Upload config

config.json

@@ -1,10 +1,9 @@
 {
-  "_name_or_path": "progen2-small",
   "activation_function": "gelu_new",
-  "architectures": [
-    "ProGenForCausalLM"
-  ],
   "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_progen.ProGenConfig"
+  },
   "bos_token_id": 1,
   "embd_pdrop": 0.0,
   "eos_token_id": 2,
@@ -20,13 +19,6 @@
   "resid_pdrop": 0.0,
   "rotary_dim": 32,
   "scale_attn_weights": true,
-  "summary_activation": null,
-  "summary_first_dropout": 0.1,
-  "summary_proj_to_labels": true,
-  "summary_type": "cls_index",
-  "summary_use_proj": true,
-  "tokenizer_class": "GPT2Tokenizer",
-  "torch_dtype": "float32",
   "transformers_version": "4.40.0",
   "use_cache": true,
   "vocab_size_emb": 32,

configuration_progen.py

@@ -0,0 +1,87 @@
+# coding=utf-8
+# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Modified configuration implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/configuration_gptj.py
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class ProGenConfig(PretrainedConfig):
+    model_type = "progen"
+
+    def __init__(
+        self,
+        vocab_size_emb=32,
+        vocab_size_lm_head=32,
+        n_positions=1024,
+        n_embd=1024,
+        n_layer=12,
+        n_head=16,
+        rotary_dim=32,
+        n_inner=None,
+        activation_function="gelu_new",
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attn_pdrop=0.0,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        scale_attn_weights=True,
+        gradient_checkpointing=False,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs
+    ):
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+        self.vocab_size_emb = vocab_size_emb
+        self.vocab_size_lm_head = vocab_size_lm_head
+        self.n_positions = n_positions      # context window size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.rotary_dim = rotary_dim
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.gradient_checkpointing = gradient_checkpointing
+        self.scale_attn_weights = scale_attn_weights
+        self.use_cache = use_cache
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+    @property
+    def max_position_embeddings(self):
+        return self.n_positions
+
+    @property
+    def hidden_size(self):
+        return self.n_embd
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer