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https://github.com/nomic-ai/gpt4all.git
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make scripts executable (#1555)
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Subproject commit a8ed8c858985ef94d97a3cf2c97085b680c6d5d0
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Subproject commit 2dee60214b0001cf03e1cec0a53a61a17b55c1eb
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gpt4all-bindings/cli/app.py
Normal file → Executable file
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gpt4all-bindings/cli/app.py
Normal file → Executable file
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#!/usr/bin/env python3
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"""GPT4All CLI
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"""GPT4All CLI
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The GPT4All CLI is a self-contained script based on the `gpt4all` and `typer` packages. It offers a
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The GPT4All CLI is a self-contained script based on the `gpt4all` and `typer` packages. It offers a
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#!/bin/sh
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mkdir -p runtimes
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mkdir -p runtimes
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rm -rf runtimes/linux-x64
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rm -rf runtimes/linux-x64
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mkdir -p runtimes/linux-x64/native
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mkdir -p runtimes/linux-x64/native
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gpt4all-bindings/python/gpt4all/tests/test_embed_timings.py
Normal file → Executable file
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gpt4all-bindings/python/gpt4all/tests/test_embed_timings.py
Normal file → Executable file
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#!/usr/bin/env python3
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import sys
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import sys
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import time
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import time
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from io import StringIO
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from io import StringIO
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gpt4all-bindings/typescript/scripts/build_unix.sh
Normal file → Executable file
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gpt4all-bindings/typescript/scripts/build_unix.sh
Normal file → Executable file
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gpt4all-chat/cmake/sign_dmg.py
Normal file → Executable file
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gpt4all-chat/cmake/sign_dmg.py
Normal file → Executable file
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#!/usr/bin/env python3
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import os
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import os
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import subprocess
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import subprocess
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import tempfile
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import tempfile
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gpt4all-training/build_map.py
Normal file → Executable file
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gpt4all-training/build_map.py
Normal file → Executable file
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#!/usr/bin/env python3
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import numpy as np
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import numpy as np
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from nomic import atlas
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from nomic import atlas
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import glob
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import glob
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@ -51,4 +52,4 @@ atlas.map_embeddings(embeddings,
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colorable_fields=["source", "loss", "trained_on"],
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colorable_fields=["source", "loss", "trained_on"],
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build_topic_model=True,
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build_topic_model=True,
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topic_label_field="inputs",
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topic_label_field="inputs",
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reset_project_if_exists=True,)
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reset_project_if_exists=True,)
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gpt4all-training/clean.py
Normal file → Executable file
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gpt4all-training/clean.py
Normal file → Executable file
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#!/usr/bin/env python3
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import numpy as np
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import numpy as np
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import glob
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import glob
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import os
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import os
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@ -71,4 +72,4 @@ for file in glob.glob(os.path.join(prompt_generation_dir, "*.jsonl")):
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clean_name = file.split(".jsonl")[0] + "_clean.jsonl"
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clean_name = file.split(".jsonl")[0] + "_clean.jsonl"
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print(f"writing to {curr_len} rows to {clean_name}")
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print(f"writing to {curr_len} rows to {clean_name}")
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df.to_json(clean_name, orient="records", lines=True)
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df.to_json(clean_name, orient="records", lines=True)
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gpt4all-training/create_hostname.sh
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gpt4all-training/create_hostname.sh
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gpt4all-training/eval_figures.py
Normal file → Executable file
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gpt4all-training/eval_figures.py
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#!/usr/bin/env python3
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import glob
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import glob
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import pickle
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import pickle
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import numpy as np
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import numpy as np
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gpt4all-training/eval_self_instruct.py
Normal file → Executable file
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gpt4all-training/eval_self_instruct.py
Normal file → Executable file
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#!/usr/bin/env python3
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import json
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import json
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import torch
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import torch
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import pickle
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import pickle
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gpt4all-training/generate.py
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gpt4all-training/generate.py
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#!/usr/bin/env python3
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModelForCausalLM
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from peft import PeftModelForCausalLM
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from read import read_config
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from read import read_config
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gpt4all-training/inference.py
Normal file → Executable file
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gpt4all-training/inference.py
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#!/usr/bin/env python3
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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gpt4all-training/launcher.sh
Normal file → Executable file
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gpt4all-training/launcher.sh
Normal file → Executable file
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gpt4all-training/train.py
Normal file → Executable file
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gpt4all-training/train.py
Normal file → Executable file
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#!/usr/bin/env python3
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import os
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import os
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from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler
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from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler
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import torch
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import torch
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