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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cache/models--Qwen--Qwen3-30B-A3B-Instruct-2507/blobs/afe775cbdefb257e1bf1d8551fb930bd4b78d74da0b10bbb651f6b6a6f67c325 filter=lfs diff=lfs merge=lfs -text
+cache/models--Qwen--Qwen3-30B-A3B-Instruct-2507/blobs/c2bc4101cde6b7c9dda36bbdff815c2ff19541d7732e94d306d87a0806c133b3 filter=lfs diff=lfs merge=lfs -text
+cache/models--Qwen--Qwen3-30B-A3B-Instruct-2507/blobs/fa5623e55641935f938894cc91090732452ef22da12596e857c8dff4fa71627b filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

config.json

@@ -0,0 +1,46 @@
+{
+  "architectures": [
+    "ScatterbrainForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_scatterbrain.ScatterbrainConfig",
+    "AutoModelForCausalLM": "modeling_scatterbrain.ScatterbrainForCausalLM"
+  },
+  "model_type": "scatterbrain",
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 16,
+  "num_hidden_layers": 1,
+  "num_loop_iterations": 30,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "_original_num_hidden_layers": 48,
+  "_extracted_from_layer": 30,
+  "_source_model": "Qwen/Qwen3-30B-A3B-Instruct-2507"
+}

config.json.bak

@@ -0,0 +1,41 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "model_type": "qwen3_moe",
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 1,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "_original_num_hidden_layers": 48,
+  "_extracted_from_layer": 30,
+  "_source_model": "Qwen/Qwen3-30B-A3B-Instruct-2507"
+}

configuration_scatterbrain.py

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+# Configuration for Scatterbrain - a single MoE layer looped multiple times
+#
+# Based on Qwen3MoeConfig
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class ScatterbrainConfig(PretrainedConfig):
+    r"""
+    Configuration class for Scatterbrain - a model that loops a single MoE layer multiple times.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the model.
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 6144):
+            Dimension of the MLP intermediate representations (for dense layers).
+        moe_intermediate_size (`int`, *optional*, defaults to 768):
+            Dimension of the MLP intermediate representations for each expert.
+        num_hidden_layers (`int`, *optional*, defaults to 1):
+            Number of physical decoder layers. For Scatterbrain this is always 1.
+        num_loop_iterations (`int`, *optional*, defaults to 30):
+            Number of times to loop through the single layer.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer.
+        num_key_value_heads (`int`, *optional*, defaults to 4):
+            Number of key_value heads for Grouped Query Attention.
+        head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each attention head.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            Activation function in the MLP.
+        max_position_embeddings (`int`, *optional*, defaults to 262144):
+            Maximum sequence length.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            Standard deviation for weight initialization.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-6):
+            Epsilon for RMS normalization.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether to use KV cache.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie input and output embeddings.
+        rope_theta (`float`, *optional*, defaults to 10000000.0):
+            Base period for rotary embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            Dropout ratio for attention weights.
+        num_experts (`int`, *optional*, defaults to 128):
+            Number of experts in the MoE layer.
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            Number of experts activated per token.
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize top-k routing probabilities.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            Coefficient for router auxiliary loss.
+    """
+
+    model_type = "scatterbrain"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=2048,
+        intermediate_size=6144,
+        moe_intermediate_size=768,
+        num_hidden_layers=1,
+        num_loop_iterations=30,
+        num_attention_heads=32,
+        num_key_value_heads=4,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=262144,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000000.0,
+        rope_scaling=None,
+        attention_dropout=0.0,
+        attention_bias=False,
+        num_experts=128,
+        num_experts_per_tok=8,
+        norm_topk_prob=True,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        decoder_sparse_step=1,
+        mlp_only_layers=None,
+        sliding_window=None,
+        use_sliding_window=False,
+        max_window_layers=48,
+        pad_token_id=None,
+        bos_token_id=151643,
+        eos_token_id=151645,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_loop_iterations = num_loop_iterations
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        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
+        self.attention_bias = attention_bias
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.norm_topk_prob = norm_topk_prob
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.decoder_sparse_step = decoder_sparse_step
+        self.mlp_only_layers = mlp_only_layers if mlp_only_layers is not None else []
+        self.sliding_window = sliding_window
+        self.use_sliding_window = use_sliding_window
+        self.max_window_layers = max_window_layers
+
+        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,
+        )

convert_to_stacked.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+"""
+Convert Scatterbrain checkpoint from individual expert weights to stacked format.
+
+This converts:
+  model.layers.0.mlp.experts.{i}.gate_proj.weight -> model.layers.0.mlp.expert_gate_proj
+  model.layers.0.mlp.experts.{i}.up_proj.weight   -> model.layers.0.mlp.expert_up_proj
+  model.layers.0.mlp.experts.{i}.down_proj.weight -> model.layers.0.mlp.expert_down_proj
+"""
+
+import json
+import os
+import re
+import torch
+from safetensors.torch import load_file, save_file
+from collections import defaultdict
+
+
+def convert_checkpoint(model_dir: str, output_dir: str = None):
+    """Convert checkpoint from individual experts to stacked format."""
+    if output_dir is None:
+        output_dir = model_dir
+
+    # Load config to get num_experts
+    config_path = os.path.join(model_dir, "config.json")
+    with open(config_path) as f:
+        config = json.load(f)
+
+    num_experts = config["num_experts"]
+    print(f"Converting {num_experts} experts to stacked format...")
+
+    # Find all safetensor files
+    safetensor_files = sorted([f for f in os.listdir(model_dir) if f.endswith('.safetensors')])
+
+    if not safetensor_files:
+        raise ValueError(f"No safetensor files found in {model_dir}")
+
+    # Load all weights
+    all_weights = {}
+    for sf_file in safetensor_files:
+        path = os.path.join(model_dir, sf_file)
+        print(f"Loading {sf_file}...")
+        weights = load_file(path)
+        all_weights.update(weights)
+
+    # Pattern to match expert weights
+    expert_pattern = re.compile(r'(.+\.mlp)\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight')
+
+    # Group expert weights by layer and projection type
+    expert_groups = defaultdict(lambda: defaultdict(dict))
+    other_weights = {}
+
+    for key, value in all_weights.items():
+        match = expert_pattern.match(key)
+        if match:
+            layer_prefix = match.group(1)  # e.g., "model.layers.0.mlp"
+            expert_idx = int(match.group(2))
+            proj_type = match.group(3)  # gate_proj, up_proj, down_proj
+            expert_groups[layer_prefix][proj_type][expert_idx] = value
+        else:
+            other_weights[key] = value
+
+    # Stack expert weights
+    new_weights = dict(other_weights)
+
+    for layer_prefix, proj_types in expert_groups.items():
+        for proj_type, expert_weights in proj_types.items():
+            # Sort by expert index and stack
+            sorted_weights = [expert_weights[i] for i in range(num_experts)]
+            stacked = torch.stack(sorted_weights, dim=0)
+
+            new_key = f"{layer_prefix}.expert_{proj_type}"
+            new_weights[new_key] = stacked
+            print(f"  {new_key}: {stacked.shape}")
+
+    # Save to single safetensor file
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, "model.safetensors")
+
+    print(f"\nSaving to {output_path}...")
+    save_file(new_weights, output_path)
+
+    # Remove old safetensor files if output_dir == model_dir
+    if output_dir == model_dir:
+        for sf_file in safetensor_files:
+            if sf_file != "model.safetensors":
+                old_path = os.path.join(model_dir, sf_file)
+                print(f"Removing old file: {sf_file}")
+                os.remove(old_path)
+
+        # Update index file if it exists
+        index_path = os.path.join(model_dir, "model.safetensors.index.json")
+        if os.path.exists(index_path):
+            print("Removing old index file...")
+            os.remove(index_path)
+
+    print("\nConversion complete!")
+    print(f"New weights saved to: {output_path}")
+
+    # Print summary
+    total_params = sum(p.numel() for p in new_weights.values())
+    print(f"Total parameters: {total_params:,}")
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) < 2:
+        model_dir = "/home/aibox/training/scatterbrain-small-experimental"
+    else:
+        model_dir = sys.argv[1]
+
+    output_dir = sys.argv[2] if len(sys.argv) > 2 else None
+
+    convert_checkpoint(model_dir, output_dir)

generation_config.json

@@ -0,0 +1,13 @@
+{
+    "bos_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.7,
+    "top_k": 20,
+    "top_p": 0.8,
+    "transformers_version": "4.51.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45fb88364e2e510bacdd175533ce9540ea31458d2c75ae11fcf83ec66e0714ac
+size 2490906760

modeling_scatterbrain.py

@@ -0,0 +1,683 @@
+# Scatterbrain - A single MoE layer looped multiple times
+#
+# Based on Qwen3MoeForCausalLM architecture with looping logic from Loopstral
+
+import copy
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, DynamicLayer
+
+# Try to import Liger kernels for optimized operations
+try:
+    from liger_kernel.transformers import LigerRMSNorm, LigerSwiGLUMLP
+    LIGER_AVAILABLE = True
+except ImportError:
+    LIGER_AVAILABLE = False
+
+# Try to import ScatterMoE for optimized MoE computation
+try:
+    from scattermoe import flatten_sort_count, parallel_linear
+    SCATTERMOE_AVAILABLE = True
+except ImportError:
+    SCATTERMOE_AVAILABLE = False
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
+from transformers.utils.deprecation import deprecate_kwarg
+
+from .configuration_scatterbrain import ScatterbrainConfig
+
+
+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)
+
+
+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."""
+    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
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """Repeat KV heads for GQA."""
+    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)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class ScatterbrainAttention(nn.Module):
+    """Multi-headed attention with QK normalization."""
+
+    def __init__(self, config: ScatterbrainConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+        self.q_norm = ScatterbrainRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = ScatterbrainRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.sliding_window = getattr(config, "sliding_window", None)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        cache_slot_idx: Optional[int] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            # Use cache_slot_idx for looped layers - each iteration gets its own cache slot
+            slot_idx = cache_slot_idx if cache_slot_idx is not None else self.layer_idx
+            key_states, value_states = past_key_values.update(key_states, value_states, slot_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class ScatterbrainMLP(nn.Module):
+    def __init__(self, config, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = intermediate_size if intermediate_size is not None else 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, x):
+        # Fused SwiGLU: down(act(gate(x)) * up(x))
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class ScatterbrainSparseMoeBlock(nn.Module):
+    """
+    MoE block with optimized expert computation using ScatterMoE Triton kernels.
+
+    When ScatterMoE is available, uses fused Triton kernels for massive speedup.
+    Falls back to Python loop implementation when ScatterMoE is not available.
+
+    Key optimizations with ScatterMoE:
+    1. Fused scatter-gather operations in Triton
+    2. All experts processed in parallel on GPU
+    3. Efficient memory access patterns
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+        self.norm_topk_prob = config.norm_topk_prob
+        self.hidden_size = config.hidden_size
+        self.moe_intermediate_size = config.moe_intermediate_size
+
+        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+
+        # Stacked expert weights as Parameters
+        # Shape: [num_experts, out_features, in_features]
+        self.expert_gate_proj = nn.Parameter(
+            torch.empty(config.num_experts, config.moe_intermediate_size, config.hidden_size)
+        )
+        self.expert_up_proj = nn.Parameter(
+            torch.empty(config.num_experts, config.moe_intermediate_size, config.hidden_size)
+        )
+        self.expert_down_proj = nn.Parameter(
+            torch.empty(config.num_experts, config.hidden_size, config.moe_intermediate_size)
+        )
+
+        # Initialize weights
+        for param in [self.expert_gate_proj, self.expert_up_proj, self.expert_down_proj]:
+            nn.init.kaiming_uniform_(param, a=5**0.5)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states_flat = hidden_states.view(-1, hidden_dim)
+        num_tokens = hidden_states_flat.shape[0]
+
+        # Router
+        router_logits = self.gate(hidden_states_flat)
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+
+        if self.norm_topk_prob:
+            routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True)
+        routing_weights = routing_weights.to(hidden_states_flat.dtype)
+
+        if SCATTERMOE_AVAILABLE:
+            # Use ScatterMoE's optimized Triton kernels
+            final_hidden_states = self._forward_scattermoe(
+                hidden_states_flat, selected_experts, routing_weights
+            )
+        else:
+            # Fallback to Python loop
+            final_hidden_states = self._forward_loop(
+                hidden_states_flat, selected_experts, routing_weights
+            )
+
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+    def _forward_scattermoe(
+        self,
+        hidden_states: torch.Tensor,
+        selected_experts: torch.Tensor,
+        routing_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        """Forward pass using ScatterMoE Triton kernels."""
+        # Get sorting indices and expert offsets from ScatterMoE
+        sorted_expert_idxs, sorted_scattered_idxs, expert_offsets = flatten_sort_count(
+            selected_experts, num_experts=self.num_experts
+        )
+
+        # ScatterMoE parallel_linear expects weights in [E, in, out] format
+        # Our weights are stored as [E, out, in], so we need to permute them
+        # This matches what ParallelExperts.forward() does internally
+
+        # First pass: gate and up projections (hidden -> intermediate)
+        # We need to do gate and up separately, then combine with SiLU
+        gate_out = parallel_linear(
+            inputs=hidden_states,
+            expert_weights=self.expert_gate_proj.permute(0, 2, 1),  # [E, hidden, intermediate]
+            k=self.top_k,
+            sorted_expert_idxs=sorted_expert_idxs,
+            sorted_scattered_idxs=sorted_scattered_idxs,
+            expert_offsets=expert_offsets,
+            grouped_out=True,  # Keep output grouped by expert (size: num_tokens * k)
+        )
+
+        up_out = parallel_linear(
+            inputs=hidden_states,
+            expert_weights=self.expert_up_proj.permute(0, 2, 1),  # [E, hidden, intermediate]
+            k=self.top_k,
+            sorted_expert_idxs=sorted_expert_idxs,
+            sorted_scattered_idxs=sorted_scattered_idxs,
+            expert_offsets=expert_offsets,
+            grouped_out=True,  # Keep output grouped by expert (size: num_tokens * k)
+        )
+
+        # Apply SwiGLU activation
+        activated = F.silu(gate_out) * up_out
+
+        # Second pass: down projection (intermediate -> hidden) with gates for weighted sum
+        # When grouped_in=True, use k=1 since input is already expanded to num_tokens * top_k
+        output = parallel_linear(
+            inputs=activated,
+            expert_weights=self.expert_down_proj.permute(0, 2, 1),  # [E, intermediate, hidden]
+            k=1,  # k=1 because input is already grouped/expanded
+            sorted_expert_idxs=sorted_expert_idxs,
+            sorted_scattered_idxs=sorted_scattered_idxs,
+            expert_offsets=expert_offsets,
+            gates=routing_weights,  # Apply routing weights during scatter
+            grouped_in=True,  # Input is grouped by expert
+            grouped_out=False,  # Output ungrouped (back to token order)
+        )
+
+        return output
+
+    def _forward_loop(
+        self,
+        hidden_states: torch.Tensor,
+        selected_experts: torch.Tensor,
+        routing_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        """Fallback forward pass using Python loop."""
+        num_tokens = hidden_states.shape[0]
+
+        # Flatten token-expert assignments
+        flat_expert_indices = selected_experts.view(-1)
+        flat_token_indices = torch.arange(num_tokens, device=hidden_states.device).unsqueeze(1).expand(-1, self.top_k).reshape(-1)
+        flat_routing_weights = routing_weights.view(-1)
+
+        # Sort by expert for contiguous memory access
+        sorted_indices = torch.argsort(flat_expert_indices, stable=True)
+        sorted_expert_indices = flat_expert_indices[sorted_indices]
+        sorted_token_indices = flat_token_indices[sorted_indices]
+        sorted_routing_weights = flat_routing_weights[sorted_indices]
+        sorted_hidden = hidden_states[sorted_token_indices]
+
+        # Compute expert boundaries
+        expert_counts = torch.bincount(sorted_expert_indices, minlength=self.num_experts)
+        expert_offsets = torch.zeros(self.num_experts + 1, dtype=torch.long, device=hidden_states.device)
+        expert_offsets[1:] = torch.cumsum(expert_counts, dim=0)
+
+        final_hidden_states = torch.zeros_like(hidden_states)
+
+        # Pre-fetch to lists to avoid repeated tensor->scalar conversions
+        counts_list = expert_counts.tolist()
+        offsets_list = expert_offsets.tolist()
+
+        # Process each expert
+        for expert_idx in range(self.num_experts):
+            count = counts_list[expert_idx]
+            if count == 0:
+                continue
+
+            start_idx = offsets_list[expert_idx]
+            end_idx = offsets_list[expert_idx + 1]
+
+            # Get tokens for this expert
+            expert_tokens = sorted_hidden[start_idx:end_idx]
+            expert_weights = sorted_routing_weights[start_idx:end_idx]
+            token_indices = sorted_token_indices[start_idx:end_idx]
+
+            # Get this expert's weight matrices
+            gate_w = self.expert_gate_proj[expert_idx]
+            up_w = self.expert_up_proj[expert_idx]
+            down_w = self.expert_down_proj[expert_idx]
+
+            # SwiGLU computation: down(silu(gate(x)) * up(x))
+            gate_out = F.linear(expert_tokens, gate_w)
+            up_out = F.linear(expert_tokens, up_w)
+            activated = F.silu(gate_out) * up_out
+            expert_out = F.linear(activated, down_w)
+
+            # Weight and accumulate
+            weighted_out = expert_out * expert_weights.unsqueeze(-1)
+            final_hidden_states.index_add_(0, token_indices, weighted_out)
+
+        return final_hidden_states
+
+
+class _ScatterbrainRMSNormFallback(nn.Module):
+    """Fallback RMSNorm when Liger is not available."""
+    def __init__(self, hidden_size, eps=1e-6):
+        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}"
+
+
+# Use Liger RMSNorm if available, otherwise fallback
+ScatterbrainRMSNorm = LigerRMSNorm if LIGER_AVAILABLE else _ScatterbrainRMSNormFallback
+
+
+class ScatterbrainDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: ScatterbrainConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = ScatterbrainAttention(config, layer_idx)
+
+        # MoE layer
+        if config.num_experts > 0:
+            self.mlp = ScatterbrainSparseMoeBlock(config)
+        else:
+            self.mlp = ScatterbrainMLP(config, intermediate_size=config.intermediate_size)
+
+        self.input_layernorm = ScatterbrainRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = ScatterbrainRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        cache_slot_idx: Optional[int] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            cache_position=cache_position,
+            cache_slot_idx=cache_slot_idx,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # MLP / MoE
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        # Unpack MoE output
+        if isinstance(hidden_states, tuple):
+            hidden_states, _ = hidden_states
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class ScatterbrainRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor
+
+    def __init__(self, config: ScatterbrainConfig, device=None):
+        super().__init__()
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            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.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.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() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class ScatterbrainPreTrainedModel(PreTrainedModel):
+    config_class = ScatterbrainConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["ScatterbrainDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False  # MoE models don't work with torch.compile
+    _supports_attention_backend = True
+
+
+@auto_docstring
+class ScatterbrainModel(ScatterbrainPreTrainedModel):
+    def __init__(self, config: ScatterbrainConfig):
+        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)
+        # Only one physical layer
+        self.layers = nn.ModuleList(
+            [ScatterbrainDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = ScatterbrainRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = ScatterbrainRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Number of loop iterations and cache slots
+        self._num_loop_iterations = config.num_loop_iterations
+        self._num_cache_slots = config.num_loop_iterations
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # Handle cache creation/extension for looping
+        if use_cache:
+            if past_key_values is None:
+                # Create cache with enough slots for all loop iterations
+                cache_config = copy.copy(self.config)
+                cache_config.num_hidden_layers = self._num_cache_slots
+                past_key_values = DynamicCache(config=cache_config)
+            elif isinstance(past_key_values, DynamicCache) and len(past_key_values.layers) < self._num_cache_slots:
+                # Extend cache if created externally with fewer slots
+                while len(past_key_values.layers) < self._num_cache_slots:
+                    past_key_values.layers.append(DynamicLayer())
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+        causal_mask = mask_function(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # Loop through the single layer multiple times
+        # Each iteration gets its own cache slot
+        decoder_layer = self.layers[0]
+        for loop_idx in range(self._num_loop_iterations):
+            hidden_states = decoder_layer(
+                hidden_states,
+                position_embeddings=position_embeddings,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                cache_slot_idx=loop_idx,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring
+class ScatterbrainForCausalLM(ScatterbrainPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ScatterbrainModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        # 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 get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeCausalLMOutputWithPast:
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        outputs: MoeModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_router_logits=output_router_logits,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+
+__all__ = [
+    "ScatterbrainConfig",
+    "ScatterbrainForCausalLM",
+    "ScatterbrainModel",
+    "ScatterbrainPreTrainedModel",
+]

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+    "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": {
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+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
+        },
+        "151651": {
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+            "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": {
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+            "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
+        },
+        "151657": {
+            "content": "<tool_call>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151658": {
+            "content": "</tool_call>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151659": {
+            "content": "<|fim_prefix|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151660": {
+            "content": "<|fim_middle|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151661": {
+            "content": "<|fim_suffix|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151662": {
+            "content": "<|fim_pad|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151663": {
+            "content": "<|repo_name|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151664": {
+            "content": "<|file_sep|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151665": {
+            "content": "<tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151666": {
+            "content": "</tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151667": {
+            "content": "<think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151668": {
+            "content": "</think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        }
+    },
+    "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|>"
+    ],
+    "bos_token": null,
+    "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 1010000,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}