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README.md

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+---
+datasets:
+- open-web-math/open-web-math
+---
+
+Mistral-7b with continued pretraining using Quiet-STaR (https://arxiv.org/abs/2403.09629) for generating 8 thought tokens before each output token.

added_tokens.json

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+{
+  "<|endthought|>": 32000,
+  "<|startthought|>": 32001
+}

config.json

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+{
+    "_name_or_path": "Crystalcareai/Quiet-Star-Custom",
+    "architectures": [
+        "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",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "max_thoughts": 10,
+    "merged_lm_and_talk_heads": false,
+    "merged_lm_and_think_heads": true,
+    "merged_talk_heads": true,
+    "model_type": "quiet",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 10000.0,
+    "sliding_window": 4096,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.37.0.dev0",
+    "use_cache": true,
+    "use_complex_talk_head": true,
+    "use_complex_think_head": false,
+    "use_concat_talk_head": true,
+    "use_shallow_talk": false,
+    "use_shallow_think": true,
+    "use_weighted_talk_head": true,
+    "vocab_size": 32002,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.15",
+        "bits": 4.6,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

configuration_quiet.py

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+# 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,
+        )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.37.0.dev0"
+}

inference.py

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+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
+)

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train.py

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+import torch
+torch.backends.cuda.matmul.allow_tf32 = True
+import random
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from datasets import load_dataset
+from transformers import TrainingArguments
+from trl import SFTTrainer
+from peft import LoraConfig
+
+import time
+random_seed = 42
+torch.manual_seed(random_seed)
+random.seed(random_seed)
+
+dataset = load_dataset("HuggingFaceH4/deita-10k-v0-sft", split="train_sft")
+
+n_ahead_talk_global = 2
+n_passes_global = 2
+n_ahead_global = 2
+n_examples = 0
+full_batch_size = 2
+eval_and_logging_steps = 2
+save_steps = 100
+
+
+def model_init(params):
+    original = False
+    if params is None:
+        params = {}
+    else:
+        params = params.params
+    # save params to file
+    n_ahead = params.get("n_ahead", n_ahead_global if not original else 1)
+    n_ahead_talk = params.get("n_ahead_talk", n_ahead_talk_global if not original else 1)
+    n_passes = params.get("n_passes", n_passes_global if not original else 1)
+    gumbel_temperature = params.get("gumbel_temperature", 1)
+    use_start_thought_token = params.get("use_start_thought_token", True)
+    use_end_thought_token = params.get("use_end_thought_token", True)
+    include_policy_loss = params.get("include_policy_loss", True)
+    gumbel_detach = params.get("gumbel_detach", True)
+    merged_talk_heads = params.get("merged_talk_heads", True)
+    gradient_accumulation_steps = params.get("gradient_accumulation_steps", global_gradient_accumulation_steps)
+    residual_think_head = params.get("residual_think_head", False)
+    optimize_lm_head_only_at_start = params.get("optimize_lm_head_only_at_start", False)
+
+    model_id = "Crystalcareai/Quiet-Star-Custom"
+    tokenizer_id = model_id
+    print("Loading model")
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+        max_thoughts=n_ahead + n_ahead_talk + 1,
+        merged_talk_heads=merged_talk_heads,
+        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,
+        trust_remote_code=True,  
+        device_map="auto",
+    )
+    print("Loaded model")
+   
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id,padding=False,truncation=True)
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    special_tokens_to_add = []
+    if model.use_start_thought_token:
+        special_tokens_to_add.append("<|startthought|>")
+    if model.use_end_thought_token:
+        special_tokens_to_add.append("<|endthought|>")
+    if special_tokens_to_add:
+        tokenizer.add_special_tokens({"additional_special_tokens": special_tokens_to_add})
+        model.resize_token_embeddings(len(tokenizer))
+    model.tokenizer = tokenizer
+    model.gumbel_detach = gumbel_detach
+    model.include_policy_loss = include_policy_loss
+    model.use_end_thought_token = use_end_thought_token
+    model.use_start_thought_token = use_start_thought_token
+    model.n_ahead = n_ahead
+    model.n_ahead_talk = n_ahead_talk
+    model.n_passes = n_passes
+    model.n_tokens_print = gradient_accumulation_steps
+    model.gradient_accumulation_steps = gradient_accumulation_steps
+    model.residual_think_head = residual_think_head
+    model.optimize_lm_head_only_at_start = optimize_lm_head_only_at_start
+    model.gumbel_temperature = gumbel_temperature
+    model.original_mode = original
+    model.config_params = params
+    model.run_start = int(time.time())
+    model.kill_after = 100
+    model.train()
+    return model
+
+
+batch_size = full_batch_size // n_passes_global
+global_gradient_accumulation_steps = full_batch_size // batch_size
+run_id = int(time.time())
+training_args = TrainingArguments(
+    output_dir="./out",
+    num_train_epochs=3,
+    per_device_train_batch_size=1,
+    gradient_checkpointing=False,
+    gradient_accumulation_steps=4,
+    optim="adamw_torch_fused",
+    logging_steps=1,
+    save_strategy="steps",
+    save_steps=300,
+    bf16=True,
+    tf32=False,
+    # auto_find_batch_size=True
+    learning_rate=2e-07,
+    max_grad_norm=1.0,  # Gradient clipping with a maximum gradient norm of 0.3
+    warmup_steps=100,
+    lr_scheduler_type="cosine",
+    push_to_hub=False,
+)
+
+# peft_config = LoraConfig(
+#           r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+#     target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+#                       "gate_proj", "up_proj", "down_proj",],
+#     lora_alpha = 16,
+#     lora_dropout = 0, # Supports any, but = 0 is optimized
+#     bias = "none", # Enable Dora method
+#     use_dora=True,
+# )
+
+torch.autograd.set_detect_anomaly(True)
+model = model_init(None)  # Initialize the model
+tokenizer = model.tokenizer  
+
+trainer = SFTTrainer(
+    args=training_args,
+    train_dataset=dataset,
+    model=model,
+    # peft_config=peft_config,
+    tokenizer=tokenizer,  
+)
+
+trainer.train()