Semi-working cmdline stable-vicuna interaction
parent
5be80d49c2
commit
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def main():
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print("Hey this is the cli application")
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import click
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from . import run_stable_vicuna
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@click.group()
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def cli():
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pass
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@cli.command()
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@click.option(
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"--model-dir",
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type=click.Path(exists=True, file_okay=False, dir_okay=True),
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envvar="SAVANT_MODEL_DIR",
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default="~/models",
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show_default=True,
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help="The path to a directory containing a hugging face Stable-Vicuna model.",
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)
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def stable_vicuna(model_dir):
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"""Runs a Stable Vicuna CLI prompt."""
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run_stable_vicuna.main(model_dir=model_dir)
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if __name__ == "__main__":
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main()
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cli()
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@ -0,0 +1,74 @@
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import textwrap
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import colorama
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from transformers import LlamaForCausalLM, LlamaTokenizer
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from transformers import logging as t_logging
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from transformers import pipeline
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# Configure logging level for transformers library
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t_logging.logging.set_verbosity_info()
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# Utility Functions
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def get_prompt(human_prompt):
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prompt_template = f"### Human: {human_prompt} \n### Assistant:"
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return prompt_template
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def remove_human_text(text):
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return text.split("### Human:", 1)[0]
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def parse_text(data):
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for item in data:
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text = item["generated_text"]
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assistant_text_index = text.find("### Assistant:")
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if assistant_text_index != -1:
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assistant_text = text[
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assistant_text_index + len("### Assistant:") :
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].strip()
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assistant_text = remove_human_text(assistant_text)
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wrapped_text = textwrap.fill(assistant_text, width=100)
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print(wrapped_text)
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# Reasoning question
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EXAMPLE_REASONING = "Answer the following question by reasoning step by step. \
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The cafeteria had 22 apples. If they used 20 for lunch, and bought 6 more, \
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how many apple do they have?"
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# User interface
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def main(model_dir):
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# Model loading for inference
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tokenizer = LlamaTokenizer.from_pretrained(model_dir)
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base_model = LlamaForCausalLM.from_pretrained(
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model_dir,
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load_in_8bit=True,
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device_map="auto",
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)
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pipe = pipeline(
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"text-generation",
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model=base_model,
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tokenizer=tokenizer,
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max_length=512,
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temperature=0.7,
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top_p=0.95,
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repetition_penalty=1.15,
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)
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print("Reading for inference!")
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while True:
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input_prompt = ""
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input_prompt = input("USER:")
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print(colorama.Style.DIM + f"You are submitting: {input_prompt}")
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print(colorama.Style.RESET_ALL)
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raw_output = pipe(get_prompt(input_prompt))
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parse_text(raw_output)
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if __name__ == "__main__":
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print("Warming up the engines...")
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main()
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# Invoke tab-completion script to be sourced with Bash shell.
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# Known to work on Bash 3.x, untested on 4.x.
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_complete_invoke() {
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local candidates
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# COMP_WORDS contains the entire command string up til now (including
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# program name).
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# We hand it to Invoke so it can figure out the current context: spit back
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# core options, task names, the current task's options, or some combo.
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candidates=`invoke --complete -- ${COMP_WORDS[*]}`
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# `compgen -W` takes list of valid options & a partial word & spits back
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# possible matches. Necessary for any partial word completions (vs
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# completions performed when no partial words are present).
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#
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# $2 is the current word or token being tabbed on, either empty string or a
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# partial word, and thus wants to be compgen'd to arrive at some subset of
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# our candidate list which actually matches.
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#
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# COMPREPLY is the list of valid completions handed back to `complete`.
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COMPREPLY=( $(compgen -W "${candidates}" -- $2) )
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}
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# Tell shell builtin to use the above for completing our invocations.
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# * -F: use given function name to generate completions.
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# * -o default: when function generates no results, use filenames.
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# * positional args: program names to complete for.
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complete -F _complete_invoke -o default invoke inv
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# vim: set ft=sh :
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@ -0,0 +1,9 @@
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transformers @ git+https://github.com/huggingface/transformers@849367ccf741d8c58aa88ccfe1d52d8636eaf2b7
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bitsandbytes
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datasets
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loralib
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sentencepiece
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bitsandbytes
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accelerate
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langchain
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colorama
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@ -1,6 +1,227 @@
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#
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# This file is autogenerated by pip-compile with Python 3.11
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# This file is autogenerated by pip-compile with Python 3.10
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# by the following command:
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#
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# pip-compile --output-file=requirements.txt requirements.in
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# pip-compile requirements.in
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#
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accelerate==0.18.0
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# via -r requirements.in
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aiohttp==3.8.4
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# via
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# datasets
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# fsspec
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# langchain
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aiosignal==1.3.1
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# via aiohttp
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async-timeout==4.0.2
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# via
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# aiohttp
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# langchain
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attrs==23.1.0
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# via aiohttp
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bitsandbytes==0.38.1
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# via -r requirements.in
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certifi==2022.12.7
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# via requests
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charset-normalizer==3.1.0
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# via
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# aiohttp
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# requests
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cmake==3.26.3
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# via triton
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colorama==0.4.6
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# via -r requirements.in
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dataclasses-json==0.5.7
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# via langchain
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datasets==2.12.0
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# via -r requirements.in
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dill==0.3.6
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# via
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# datasets
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# multiprocess
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filelock==3.12.0
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# via
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# huggingface-hub
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# torch
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# transformers
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# triton
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frozenlist==1.3.3
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# via
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# aiohttp
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# aiosignal
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fsspec[http]==2023.4.0
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# via
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# datasets
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# huggingface-hub
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greenlet==2.0.2
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# via sqlalchemy
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huggingface-hub==0.14.1
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# via
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# datasets
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# transformers
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idna==3.4
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# via
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# requests
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# yarl
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jinja2==3.1.2
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# via torch
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langchain==0.0.160
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# via -r requirements.in
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lit==16.0.3
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# via triton
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loralib==0.1.1
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# via -r requirements.in
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markupsafe==2.1.2
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# via jinja2
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marshmallow==3.19.0
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# via
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# dataclasses-json
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# marshmallow-enum
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marshmallow-enum==1.5.1
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# via dataclasses-json
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mpmath==1.3.0
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# via sympy
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multidict==6.0.4
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# via
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# aiohttp
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# yarl
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multiprocess==0.70.14
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# via datasets
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mypy-extensions==1.0.0
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# via typing-inspect
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networkx==3.1
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# via torch
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numexpr==2.8.4
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# via langchain
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numpy==1.24.3
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# via
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# accelerate
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# datasets
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# langchain
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# numexpr
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# pandas
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# pyarrow
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# transformers
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nvidia-cublas-cu11==11.10.3.66
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# via
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# nvidia-cudnn-cu11
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# nvidia-cusolver-cu11
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# torch
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nvidia-cuda-cupti-cu11==11.7.101
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# via torch
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nvidia-cuda-nvrtc-cu11==11.7.99
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# via torch
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nvidia-cuda-runtime-cu11==11.7.99
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# via torch
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nvidia-cudnn-cu11==8.5.0.96
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# via torch
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nvidia-cufft-cu11==10.9.0.58
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# via torch
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nvidia-curand-cu11==10.2.10.91
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# via torch
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nvidia-cusolver-cu11==11.4.0.1
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# via torch
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nvidia-cusparse-cu11==11.7.4.91
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# via torch
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nvidia-nccl-cu11==2.14.3
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# via torch
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nvidia-nvtx-cu11==11.7.91
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# via torch
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openapi-schema-pydantic==1.2.4
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# via langchain
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packaging==23.1
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# via
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# accelerate
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# datasets
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# huggingface-hub
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# marshmallow
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# transformers
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pandas==2.0.1
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# via datasets
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psutil==5.9.5
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# via accelerate
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pyarrow==12.0.0
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# via datasets
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pydantic==1.10.7
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# via
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# langchain
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# openapi-schema-pydantic
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python-dateutil==2.8.2
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# via pandas
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pytz==2023.3
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# via pandas
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pyyaml==6.0
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# via
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# accelerate
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# datasets
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# huggingface-hub
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# langchain
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# transformers
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regex==2023.5.5
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# via transformers
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requests==2.30.0
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# via
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# datasets
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# fsspec
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# huggingface-hub
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# langchain
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# responses
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# transformers
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responses==0.18.0
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# via datasets
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sentencepiece==0.1.99
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# via -r requirements.in
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six==1.16.0
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# via python-dateutil
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sqlalchemy==2.0.12
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# via langchain
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sympy==1.11.1
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# via torch
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tenacity==8.2.2
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# via langchain
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tokenizers==0.13.3
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# via transformers
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torch==2.0.0
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# via
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# accelerate
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# triton
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tqdm==4.65.0
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# via
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# datasets
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# huggingface-hub
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# langchain
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# transformers
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transformers @ git+https://github.com/huggingface/transformers@849367ccf741d8c58aa88ccfe1d52d8636eaf2b7
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# via -r requirements.in
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triton==2.0.0
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# via torch
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typing-extensions==4.5.0
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# via
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# huggingface-hub
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# pydantic
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# sqlalchemy
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# torch
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# typing-inspect
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typing-inspect==0.8.0
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# via dataclasses-json
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tzdata==2023.3
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# via pandas
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urllib3==2.0.2
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# via
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# requests
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# responses
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wheel==0.40.0
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# via
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# nvidia-cublas-cu11
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# nvidia-cuda-cupti-cu11
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# nvidia-cuda-runtime-cu11
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# nvidia-curand-cu11
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# nvidia-cusparse-cu11
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# nvidia-nvtx-cu11
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xxhash==3.2.0
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# via datasets
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yarl==1.9.2
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# via aiohttp
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# The following packages are considered to be unsafe in a requirements file:
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# setuptools
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