Add README

README.md

@@ -0,0 +1,47 @@
+---
+license: apache-2.0
+pipeline_tag: text-generation
+---
+# Grok-1 (PyTorch Version)
+
+This repository contains the model and weights of the **torch version** of Grok-1 open-weights model. 
+
+You could find the original weights released by [xAI](https://x.ai/blog) in [Hugging Face](https://huggingface.co/xai-org/grok-1) and the original model in the Grok open release [GitHub Repository](https://github.com/xai-org/grok-1/tree/main).
+
+## Conversion
+
+We translated the original modeling written in JAX into PyTorch version, and converted the weights by mapping tensor files with parameter keys, de-quantizing the tensors with corresponding packed scales, and save to checkpoint file with torch APIs.
+
+The original tokenizer is supposed to be used (i.e. `tokenizer.model` in [GitHub Repository](https://github.com/xai-org/grok-1/tree/main)) with the torch-version model. 
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+
+torch.set_default_dtype(torch.bfloat16)
+model = AutoModelForCausalLM.from_pretrained(
+    "hpcaitech/grok-1",
+    trust_remote_code=True,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+sp = SentencePieceProcessor(model_file="tokenizer.model")
+
+text = "Replace this with your text"
+input_ids = sp.encode(text)
+input_ids = torch.tensor([input_ids]).cuda()
+attention_mask = torch.ones_like(input_ids)
+generate_kwargs = {}  # Add any additional args if you want
+inputs = {
+    "input_ids": input_ids,
+    "attention_mask": attention_mask,
+    **generate_kwargs,
+}
+outputs = model.generate(**inputs)
+```
+
+You could find a complete example code of using the torch-version Grok-1 in ColossalAI [GitHub Repository](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/grok-1). We also applies parallelism techniques from ColossalAI framework (Tensor Parallelism for now) to accelerate the inference.
+
+Note: A multi-GPU machine is required to test the model with the example code (For now, a 8x80G multi-GPU machine is required).