Manually merge local checkpoint

README.md

@@ -1,3 +1,60 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- RUC-AIBOX/long_form_thought_data_5k
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+---
+# Model Card for Model ID
+
+## Model Details:
+
+- **Base Model:** Qwen/Qwen2.5-32B-Instruct  
+- **datasets:** RUC-AIBOX/long_form_thought_data_5k
+- **Training Framework:** Supervised Fine-tuning  
+- **Parameters:** 32B  
+- **Special Features:**
+  - Replace 50% full attention layers with streaming attention
+
+---
+
+## Model Details
+
+**QwQ-LightTransfer** is a 32B-parameter model built on **Qwen/Qwen2.5-32B-Instruct** and fine-tuned via SFT on **RUC-AIBOX/long_form_thought_data_5k**. By replacing 50% of the model’s full attention layers with streaming attention,specifically layers [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 35, 37, 38, 43, 51], it substantially reduces memory costs. QwQ-LightTransfer scores 53.3% on the advanced math benchmark AIME24, demonstrating its strong o1-like long reasoning capabilities.
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = 'QwQ-32B-LightTransfer'
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype=torch.bfloat16,trust_remote_code=True,device_map='auto')
+
+text = "Hi, I'm QwQ-32B-LightTransfer,"
+
+inputs = tokenizer(text, return_tensors='pt').to(model.device)
+
+
+with torch.no_grad():
+    outputs = model.generate(inputs['input_ids'],max_gen_len=256)
+
+print(tokenizer.decode(outputs[0]))
+```
+
+
+## Citation
+
+
+```
+@misc{zhang2025lighttransferlongcontextllmsecretly,
+      title={LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation}, 
+      author={Xuan Zhang and Fengzhuo Zhang and Cunxiao Du and Chao Du and Tianyu Pang and Wei Gao and Min Lin},
+      year={2025},
+      eprint={2410.13846},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.13846}, 
+}
+```
+

added_tokens.json

@@ -0,0 +1,24 @@
+{
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+  "<tool_call>": 151657,
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+  "<|box_start|>": 151648,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "Qwen2LitTransForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_qwenLitTrans.Qwen2LitTransConfig",
+    "AutoModelForCausalLM": "modeling_qwenLitTrans.Qwen2LitTransForCausalLM"
+  },
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2littrans",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

configuration_qwenLitTrans.py

@@ -0,0 +1,202 @@
+# coding=utf-8
+# Copyright 2025 LightTransfer team and the HuggingFace Inc. team. All rights reserved.
+# This code is based on src/transformers/models/qwen2/configuration_qwen2.py
+#
+# 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.
+"""Qwen2LitTrans model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen2LitTransConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2LitTransModel`]. It is used to instantiate a
+    Qwen2LitTrans model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2LitTrans-7B-beta [Qwen/Qwen2LitTrans-7B-beta](https://huggingface.co/Qwen/Qwen2LitTrans-7B-beta).
+
+    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 151936):
+            Vocabulary size of the Qwen2LitTrans model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2LitTransModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            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 32):
+            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 `32`.
+        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 32768):
+            The maximum sequence length that this model might ever be used with.
+        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`.
+        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.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import Qwen2Model, Qwen2Config
+
+    >>> # Initializing a Qwen2 style configuration
+    >>> configuration = Qwen2Config()
+
+    >>> # Initializing a model from the Qwen2-7B style configuration
+    >>> model = Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2littrans"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `Qwen2LitTrans`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        **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.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window  # we check `use_sliding_window` in the modeling code
+        self.max_window_layers = max_window_layers
+
+        # 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.rope_scaling = rope_scaling
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.45.2"
+}

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+}

modeling_qwenLitTrans.py

@@ -0,0 +1,798 @@
+# coding=utf-8
+#
+# Copyright 2025 LightTransfer team and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/qwen2/modeling_qwen2.py
+#
+# 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.
+"""PyTorch Qwen2LitTrans model."""
+
+import matplotlib.pyplot as plt
+import os
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+torch.set_printoptions(precision=2)
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from functools import partial
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    is_torchdynamo_compiling,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_qwenLitTrans import Qwen2LitTransConfig
+from flash_attn import flash_attn_func, flash_attn_with_kvcache
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+# from torch.nn.attention.flex_attention import flex_attention
+
+logger = logging.get_logger(__name__)
+
+
+_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
+_CONFIG_FOR_DOC = "Qwen2LitTransConfig"
+
+def torch_tree_attention(q, k_cache, v_cache, k, v, kv_seqlen=None, tree_mask=None):
+    bsz, num_kv_heads, kv_len, head_dim = k.size()
+    kv_groups = q.size(1) // num_kv_heads
+
+    insert_indices = kv_seqlen.unsqueeze(-1) + torch.arange(kv_len, device=kv_seqlen.device).unsqueeze(0)
+    insert_indices = insert_indices[:, None, :, None].expand(-1, num_kv_heads, -1, head_dim)
+
+    k_cache.scatter_(2, insert_indices, k)
+    v_cache.scatter_(2, insert_indices, v)
+
+    # NOTE must after the scater!
+    cur_kv_seqlen = kv_seqlen +  k.size(2)
+
+    max_len = cur_kv_seqlen.max().item() #, k_cache.size(2))
+    
+    k, v = k_cache[:, :, :max_len], v_cache[:, :, :max_len]
+    seqlen_mask = torch.arange(max_len, device=k.device) >= cur_kv_seqlen.unsqueeze(-1)  # [B, S]
+    seqlen_mask = seqlen_mask.unsqueeze(1)
+
+
+    if kv_groups > 1:
+        k = k.unsqueeze(2).expand(-1, -1, kv_groups, -1, -1).reshape(bsz, num_kv_heads * kv_groups, max_len, head_dim)
+        v = v.unsqueeze(2).expand(-1, -1, kv_groups, -1, -1).reshape(bsz, num_kv_heads * kv_groups, max_len, head_dim)
+
+
+    out = torch.nn.functional.scaled_dot_product_attention(
+        q,
+        k[:, :, :max_len],
+        v[:, :, :max_len],
+        attn_mask=None,
+        dropout_p=0.0,
+        is_causal=True,
+    )
+
+    return out.transpose(1, 2)
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2LitTrans
+class Qwen2LitTransRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Qwen2LitTransRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Qwen2LitTrans
+class Qwen2LitTransRotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim=None,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+        rope_type="default",
+        config: Optional[Qwen2LitTransConfig] = None,
+    ):
+        super().__init__()
+        # TODO (joao): remove the `if` below, only used for BC
+        self.rope_kwargs = {}
+        if config is None:
+            logger.warning_once(
+                "`Qwen2LitTransRotaryEmbedding` can now be fully parameterized by passing the model config through the "
+                "`config` argument. All other arguments will be removed in v4.46"
+            )
+            self.rope_kwargs = {
+                "rope_type": rope_type,
+                "factor": scaling_factor,
+                "dim": dim,
+                "base": base,
+                "max_position_embeddings": max_position_embeddings,
+            }
+            self.rope_type = rope_type
+            self.max_seq_len_cached = max_position_embeddings
+            self.original_max_seq_len = max_position_embeddings
+        else:
+            # BC: "rope_type" was originally "type"
+            # if config.rope_scaling is not None:
+                # self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+            # else:
+            self.rope_type = "default"
+            self.max_seq_len_cached = config.max_position_embeddings
+            self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len, **self.rope_kwargs
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+
+        # Core RoPE block
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2LitTrans
+class Qwen2LitTransMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state):
+        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Qwen2LitTransAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: Qwen2LitTransConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = Qwen2LitTransRotaryEmbedding(config=self.config)
+        self.K_Cache = None
+        self.V_Cache = None
+        self.answer_K_Cache = None
+        self.answer_V_Cache = None
+        self.max_len = 2048
+        self.log_ratio = math.log(0.7)
+        self.prefix_lens = None
+        self.layer_idx = layer_idx
+        self.last_layer = (config.num_hidden_layers == self.layer_idx + 1)
+        self.softmax_scale = 1 / (128 ** 0.5)
+        self.range_indices = None
+        self.sink_len = 128
+        self.kv_len = 2048
+        self.lazy_ratio = None
+        self.lazy_cnt = 0
+        self.key_importance = None
+        self.fa_cache_lens = None
+        self.print_flag = True
+        self.lazy_list = [38, 51, 35, 26, 23, 43, 37, 24, 21, 28, 30, 22, 32, 33, 13, 20, 31, 27, 18, 9, 15, 19, 16, 17, 12, 8, 6, 10, 7, 5, 11, 14]
+        # self.lazy_list =[31, 27, 18, 9, 15, 19, 16, 17, 12, 8, 6, 10, 7, 5, 11, 14]
+        # self.lazy_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]
+        # self.lazy_list = [-1]
+    def forward(
+        self,
+        hidden_states,
+        position_embeddings,
+        cache_lens=None,
+        flex_attn=None,
+        tree_mask=None,
+        exec_type="training",
+        vis_lens=None,
+        sample_id=None,
+    ):
+        
+        kv_cache = None
+        torch.cuda.empty_cache()
+        if exec_type == "prefill":
+            # print(self.layer_idx)
+            y = self.prefill(hidden_states, position_embeddings)
+            torch.cuda.empty_cache()
+        elif exec_type == "decoding":
+            # print(self.layer_idx)
+            if self.layer_idx in self.lazy_list:
+                y = self.streaming_decoding(hidden_states, position_embeddings, cache_lens)
+            else:
+                y = self.decoding(hidden_states, position_embeddings, cache_lens)
+        else:
+            raise ValueError(f"Unknown inference_type: {exec_type}")
+        return y, kv_cache
+    
+    def prefill(
+            self,
+            hidden_states,
+            position_embeddings,
+            ):
+    
+        bsz, q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=2)
+
+        
+        # print(attn_output.shape)
+        if self.layer_idx in self.lazy_list:
+            self.K_Cache = query_states.new_zeros((bsz, 1024+128, self.num_key_value_heads, self.head_dim))
+            self.V_Cache = query_states.new_zeros((bsz, 1024+128, self.num_key_value_heads, self.head_dim))
+        else:
+            self.K_Cache = query_states.new_zeros((bsz, 32000, self.num_key_value_heads, self.head_dim))
+            self.V_Cache = query_states.new_zeros((bsz, 32000, self.num_key_value_heads, self.head_dim))
+        # print(self.K_Cache.size())
+        # print(self.K_Cache)
+
+        self.K_Cache[:, :q_len] = key_states
+        self.V_Cache[:, :q_len] = value_states
+        attn_output = flash_attn_func(query_states, key_states, value_states, causal=True)
+        self.range_indices = torch.arange(8192, device=self.K_Cache.device)
+        attn_output = self.o_proj(attn_output.view(bsz, q_len, -1))
+        self.lazy_cnt = 0
+        self.lazy_ratio = None
+
+        return attn_output
+    def decoding(
+            self,
+            hidden_states,
+            position_embeddings,
+            cache_lens,
+            ):
+
+        bsz, q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=2)
+
+        attn_output = flash_attn_with_kvcache(query_states, self.K_Cache, self.V_Cache, key_states, value_states, causal=True, cache_seqlens=cache_lens)
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output
+    def streaming_decoding(
+            self,
+            hidden_states,
+            position_embeddings,
+            cache_lens,
+            ):
+        # if self.print_flag:
+            # print(True)
+        # print(self.layer_idx)
+            # self.print_flag = False
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=2)
+        if cache_lens < self.K_Cache.size(1):
+            attn_output = flash_attn_with_kvcache(query_states, self.K_Cache, self.V_Cache, key_states, value_states, causal=True, cache_seqlens=cache_lens)
+        else:
+            remap_cache_len = (cache_lens % (self.K_Cache.size(1) - self.sink_len)) + self.sink_len - 1
+            self.K_Cache[self.range_indices[:bsz], remap_cache_len] = key_states
+            self.V_Cache[self.range_indices[:bsz], remap_cache_len] = value_states
+            attn_output = flash_attn_with_kvcache(query_states, self.K_Cache, self.V_Cache, causal=True, cache_seqlens=self.K_Cache.size(1))
+        # print(self.K_Cache.size(1))
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output
+
+Qwen2LitTrans_ATTENTION_CLASSES = {
+    "eager": Qwen2LitTransAttention,
+    "flash_attention_2": Qwen2LitTransAttention,
+    "sdpa": Qwen2LitTransAttention,
+}
+
+
+class Qwen2LitTransDecoderLayer(nn.Module):
+    def __init__(self, config: Qwen2LitTransConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.layer_idx = layer_idx
+        self.last_layer = (config.num_hidden_layers == self.layer_idx + 1)
+        if config.sliding_window and config._attn_implementation != "flash_attention_2":
+            logger.warning_once(
+                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
+                "unexpected results may be encountered."
+            )
+        self.self_attn = Qwen2LitTrans_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+        self.mlp = Qwen2LitTransMLP(config)
+        self.input_layernorm = Qwen2LitTransRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2LitTransRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        position_embeddings,  # will become mandatory in v4.46
+        cache_lens=None,
+        flex_attn=None,
+        exec_type=None,
+        tree_mask=None,
+        vis_lens=None,
+        sample_id=None,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, kv_cache = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            cache_lens=cache_lens,
+            flex_attn=flex_attn,
+            exec_type=exec_type,
+            tree_mask=tree_mask,
+            vis_lens=vis_lens,
+            sample_id=sample_id,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states, kv_cache)
+
+        return outputs
+
+class Qwen2LitTransPreTrainedModel(PreTrainedModel):
+    config_class = Qwen2LitTransConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2LitTransDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+class Qwen2LitTransModel(Qwen2LitTransPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2LitTransDecoderLayer`]
+
+    Args:
+        config: Qwen2LitTransConfig
+    """
+
+    def __init__(self, config: Qwen2LitTransConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [Qwen2LitTransDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = Qwen2LitTransRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen2LitTransRotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+    
+
+    def forward(
+        self,
+        input_ids,
+        position_ids=None,
+        inputs_embeds=None,
+        cache_lens=None,
+        flex_attn=None,
+        exec_type=None,
+        tree_mask=None,
+        vis_lens=None,
+        sample_id=None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+
+
+        if position_ids is None:
+            position_ids = torch.arange(0, input_ids.size(1))[None, :].to(input_ids.device)
+            if cache_lens is not None:
+                # print(f"cache_lens: {cache_lens}")
+                # print(f"position_ids: {position_ids}")
+                if tree_mask is None:
+                    position_ids = position_ids + cache_lens[:, None]
+                else:
+                    position_ids = tree_mask.sum(dim=-1) - 1 + cache_lens[:, None]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers:
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    position_embeddings,
+                    cache_lens,
+                    flex_attn,
+                    exec_type,
+                    tree_mask,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    position_embeddings,
+                    cache_lens,
+                    flex_attn,
+                    exec_type,
+                    tree_mask,
+                    vis_lens,
+                    sample_id,
+                )
+
+            hidden_states = layer_outputs[0]
+
+        hidden_states = self.norm(hidden_states)
+
+        if exec_type == "glide_training":
+            kv_cache = layer_outputs[1]
+        else:
+            kv_cache = None
+        # add hidden states from the last decoder layer
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=kv_cache,
+            hidden_states=None,
+            attentions=None,
+        )
+
+
+class Qwen2LitTransForCausalLM(Qwen2LitTransPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen2LitTransModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.eod = 151645
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def set_max_gen_len(self, max_gen_len):
+        for layer in self.model.layers:
+            layer.self_attn.max_len = max_gen_len
+
+    def set_stream_len(self, sink_len, kv_len):
+        for layer in self.model.layers:
+            layer.self_attn.sink_len = sink_len
+            layer.self_attn.kv_len = kv_len
+            layer.self_attn.max_len = sink_len + kv_len
+    
+    def set_log_ratio(self, log_ratio):
+        for layer in self.model.layers:
+            layer.self_attn.log_ratio = log_ratio
+    
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids,
+        position_ids=None,
+        inputs_embeds=None,
+        labels=None,
+        cache_lens=None,
+        exec_type="training",
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        
+        if exec_type == "free_training":
+
+            bsz, seqlen = position_ids.size()
+            eod_mask = position_ids.eq(self.eod)
+            eod_indices = torch.nonzero(eod_mask, as_tuple=False)
+
+
+            assert eod_indices.size(0) == bsz * self.sample_num, "dataset needs all batch samples have same output samples equasl to self.sample_num"
+
+            eod_col = eod_indices[:, 1].view(bsz, 10)
+            prefix_end, doc_end = eod_col[:, 0], eod_col[:, 1:]
+            # block_mask = construct_doc_mask(bsz, prefix_end, doc_end, seqlen)
+            # block_mask = create_block_mask(construct_doc_mask, B=None, H=None, Q_LEN=8192, KV_LEN=8192, _compile=True)
+            # flex_attn = torch.compile(partial(flex_attention, block_mask=block_mask, enable_gqa=True))
+            flex_attn = None
+        else:
+            flex_attn = None
+
+        outputs = self.model(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            cache_lens=cache_lens,
+            flex_attn=flex_attn,
+            exec_type=exec_type,
+        )
+
+        hidden_states = outputs[0]
+        last_kv = outputs[1]
+
+        loss = None
+        if labels is not None:
+            from liger_kernel.transformers import LigerFusedLinearCrossEntropyLoss
+            loss_fn = LigerFusedLinearCrossEntropyLoss()
+            hidden_dim = hidden_states.size(-1)
+            loss = loss_fn(self.lm_head.weight, hidden_states[:, 1:].reshape(-1, hidden_dim), labels[:, :-1].view(-1))
+        else:
+            logits = self.lm_head(hidden_states[:, -128:, :]).float()
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=last_kv,
+            hidden_states=None,
+            attentions=None,
+        )
+
+    def attn_vis(self, input_ids, padding_id=151645, sample_idx=0):
+        assert input_ids.size(0) == 1, "only support batch_size=1"
+        save_dir = "vis/sample_id"
+        os.makedirs(save_dir, exist_ok=True)
+        vis_lens = input_ids.eq(padding_id).sum()
+        outputs = self.model(
+            input_ids=input_ids,
+            cache_lens=None,
+            flex_attn=None,
+            exec_type="attn_vis",
+            vis_lens=vis_lens,
+            sample_id=sample_idx,
+        )
+
+    
+    def generate(self, input_ids, max_gen_len=32000, pad_id=151643, eos_id=151645):
+        assert input_ids != None, "please give the input"
+        bsz = input_ids.size(0)
+        output_ids = input_ids.new_zeros((bsz, max_gen_len)).fill_(pad_id)
+        
+        self.set_max_gen_len(max_gen_len)
+        
+        cache_lens = input_ids.new_zeros((bsz)).int()
+        hidden_states = self.model.forward(input_ids, exec_type="prefill").last_hidden_state
+        input_len = input_ids.ne(pad_id).sum(dim=-1)
+        output_ids[:, 0] = self.lm_head(hidden_states[range(bsz), input_len-1, :]).argmax(dim=-1)
+        cache_lens += input_len
+        for step in range(1, max_gen_len):
+            if step % 128 == 1:
+                print(f"current we are decoding {step}-st token")
+            input_ids = output_ids[range(bsz), cache_lens - input_len].view(bsz, -1)
+            hidden_states = self.model.forward(input_ids, cache_lens=cache_lens.clone(), exec_type="decoding").last_hidden_state
+            llm_output = self.lm_head(hidden_states[:, -1, :]).argmax(dim=-1)
+            cache_lens += 1
+            output_ids[range(bsz), cache_lens - input_len] = llm_output.view(-1)
+            if (output_ids.eq(eos_id).any(dim=-1).all()):
+                mask = (output_ids == eos_id).int().cumsum(dim=1) >= 1
+                output_ids[mask] = pad_id
+                break
+
+        return output_ids

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,208 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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