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  1. pages/🦙_Alpaca.py +0 -201
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- ##### `🦙_Alpaca.py`
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- ##### Alpaca Model
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- ##### https://github.com/seemanne/llamacpypy
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- ##### https://github.com/shaunabanana/llama.py
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- ##### https://github.com/thomasantony/llamacpp-python
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- ##### https://github.com/abetlen/llama-cpp-python
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- ##### Please reach out to [email protected] for any questions
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- #### Loading needed Python libraries
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- import streamlit as st
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- #from llamacpypy import Llama
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- #import llamacpp
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- from llama_cpp import Llama
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- import os
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- import subprocess
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- import sys
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-
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-
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-
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-
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- #### Streamlit initial setup
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- st.set_page_config(
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- page_title = "🦙 Alpaca",
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- page_icon = "images/Logo.png",
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- layout = "centered",
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- initial_sidebar_state = "expanded"
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- )
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-
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-
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-
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-
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- #### Functions of the Python Wrapper
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- def llama_stream(
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- prompt = '',
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- skip_prompt = True,
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- trim_prompt = 0,
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- executable = 'pages/llama.cpp/main',
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- model = 'models/7B/ggml-model-q4_0.bin',
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- threads = 4,
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- temperature = 0.7,
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- top_k = 40,
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- top_p = 0.5,
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- repeat_last_n = 256,
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- repeat_penalty = 1.17647,
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- n = 4096,
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- interactive = False,
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- reverse_prompt = "User:"
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- ):
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- command = [
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- executable,
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- '-m', model,
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- '-t', str(threads),
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- '--temp', str(temperature),
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- '--top_k', str(top_k),
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- '--top_p', str(top_p),
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- '--repeat_last_n', str(repeat_last_n),
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- '--repeat_penalty', str(repeat_penalty),
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- '-n', str(n),
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- '-p', prompt
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- ]
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- if interactive:
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- command += ['-i', '-r', reverse_prompt]
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-
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- process = subprocess.Popen(
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- command,
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- stdin = subprocess.PIPE,
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- stdout = subprocess.PIPE,
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- stderr = subprocess.PIPE,
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- )
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-
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- token = b''
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- generated = ''
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- while True:
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- token += process.stdout.read(1)
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- if token: # neither empty string nor None
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- try:
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- decoded = token.decode('utf-8')
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-
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- trimmed_prompt = prompt
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- if trim_prompt > 0:
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- trimmed_prompt = prompt[:-trim_prompt]
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- prompt_finished = generated.startswith(trimmed_prompt)
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- reverse_prompt_encountered = generated.endswith(reverse_prompt)
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- if not skip_prompt or prompt_finished:
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- yield decoded
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- if interactive and prompt_finished and reverse_prompt_encountered:
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- user_input = input()
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- process.stdin.write(user_input.encode('utf-8') + b'\n')
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- process.stdin.flush()
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-
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- generated += decoded
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- token = b''
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- except UnicodeDecodeError:
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- continue
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- elif process.poll() is not None:
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- return
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-
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-
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- def llama(
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- prompt = '',
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- stream = False,
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- skip_prompt = False,
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- trim_prompt = 0,
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- executable = 'pages/llama.cpp/main',
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- model = 'models/7B/ggml-model-q4_0.bin',
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- threads = 4,
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- temperature = 0.7,
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- top_k = 40,
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- top_p = 0.5,
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- repeat_last_n = 256,
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- repeat_penalty = 1.17647,
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- n = 4096,
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- interactive = False,
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- reverse_prompt = "User:"
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- ):
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- streamer = llama_stream(
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- prompt = prompt,
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- skip_prompt = skip_prompt,
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- trim_prompt = trim_prompt,
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- executable = executable,
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- model = model,
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- threads = threads,
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- temperature = temperature,
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- top_k = top_k,
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- top_p = top_p,
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- repeat_last_n = repeat_last_n,
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- repeat_penalty = repeat_penalty,
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- n = n,
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- interactive = interactive,
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- reverse_prompt = reverse_prompt
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- )
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- if stream:
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- return streamer
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- else:
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- return ''.join(list(streamer))
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-
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-
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-
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- ### Python Wrapper (functions above)
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- #text = []
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- #for token in llama(prompt = 'What is your purpose?', repeat_penalty = 1.05, skip_prompt = False, interactive = False):
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- # print(token, end = '', flush = True)
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- # text.append(token)
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- #st.subheader('Debug')
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- #st.experimental_show(text[0])
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- #st.experimental_show(text[1])
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- #st.subheader('Answer')
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- #st.write(''.join(text))
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-
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-
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-
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- ### llamacpypy
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- #llama = Llama(model_name = 'models/7B/ggml-model-q4_0.bin', warm_start = True)
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- #llama.load_model()
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- #var = llama.generate("This is the weather report, we are reporting a clown fiesta happening at backer street. The clowns ")
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- #st.write(var)
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-
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-
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-
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- ### llamacpp
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- #model_path = "./models/7B/ggml-model-q4_0.bin"
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- #params = llamacpp.gpt_params(model_path, 4096, 40, 0.1, 0.7, 2.0)
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- #model = llamacpp.PyLLAMA(model_path, params)
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- #text = model.predict("Hello, I'm a llama.", 10)
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- #st.write(text)
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- #params = llamacpp.gpt_params('./models/7B/ggml-model-q4_0.bin', # model,
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- # 512, # ctx_size
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- # 100, # n_predict
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- # 40, # top_k
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- # 0.95, # top_p
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- # 0.85, # temp
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- # 1.30, # repeat_penalty
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- # -1, # seed
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- # 8, # threads
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- # 64, # repeat_last_n
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- # 8, # batch_size
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- #)
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- #model = llamacpp.PyLLAMA(params)
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- #model.add_bos() # Adds "beginning of string" token
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- #model.update_input("A llama is a")
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- #model.print_startup_stats()
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- #model.prepare_context()
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-
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- #model.ingest_all_pending_input(True)
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- #while not model.is_finished():
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- # text, is_finished = model.infer_text()
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- # print(text, end="")
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-
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- # if is_finished:
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- # break
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-
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- # Flush stdout
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- #sys.stdout.flush()
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- #model.print_end_stats()
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-
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-
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-
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- ### Llama cpp
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- llm = Llama(model_path = "xfh/alpaca.cpp_65b_ggml")
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- output = llm("Q: Name the planets in the solar system? A: ", max_tokens = 32, stop = ["Q:", "\n"], echo = True)
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- st.write(output)
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-