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Small dummy LLama2-type Model useable for Unit/Integration tests. Suitable for CPU only machines, see [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio/blob/main/tests/integration/test_integration.py) for an example integration test. |
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Model was created as follows: |
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```python |
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from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM |
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repo_name = "MaxJeblick/llama2-0b-unit-test" |
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model_name = "h2oai/h2ogpt-4096-llama2-7b-chat" |
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config = AutoConfig.from_pretrained(model_name) |
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config.hidden_size = 12 |
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config.max_position_embeddings = 32 |
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config.intermediate_size = 24 |
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config.num_attention_heads = 2 |
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config.num_hidden_layers = 2 |
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config.num_key_value_heads = 2 |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_config(config) |
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print(model.num_parameters()) # 770_940 |
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model.push_to_hub(repo_name, private=False) |
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tokenizer.push_to_hub(repo_name, private=False) |
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config.push_to_hub(repo_name, private=False) |
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``` |
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Use the following configuration in [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to run a complete experiment in **5 seconds** using the default dataset and default settings otherwise: |
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```yaml |
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Validation Size: 0.1 |
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Data Sample: 0.1 |
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Max Length Prompt: 32 |
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Max Length Answer: 32 |
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Max Length: 64 |
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Backbone Dtype: float16 |
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Gradient Checkpointing: False |
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Batch Size: 8 |
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Max Length Inference: 16 |
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``` |
