Update instruction

.gitignore

@@ -1,2 +0,0 @@
-checkpoints/*
-!checkpoints/README.md

README.md

@@ -2,7 +2,7 @@
 
 This repository contains JAX example code for loading and running the Grok-1 open-weights model.
 
-Make sure to download the checkpoint and place the `ckpt-0` directory in `checkpoints` - see [Downloading the weights](#downloading-the-weights)
+Make sure to download the checkpoint and place `ckpt-0` directory in `checkpoint` - see [Downloading the weights](Downloading-the-weights)
 
 Then, run
 
@@ -18,31 +18,14 @@ The script loads the checkpoint and samples from the model on a test input.
 Due to the large size of the model (314B parameters), a machine with enough GPU memory is required to test the model with the example code.
 The implementation of the MoE layer in this repository is not efficient. The implementation was chosen to avoid the need for custom kernels to validate the correctness of the model.
 
-# Model Specifications
-
-Grok-1 is currently designed with the following specifications:
-
-- **Parameters:** 314B
-- **Architecture:** Mixture of 8 Experts (MoE)
-- **Experts Utilization:** 2 experts used per token
-- **Layers:** 64
-- **Attention Heads:** 48 for queries, 8 for keys/values
-- **Embedding Size:** 6,144
-- **Tokenization:** SentencePiece tokenizer with 131,072 tokens
-- **Additional Features:**
-  - Rotary embeddings (RoPE)
-  - Supports activation sharding and 8-bit quantization
-- **Maximum Sequence Length (context):** 8,192 tokens
-
 # Downloading the weights
 
 You can download the weights using a torrent client and this magnet link:
-
 ```
 magnet:?xt=urn:btih:5f96d43576e3d386c9ba65b883210a393b68210e&tr=https%3A%2F%2Facademictorrents.com%2Fannounce.php&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce
 ```
 
-or directly using [HuggingFace 🤗 Hub](https://huggingface.co/xai-org/grok-1):
+or directly using HuggingFace:
 ```
 git clone https://github.com/xai-org/grok-1.git && cd grok-1
 pip install huggingface_hub[hf_transfer]

checkpoint.py

@@ -26,10 +26,6 @@ import tempfile
 from concurrent.futures import ThreadPoolExecutor, wait
 from typing import Any, Optional
 
-# For get_load_path_str
-# A simple caching mechanism to avoid recomputing regex matches for paths that have already been processed.
-from functools import lru_cache
-
 import jax
 import numpy as np
 from jax.experimental import multihost_utils
@@ -108,21 +104,7 @@ def load_tensors(shaped_arrays, directory, mesh_config, tensor_indices=None):
         else:
             fs.append(pool.submit(np.zeros, t.shape, dtype=t.dtype))
     wait(fs)
-    
+    return [f.result() for f in fs]
-    # return [f.result() for f in fs]
-    """
-    Improve error reporting in load_tensors by catching exceptions within the futures-
-    and logging detailed information about the failure.
-    """
-    results = []
-    for future in fs:
-        try:
-            result = future.result()
-            results.append(result)
-        except Exception as e:
-            logger.error(f"Failed to load tensor: {e}")
-            raise
-    return results
 
 
 def path_tuple_to_string(path: tuple) -> str:
@@ -137,22 +119,6 @@ def path_tuple_to_string(path: tuple) -> str:
     return "/".join(pieces)
 
 
-"""
-For get_load_path_str(),
-introducing a simple caching mechanism to avoid recomputing regex matches for paths that have already been processed.
-"""
-@lru_cache(maxsize=32)  # Set maxsize to 32MB
-def get_load_path_str_cached(
-    init_path_str: str,
-    load_rename_rules: Optional[list[tuple[str, str]]] = None,
-    load_exclude_rules: Optional[list[str]] = None,
-) -> Optional[str]:
-    return get_load_path_str(
-        init_path_str, 
-        load_rename_rules, 
-        load_exclude_rules
-    )
-
 def get_load_path_str(
     init_path_str: str,
     load_rename_rules: Optional[list[tuple[str, str]]] = None,
@@ -191,7 +157,7 @@ def replace_with_load_state(
     data_model_shards = math.prod(mesh_config)
     for i, (init_path, tensor) in enumerate(flatten_init):
         init_path_str = path_tuple_to_string(init_path)
-        load_path_str = get_load_path_str_cached(init_path_str, load_rename_rules, load_exclude_rules)
+        load_path_str = get_load_path_str(init_path_str, load_rename_rules, load_exclude_rules)
         if load_path_str is None:
             rank_logger.info(f"Excluded from restore: {init_path_str}.")
             replaced.append(tensor)

requirements.txt

@@ -1,4 +1,4 @@
 dm_haiku==0.0.12
-jax[cuda12-pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+jax[cuda12_pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
 numpy==1.26.4
 sentencepiece==0.2.0