mirror of
https://github.com/ggml-org/llama.cpp.git
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2af23d3043
* feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import os
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import sys
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from tqdm import tqdm
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import requests
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if len(sys.argv) < 3:
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print("Usage: download-pth.py dir-model model-type\n")
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print(" model-type: Available models 7B, 13B, 30B or 65B")
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sys.exit(1)
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modelsDir = sys.argv[1]
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model = sys.argv[2]
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num = {
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"7B": 1,
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"13B": 2,
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"30B": 4,
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"65B": 8,
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}
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if model not in num:
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print(f"Error: model {model} is not valid, provide 7B, 13B, 30B or 65B")
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sys.exit(1)
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print(f"Downloading model {model}")
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files = ["checklist.chk", "params.json"]
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for i in range(num[model]):
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files.append(f"consolidated.0{i}.pth")
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resolved_path = os.path.abspath(os.path.join(modelsDir, model))
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os.makedirs(resolved_path, exist_ok=True)
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for file in files:
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dest_path = os.path.join(resolved_path, file)
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if os.path.exists(dest_path):
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print(f"Skip file download, it already exists: {file}")
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continue
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url = f"https://agi.gpt4.org/llama/LLaMA/{model}/{file}"
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response = requests.get(url, stream=True)
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with open(dest_path, 'wb') as f:
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with tqdm(unit='B', unit_scale=True, miniters=1, desc=file) as t:
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for chunk in response.iter_content(chunk_size=1024):
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if chunk:
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f.write(chunk)
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t.update(len(chunk))
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files2 = ["tokenizer_checklist.chk", "tokenizer.model"]
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for file in files2:
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dest_path = os.path.join(modelsDir, file)
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if os.path.exists(dest_path):
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print(f"Skip file download, it already exists: {file}")
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continue
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url = f"https://agi.gpt4.org/llama/LLaMA/{file}"
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response = requests.get(url, stream=True)
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with open(dest_path, 'wb') as f:
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with tqdm(unit='B', unit_scale=True, miniters=1, desc=file) as t:
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for chunk in response.iter_content(chunk_size=1024):
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if chunk:
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f.write(chunk)
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t.update(len(chunk)) |