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--- |
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license: apache-2.0 |
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tags: |
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- StepLaw |
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- causal-lm |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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model-index: |
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- name: step2v2_0618_h1024_ffnh9552_numh16_numl8_lr9.766e-04_bs768_ti63578_mlr1e-5 |
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results: [] |
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--- |
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# Wandb Model Name: step2v2_0618_h1024_ffnh9552_numh16_numl8_lr9.766e-04_bs768_ti63578_mlr1e-5 |
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This model is part of the [StepLaw-N_268M-D_99.0B](https://huggingface.co/collections/StepLaw/StepLaw-N_268M-D_99.0B) collection. |
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## Model Specifications |
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### Architecture |
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- **Hidden size (H)**: 1024 |
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- **Feed-forward network size (FFN)**: 9552 |
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- **Attention heads**: 16 |
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- **Layers**: 8 |
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- **Parameter count**: 268M |
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### Training Parameters |
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- **Learning rate (lr)**: 9.766e-04 |
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- **Batch size (bs)**: 1572864 |
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- **Training iterations**: 63578 |
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- **Training tokens (D)**: 100.0B |
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## Model Description |
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StepLaw models are trained with various hyperparameter settings to enable research on scaling laws and hyperparameter optimization. This specific model was trained with learning rate 9.766e-04 and batch size 1572864 for 63578 iterations, using a total of 100.0B training tokens. |
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## Usage Example |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "StepLaw/StepLaw-N_268M-D_99.0B-LR9.766e-04-BS1572864" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) |
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# Generate text |
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inputs = tokenizer("A long time ago in a galaxy far, far away", return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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