mirror of
https://github.com/ggerganov/llama.cpp.git
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a0ec17b32e
Converter script can now read these two fields as a detailed base model and dataset source.
This was done so that it will be easier for Hugging Face to integrate detailed metadata as needed.
- base_model_sources (List[dict], optional)
- dataset_sources (List[dict], optional)
Dataset now represented as:
- general.dataset.count
- general.dataset.{id}.name
- general.dataset.{id}.author
- general.dataset.{id}.version
- general.dataset.{id}.organization
- general.dataset.{id}.description
- general.dataset.{id}.url
- general.dataset.{id}.doi
- general.dataset.{id}.uuid
- general.dataset.{id}.repo_url
This also adds to base model these metadata:
- general.base_model.{id}.description
239 lines
15 KiB
Python
Executable File
239 lines
15 KiB
Python
Executable File
#!/usr/bin/env python3
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import unittest
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from pathlib import Path
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import os
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import sys
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# Necessary to load the local gguf package
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if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import gguf
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class TestMetadataMethod(unittest.TestCase):
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def test_id_to_title(self):
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self.assertEqual(gguf.Metadata.id_to_title("Mixtral-8x7B-Instruct-v0.1"), "Mixtral 8x7B Instruct v0.1")
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self.assertEqual(gguf.Metadata.id_to_title("Meta-Llama-3-8B"), "Meta Llama 3 8B")
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self.assertEqual(gguf.Metadata.id_to_title("hermes-2-pro-llama-3-8b-DPO"), "Hermes 2 Pro Llama 3 8b DPO")
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def test_get_model_id_components(self):
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# This is the basic standard form with organization marker
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self.assertEqual(gguf.Metadata.get_model_id_components("Mistral/Mixtral-8x7B-Instruct-v0.1"),
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('Mixtral-8x7B-Instruct-v0.1', "Mistral", 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
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# Similar to basic standard form but without organization marker
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct-v0.1"),
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('Mixtral-8x7B-Instruct-v0.1', None, 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
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# Missing version
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct"),
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('Mixtral-8x7B-Instruct', None, 'Mixtral', 'Instruct', None, '8x7B'))
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# Missing finetune
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-v0.1"),
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('Mixtral-8x7B-v0.1', None, 'Mixtral', None, 'v0.1', '8x7B'))
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# Base name and size label only
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B"),
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('Mixtral-8x7B', None, 'Mixtral', None, None, '8x7B'))
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# Base name and version only
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-v0.1"),
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('Mixtral-v0.1', None, 'Mixtral', None, 'v0.1', None))
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## Edge Cases ##
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# This is too ambiguous... best to err on caution and output nothing
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self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral"),
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('Mixtral', None, None, None, None, None))
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# Basename has numbers mixed in and also size label provided. Must avoid capturing number in basename
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self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"),
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('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B'))
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# Non standard naming
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self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"),
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('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B'))
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# Capture 'sub size labels' e.g. A14B in '57B-A14B' usually refers to activated params/weight count
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self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-57B-A14B-Instruct"),
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('Qwen2-57B-A14B-Instruct', None, 'Qwen2', 'Instruct', None, '57B-A14B'))
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# Check that it can handle a real model id with no version code
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# Note that 4k in this string is non standard and microsoft were referring to context length rather than weight count
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self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Phi-3-mini-4k-instruct", 4 * 10**9),
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('Phi-3-mini-4k-instruct', 'microsoft', 'Phi-3', '4k-instruct', None, 'mini'))
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# There is some legitimate models with only thousands of parameters
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self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3),
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('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K'))
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# Non standard and not easy to disambiguate
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self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"),
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('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None))
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# This is a real model_id where they append 2DPO to refer to Direct Preference Optimization
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self.assertEqual(gguf.Metadata.get_model_id_components("crestf411/daybreak-kunoichi-2dpo-7b"),
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('daybreak-kunoichi-2dpo-7b', 'crestf411', 'daybreak-kunoichi', '2dpo', None, '7B'))
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# This is a real model id where the weight size has a decimal point
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self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-0.5B-Instruct"),
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('Qwen2-0.5B-Instruct', None, 'Qwen2', 'Instruct', None, '0.5B'))
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# Uses an underscore in the size label
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self.assertEqual(gguf.Metadata.get_model_id_components("smallcloudai/Refact-1_6B-fim"),
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('Refact-1_6B-fim', 'smallcloudai', 'Refact', 'fim', None, '1.6B'))
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# Uses Iter3 for the version
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self.assertEqual(gguf.Metadata.get_model_id_components("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3"),
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('Gemma-2-9B-It-SPPO-Iter3', 'UCLA-AGI', 'Gemma-2', 'It-SPPO', 'Iter3', '9B'))
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# Has two potential versions in the basename
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self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Hermes-2-Theta-Llama-3-8B"),
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('Hermes-2-Theta-Llama-3-8B', 'NousResearch', 'Hermes-2-Theta-Llama-3', None, None, '8B'))
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# Potential version in the basename
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self.assertEqual(gguf.Metadata.get_model_id_components("SeaLLMs/SeaLLMs-v3-7B-Chat"),
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('SeaLLMs-v3-7B-Chat', 'SeaLLMs', 'SeaLLMs-v3', 'Chat', None, '7B'))
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# Underscore in the basename, and 1m for the context size
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self.assertEqual(gguf.Metadata.get_model_id_components("internlm/internlm2_5-7b-chat-1m", 7 * 10**9),
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('internlm2_5-7b-chat-1m', 'internlm', 'internlm2_5', 'chat-1m', None, '7B'))
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# Version before the finetune name
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self.assertEqual(gguf.Metadata.get_model_id_components("pszemraj/jamba-900M-v0.13-KIx2"),
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('jamba-900M-v0.13-KIx2', 'pszemraj', 'jamba', 'KIx2', 'v0.13', '900M'))
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# TODO: hf suffix which could be ignored but isn't
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self.assertEqual(gguf.Metadata.get_model_id_components("state-spaces/mamba-2.8b-hf"),
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('mamba-2.8b-hf', 'state-spaces', 'mamba', 'hf', None, '2.8B'))
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# Two sizes, don't merge them, the other is the number of tokens on which it was trained
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self.assertEqual(gguf.Metadata.get_model_id_components("abacaj/llama-161M-100B", 161 * 10**6),
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('llama-161M-100B', 'abacaj', 'llama', '100b', None, '161M'))
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# It's a trap, there is no size label
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self.assertEqual(gguf.Metadata.get_model_id_components("SparseLLM/relu-100B", 1340 * 10**6),
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('relu-100B', 'SparseLLM', 'relu', '100b', None, None))
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# Weird size notation
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self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"),
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('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B'))
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# Ignore full-text size labels when there are number-based ones, and deduplicate size labels
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self.assertEqual(gguf.Metadata.get_model_id_components("MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1"),
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('GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1', 'MaziyarPanahi', 'GreenNode-mini', 'multilingual-v1olet-Mistral-Instruct', 'v0.1', '7B'))
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# Instruct in a name without a size label
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self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/Mistral-Nemo-Instruct-2407"),
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('Mistral-Nemo-Instruct-2407', 'mistralai', 'Mistral-Nemo', 'Instruct', '2407', None))
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# Non-obvious splitting relying on 'chat' keyword
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self.assertEqual(gguf.Metadata.get_model_id_components("deepseek-ai/DeepSeek-V2-Chat-0628"),
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('DeepSeek-V2-Chat-0628', 'deepseek-ai', 'DeepSeek-V2', 'Chat', '0628', None))
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# Multiple versions
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self.assertEqual(gguf.Metadata.get_model_id_components("OpenGVLab/Mini-InternVL-Chat-2B-V1-5"),
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('Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab', 'Mini-InternVL', 'Chat', 'V1-5', '2B'))
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# TODO: DPO in the name
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self.assertEqual(gguf.Metadata.get_model_id_components("jondurbin/bagel-dpo-2.8b-v0.2"),
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('bagel-dpo-2.8b-v0.2', 'jondurbin', 'bagel-dpo', None, 'v0.2', '2.8B'))
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# DPO in name, but can't be used for the finetune to keep 'LLaMA-3' in the basename
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self.assertEqual(gguf.Metadata.get_model_id_components("voxmenthe/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized"),
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('SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized', 'voxmenthe', 'SFR-Iterative-DPO-LLaMA-3', 'R-unquantized', None, '8B'))
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# Too ambiguous
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# TODO: should "base" be a 'finetune' or 'size_label'?
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# (in this case it should be a size label, but other models use it to signal that they are not finetuned)
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self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Florence-2-base"),
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('Florence-2-base', 'microsoft', None, None, None, None))
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## Invalid cases ##
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# Start with a dash and has dashes in rows
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self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"),
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('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None))
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## LoRA ##
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self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"),
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('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B'))
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# Negative size --> output is a LoRA adaper --> prune "LoRA" out of the name to avoid redundancy with the suffix
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self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234),
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('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B'))
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def test_apply_metadata_heuristic_from_model_card(self):
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model_card = {
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'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],
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'model-index': [{'name': 'Mixtral-8x7B-Instruct-v0.1', 'results': []}],
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'language': ['en'],
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'datasets': ['teknium/OpenHermes-2.5'],
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'widget': [{'example_title': 'Hermes 2 Pro', 'messages': [{'role': 'system', 'content': 'You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.'}, {'role': 'user', 'content': 'Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.'}]}],
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'base_model': ["EmbeddedLLM/Mistral-7B-Merge-14-v0", "janai-hq/trinity-v1"]
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}
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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expect = gguf.Metadata()
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expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
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expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl']
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expect.languages=['en']
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expect.datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]
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self.assertEqual(got, expect)
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# Base Model spec is inferred from model id
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model_card = {'base_models': 'teknium/OpenHermes-2.5'}
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expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Base Model spec is only url
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model_card = {'base_models': ['https://huggingface.co/teknium/OpenHermes-2.5']}
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expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Base Model spec is given directly
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model_card = {'base_models': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]}
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expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Dataset spec is inferred from model id
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model_card = {'datasets': 'teknium/OpenHermes-2.5'}
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expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Dataset spec is only url
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model_card = {'datasets': ['https://huggingface.co/teknium/OpenHermes-2.5']}
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expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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# Dataset spec is given directly
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model_card = {'datasets': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]}
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expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}])
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
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self.assertEqual(got, expect)
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def test_apply_metadata_heuristic_from_hf_parameters(self):
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hf_params = {"_name_or_path": "./hermes-2-pro-llama-3-8b-DPO"}
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=hf_params, model_path=None)
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expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
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self.assertEqual(got, expect)
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def test_apply_metadata_heuristic_from_model_dir(self):
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model_dir_path = Path("./hermes-2-pro-llama-3-8b-DPO")
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got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=None, model_path=model_dir_path)
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expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
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self.assertEqual(got, expect)
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if __name__ == "__main__":
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unittest.main()
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