Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,54 @@
+---
+license: apache-2.0
+tags:
+- StepLaw
+- causal-lm
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+model-index:
+- name: step2v2_0618_h2048_ffnh8192_numh16_numl16_lr1.381e-03_bs256_ti3814_mlr1e-5
+  results: []
+---
+
+# Wandb Model Name: step2v2_0618_h2048_ffnh8192_numh16_numl16_lr1.381e-03_bs256_ti3814_mlr1e-5
+
+This model is part of the [StepLaw-N_1.0B-D_1.0B](https://huggingface.co/collections/StepLaw/StepLaw-N_1.0B-D_1.0B) collection.
+
+## Model Specifications
+
+### Architecture
+- **Hidden size (H)**: 2048
+- **Feed-forward network size (FFN)**: 8192
+- **Attention heads**: 16
+- **Layers**: 16
+- **Parameter count**: 1.1BM
+
+### Training Parameters
+- **Learning rate (lr)**: 1.381e-03
+- **Batch size (bs)**: 256
+- **Training iterations**: 3814
+- **Training tokens (D)**: 2.0B
+
+## Model Description
+
+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 1.381e-03 and batch size 256 for 3814 iterations, using a total of 2.0B training tokens.
+
+## Usage Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "StepLaw/StepLaw-N_1.0B-D_1.0B-LR1.381e-03-BS524288"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+# Generate text
+inputs = tokenizer("A long time ago in a galaxy far, far away", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```## Part of StepLaw Project
+
+StepLaw is an initiative to provide thousands of models for optimal hyperparameter research.
+Visit [StepLaw Project](https://step-law.github.io/) for more information.