=cpu_count, # Number of CPU threads to use | |
n_gpu_layers=0 # Number of model layers to offload to GPU | |
) | |
## Generation kwargs | |
generation_kwargs = { | |
"max_tokens":1000, | |
"stop":["</s>"], | |
"temperature":0.2, | |
"echo":False, # Echo the prompt in the output | |
"top_k":20, | |
"top_p":0.7 | |
} | |
def chatbot(message, history): | |
prompt = "INSTRUCTION: You are a helpful assistant\nINPUT: " + message + "\nOUTPUT:" | |
airemember = "" | |
for human,assistant in history: | |
airemember += "USER: " + human + "\nASSISTANT:" + assistant+"\n\n" | |
sendtoai = airemember + prompt | |
result = llm(sendtoai, **generation_kwargs) | |
text=result["choices"][0]["text"].strip() | |
return text | |
app = gr.ChatInterface(chatbot) | |
app.launch() |