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config.json

@@ -1,9 +1,14 @@
 {
-  "_name_or_path": "cache/qstar/1710326256/checkpoint-100",
+  "_name_or_path": "Crystalcareai/Quiet-Star-Custom",
   "architectures": [
-    "MistralForCausalLM"
+    "QuietForCausalLM"
   ],
   "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_quiet.QuietConfig",
+    "AutoModel": "modeling_quiet.QuietModel",
+    "AutoModelForCausalLM": "modeling_quiet.QuietForCausalLM"
+  },
   "bos_token_id": 1,
   "eos_token_id": 2,
   "hidden_act": "silu",
@@ -15,7 +20,7 @@
   "merged_lm_and_talk_heads": false,
   "merged_lm_and_think_heads": true,
   "merged_talk_heads": true,
-  "model_type": "mistral",
+  "model_type": "quiet",
   "num_attention_heads": 32,
   "num_hidden_layers": 32,
   "num_key_value_heads": 8,
@@ -33,4 +38,4 @@
   "use_shallow_think": true,
   "use_weighted_talk_head": true,
   "vocab_size": 32002
-}
+}

configuration_quiet.py

@@ -0,0 +1,172 @@
+# coding=utf-8
+# Copyright 2023 Quiet AI and the HuggingFace Inc. team. 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.
+""" Quiet model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+QUIET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "quietai/Quiet-7B-v0.1": "https://huggingface.co/quietai/Quiet-7B-v0.1/resolve/main/config.json",
+    "quietai/Quiet-7B-Instruct-v0.1": "https://huggingface.co/quietai/Quiet-7B-Instruct-v0.1/resolve/main/config.json",
+}
+
+
+class QuietConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`QuietModel`]. It is used to instantiate an
+    Quiet model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Quiet-7B-v0.1 or Quiet-7B-Instruct-v0.1.
+
+    [quietai/Quiet-7B-v0.1](https://huggingface.co/quietai/Quiet-7B-v0.1)
+    [quietai/Quiet-7B-Instruct-v0.1](https://huggingface.co/quietai/Quiet-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Quiet model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`QuietModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Quiet's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import QuietModel, QuietConfig
+
+    >>> # Initializing a Quiet 7B style configuration
+    >>> configuration = QuietConfig()
+
+    >>> # Initializing a model from the Quiet 7B style configuration
+    >>> model = QuietModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "quiet"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        attention_dropout=0.0,
+        max_thoughts=16,
+        merged_talk_heads=True,
+        merged_lm_and_talk_heads=False,
+        merged_lm_and_think_heads=True,
+        use_concat_talk_head=True,
+        use_shallow_think=True,
+        use_shallow_talk=False,
+        use_complex_think_head=False,
+        use_complex_talk_head=True,
+        use_weighted_talk_head=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.max_thoughts = max_thoughts
+        self.merged_talk_heads = merged_talk_heads
+        self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
+        self.merged_lm_and_think_heads = merged_lm_and_think_heads
+        self.use_concat_talk_head = use_concat_talk_head
+        self.use_shallow_think = use_shallow_think
+        self.use_shallow_talk = use_shallow_talk
+        self.use_complex_think_head = use_complex_think_head
+        self.use_complex_talk_head = use_complex_talk_head
+        self.use_weighted_talk_head = use_weighted_talk_head
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

inference.py

@@ -0,0 +1,36 @@
+import torch
+from transformers import AutoTokenizer, TextStreamer, AutoModelForCausalLM
+
+model_path = "Crystalcareai/GemMoE-Medium-v0.4"
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    attn_implementation="flash_attention_2"
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+# Convert prompt to tokens
+prompt_template = "[INST] {prompt} [/INST]"
+
+prompt = "You're standing on the surface of the Earth. "\
+        "You walk one mile south, one mile west and one mile north. "\
+        "You end up exactly where you started. Where are you?"
+
+tokens = tokenizer(
+    prompt_template.format(prompt=prompt), 
+    return_tensors='pt'
+).input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(
+    tokens, 
+    streamer=streamer,
+    max_new_tokens=512
+)