diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..faad6762486a48386fdf9e47e2c7bd99439fc3e9
--- /dev/null
+++ b/README.md
@@ -0,0 +1,158 @@
+---
+language:
+- en
+library_name: transformers
+tags:
+- stepfun
+- MOE
+- pruning
+- compression
+license: other
+name: cerebras/Step-3.5-Flash-REAP-121B-A11B
+description: >
+ This model was obtained by uniformly pruning 40% of experts in Step-3.5-Flash using the REAP method.
+readme: >
+ https://huggingface.co/cerebras/Step-3.5-Flash-REAP-121B-A11B/main/README.md
+pipeline_tag: text-generation
+base_model:
+- stepfun-ai/Step-3.5-Flash
+---
+
+
+ š³ REAPš³ the Experts: Why Pruning Prevails for One-Shot MoE Compression
+ 
+
+
+# Step-3.5-Flash-REAP-121B-A11B
+
+## āØ Highlights
+
+Introducing **Step-3.5-Flash-REAP-121B-A11B**, a **memory-efficient compressed variant** of Step-3.5-Flash that maintains near-identical performance while being **40% lighter**.
+
+This model was created using **REAP (Router-weighted Expert Activation Pruning)**, a novel expert pruning method that selectively removes redundant experts while preserving the router's independent control over remaining experts. Key features include:
+
+- **Near-Lossless Performance**: Maintains almost identical accuracy on code generation, agentic coding, and function calling tasks compared to the full 196B model
+- **40% Memory Reduction**: Compressed from 196B to 121B parameters, significantly lowering deployment costs and memory requirements
+- **Preserved Capabilities**: Retains all core functionalities including code generation, math & reasoning and tool calling.
+- **Drop-in Compatibility**: Works with vanilla vLLM - no source modifications or custom patches required
+- **Optimized for Real-World Use**: Particularly effective for resource-constrained environments, local deployments, and academic research
+---
+## š Model Overview
+
+**Step-3.5-Flash-REAP-121B-A11B** has the following specifications:
+
+- **Base Model**: Step-3.5-Flash
+- **Compression Method**: REAP (Router-weighted Expert Activation Pruning)
+- **Compression Ratio**: 40% expert pruning
+- **Type**: Sparse Mixture-of-Experts (SMoE) Causal Language Model
+- **Number of Parameters**: 121B total, 11B activated per token
+- **Number of Layers**: 45
+- **Number of Attention Heads**: 64
+- **Number of Experts**: 173 (uniformly pruned from 288)
+- **Number of Activated Experts**: 8 per token
+- **Context Length**: 262,144 tokens
+- **License**: Apache 2.0
+
+---
+
+## š Evaluations
+
+
+
+---
+
+## š Deployment
+
+You can deploy the model directly using the **latest vLLM** (that supports Step-3.5-Flash), no source modifications or custom patches required.
+
+```bash
+vllm serve cerebras/Step-3.5-Flash-REAP-121B-A11B \
+ --tensor-parallel-size 8 \
+ --tool-call-parser step3p5 \
+ --reasoning-parser step3p5 \
+ --trust-remote-code \
+ --enable_expert_parallel \
+ --disable-cascade-attn \
+ --enable-auto-tool-choice
+```
+
+If you encounter insufficient memory when running this model, you might need to set a lower value for `--max-num-seqs` flag (e.g. set to 64). For more information, refer to the [official vLLM deployment guide](https://huggingface.co/stepfun-ai/Step-3.5-Flash#61-vllm).
+
+## š§© Model Creation
+
+This checkpoint was created by applying the **REAP (Router-weighted Expert Activation Pruning)** method uniformly across all Mixture-of-Experts (MoE) blocks of **Step-3.5-Flash**, with a **40% pruning rate**.
+
+### How REAP Works
+
+REAP selects experts to prune based on a novel **saliency criterion** that considers both:
+- **Router gate values**: How frequently and strongly the router activates each expert
+- **Expert activation norms**: The magnitude of each expert's output contributions
+
+This dual consideration ensures that experts contributing minimally to the layer's output are pruned, while preserving those that play critical roles in the model's computations.
+
+### Key Advantages
+
+- **One-Shot Compression**: No fine-tuning required after pruning - the model is immediately ready for deployment
+- **Preserved Router Control**: Unlike expert merging methods, REAP maintains the router's independent, input-dependent control over remaining experts, avoiding "functional subspace collapse"
+- **Generative Task Superiority**: REAP significantly outperforms expert merging approaches on generative benchmarks (code generation, creative writing, mathematical reasoning) while maintaining competitive performance on discriminative tasks
+
+š For more details, refer to the following resources:
+
+- [š§¾ arXiv Preprint](https://arxiv.org/abs/2510.13999)
+- [š§¾ REAP Blog](https://www.cerebras.ai/blog/reap)
+- [š» REAP Codebase (GitHub)](https://github.com/CerebrasResearch/reap)
+
+---
+
+## āļø License
+
+This model is derived from
+**[`stepfun-ai/Step-3.5-Flash`](https://huggingface.co/stepfun-ai/Step-3.5-Flash)**
+and distributed under the **Apache 2.0 license**.
+
+---
+
+## š§¾ Citation
+
+If you use this checkpoint, please cite the REAP paper:
+
+```bibtex
+@article{lasby-reap,
+ title={REAP the Experts: Why Pruning Prevails for One-Shot MoE compression},
+ author={Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
+ journal={arXiv preprint arXiv:2510.13999},
+ year={2025}
+}
+```
\ No newline at end of file
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000000000000000000000000000000000000..c09ea497dada6fe2c1f94067b4762dcba193c0f0
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,80 @@
+{% macro render_content(content) %}{% if content is none %}{{- '' }}{% elif content is string %}{{- content }}{% elif content is mapping %}{{- content['value'] if 'value' in content else content['text'] }}{% elif content is iterable %}{% for item in content %}{% if item.type == 'text' %}{{- item['value'] if 'value' in item else item['text'] }}{% elif item.type == 'image' %}{% endif %}{% endfor %}{% endif %}{% endmacro %}
+{{bos_token}}{%- if tools %}
+ {{- '<|im_start|>system\n' }}
+ {%- if messages[0].role == 'system' %}
+ {{- render_content(messages[0].content) + '\n\n' }}
+ {%- endif %}
+ {{- "# Tools\n\nYou have access to the following functions in JSONSchema format:\n\n" }}
+ {%- for tool in tools %}
+ {{- "\n" }}
+ {{- tool | tojson(ensure_ascii=False) }}
+ {%- endfor %}
+ {{- "\n\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner \n...\n block must be nested within \n...\n XML tags\n- Required parameters MUST be specified\n<|im_end|>\n" }}
+{%- else %}
+ {%- if messages[0].role == 'system' %}
+ {{- '<|im_start|>system\n' + render_content(messages[0].content) + '<|im_end|>\n' }}
+ {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+ {%- set index = (messages|length - 1) - loop.index0 %}
+ {%- if ns.multi_step_tool and message.role == "user" and render_content(message.content) is string and not(render_content(message.content).startswith('') and render_content(message.content).endswith('')) %}
+ {%- set ns.multi_step_tool = false %}
+ {%- set ns.last_query_index = index %}
+ {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+ {%- set content = render_content(message.content) %}
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+ {%- set role_name = 'observation' if (message.role == "system" and not loop.first and message.name == 'observation') else message.role %}
+ {{- '<|im_start|>' + role_name + '\n' + content + '<|im_end|>' + '\n' }}
+ {%- elif message.role == "assistant" %}
+ {%- if message.reasoning_content is string %}
+ {%- set reasoning_content = render_content(message.reasoning_content) %}
+ {%- else %}
+ {%- if '' in content %}
+ {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %}
+ {%- set content = content.split('')[-1].lstrip('\n') %}
+ {%- else %}
+ {%- set reasoning_content = '' %}
+ {%- endif %}
+ {%- endif %}
+ {%- if loop.index0 > ns.last_query_index %}
+ {{- '<|im_start|>' + message.role + '\n\n' + reasoning_content + '\n\n' + content }}
+ {%- else %}
+ {{- '<|im_start|>' + message.role + '\n' + content }}
+ {%- endif %}
+ {%- if message.tool_calls %}
+ {%- for tool_call in message.tool_calls %}
+ {%- if tool_call.function is defined %}
+ {%- set tool_call = tool_call.function %}
+ {%- endif %}
+ {{- '\n\n' }}
+ {%- if tool_call.arguments is defined %}
+ {%- set arguments = tool_call.arguments %}
+ {%- for args_name, args_value in arguments|items %}
+ {{- '\n' }}
+ {%- set args_value = args_value | tojson(ensure_ascii=False) | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+ {{- args_value }}
+ {{- '\n\n' }}
+ {%- endfor %}
+ {%- endif %}
+ {{- '\n' }}
+ {%- endfor %}
+ {%- endif %}
+ {{- '<|im_end|>\n' }}
+ {%- elif message.role == "tool" %}
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+ {{- '<|im_start|>tool_response\n' }}
+ {%- endif %}
+ {{- '' }}
+ {{- content }}
+ {{- '' }}
+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+ {{- '<|im_end|>\n' }}
+ {%- endif %}
+ {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+ {{- '<|im_start|>assistant\n\n' }}
+{%- endif %}
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0b177329a66f95a501806c33321f74e755243df0
--- /dev/null
+++ b/config.json
@@ -0,0 +1,312 @@
+{
+ "architectures": [
+ "Step3p5ForCausalLM"
+ ],
+ "model_type": "step3p5",
+ "auto_map": {
+ "AutoConfig": "configuration_step3p5.Step3p5Config",
+ "AutoModelForCausalLM": "modeling_step3p5.Step3p5ForCausalLM"
+ },
+ "rope_scaling": {
+ "rope_type": "llama3",
+ "factor": 2.0,
+ "original_max_position_embeddings": 131072,
+ "low_freq_factor": 1.0,
+ "high_freq_factor": 32.0
+ },
+ "yarn_only_types": ["full_attention"],
+ "hidden_size": 4096,
+ "intermediate_size": 11264,
+ "num_hidden_layers": 45,
+ "max_seq_len": 262144,
+ "vocab_size": 128896,
+ "torch_dtype": "bfloat16",
+ "use_qk_norm": true,
+ "moe_layers_enum": "3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44",
+ "num_attention_heads": 64,
+ "num_attention_groups": 8,
+ "head_dim": 128,
+ "use_moe": true,
+ "moe_num_experts": 173,
+ "moe_top_k": 8,
+ "moe_intermediate_size": 1280,
+ "share_expert_dim": 1280,
+ "moe_layer_offset": 0,
+ "moe_every_n_layer": 1,
+ "norm_expert_weight": true,
+ "moe_router_activation": "sigmoid",
+ "moe_router_scaling_factor": 3.0,
+ "att_impl_type": "GQA",
+ "rope_theta": [
+ 5000000.0,
+ 10000.0,
+ 10000.0,
+ 10000.0,
+ 5000000.0,
+ 10000.0,
+ 10000.0,
+ 10000.0,
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+ 10000.0,
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+ 10000.0,
+ 5000000.0,
+ 10000.0,
+ 10000.0,
+ 10000.0,
+ 5000000.0,
+ 10000.0,
+ 10000.0,
+ 10000.0,
+ 5000000.0,
+ 10000.0,
+ 10000.0,
+ 10000.0,
+ 5000000.0,
+ 10000.0,
+ 10000.0,
+ 10000.0
+ ],
+ "use_head_wise_attn_gate": true,
+ "sliding_window": 512,
+ "use_moe_router_bias": true,
+ "need_fp32_gate": true,
+ "sink": false,
+ "layer_types": [
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "full_attention",
+ "sliding_attention",
+ "sliding_attention",
+ "sliding_attention"
+ ],
+ "use_rope_layers": [],
+ "num_nextn_predict_layers": 3,
+ "partial_rotary_factors": [
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0,
+ 0.5,
+ 1.0,
+ 1.0,
+ 1.0
+ ],
+ "eos_token_id": [
+ 1,
+ 2,
+ 128007
+ ],
+ "bos_token_id": 0,
+ "attention_other_setting": {
+ "attention_type": "sliding_attention",
+ "num_attention_heads": 96,
+ "num_attention_groups": 8,
+ "head_dim": 128,
+ "true_head_dim": 128
+ },
+ "swiglu_limits": [
+ 0.0,
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+ 7,
+ 7,
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+ 0.0
+ ],
+ "swiglu_limits_shared": [
+ 0.0,
+ 0.0,
+ 0.0,
+ 0.0,
+ 0.0,
+ 0.0,
+ 0.0,
+ 0.0,
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+ 16,
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+ ],
+ "zero_centered": true,
+ "max_position_embeddings": 262144
+}
\ No newline at end of file
diff --git a/configuration_step3p5.py b/configuration_step3p5.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab8d068ee83c25dc0fb2b0a2bd2ca76bdc9faac9
--- /dev/null
+++ b/configuration_step3p5.py
@@ -0,0 +1,59 @@
+from typing import Any, Optional, Union
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+
+class Step3p5Config(PretrainedConfig):
+ model_type = "step3p5"
+ architectures = ["Step3p5ForCausalLM"]
+
+ def __init__(
+ self,
+ hidden_size: int = 4096,
+ intermediate_size: int = 11264,
+ num_attention_heads: int = 64,
+ num_attention_groups: int = 8,
+ num_hidden_layers: int = 45,
+ max_seq_len: int = 128000,
+ vocab_size: int = 128815,
+ rms_norm_eps: float = 1e-5,
+ moe_intermediate_size: int = 1280,
+ moe_num_experts: int = 288,
+ moe_top_k: int = 8,
+ rope_theta: float = 10000,
+ rope_scaling: Optional[dict[str, Any]] = None,
+ max_position_embeddings: int = 128000,
+ share_expert_dims: int = 1280,
+ head_dim: int = 128,
+ norm_expert_weight: bool = True,
+ layer_types: list[str] = None,
+ sliding_window: Optional[int] = None,
+ moe_layers_enum: tuple[int] = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+ 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
+ 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
+ 35, 36, 37, 38, 39, 40, 41, 42, 43, 44),
+ **kwargs,
+ ) -> None:
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_attention_heads = num_attention_heads
+ self.num_attention_groups = num_attention_groups
+ self.num_hidden_layers = num_hidden_layers
+ self.max_seq_len = max_seq_len
+ self.vocab_size = vocab_size
+ self.rms_norm_eps = rms_norm_eps
+ self.moe_intermediate_size = moe_intermediate_size
+ self.moe_num_experts = moe_num_experts
+ self.moe_top_k = moe_top_k
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self.max_position_embeddings = max_position_embeddings
+ self.share_expert_dim = share_expert_dims
+ self.head_dim = head_dim
+ self.norm_expert_weight = norm_expert_weight
+ self.moe_layers_enum = moe_layers_enum
+ self.layer_types = layer_types
+ self.sliding_window = sliding_window
+ super().__init__(**kwargs)
+
diff --git a/model-00001-of-00064.safetensors b/model-00001-of-00064.safetensors
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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+version https://git-lfs.github.com/spec/v1
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+oid sha256:dd5a6bd5e8ff1b6a80e93bb9f72baad578ad1bd7fa1a7f49e139e7b40e509353
+size 3879857140
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new file mode 100644
index 0000000000000000000000000000000000000000..108d9620d7684effd702488b6678380e3044c995
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+oid sha256:3aba70cdd5f8012f350419c43b4112f9eec4c7c29f8fef873583370323897ad4
+size 3628073232
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+}
\ No newline at end of file
diff --git a/modeling_step3p5.py b/modeling_step3p5.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa8ebda29be86a33d508f690254db3add21ee886
--- /dev/null
+++ b/modeling_step3p5.py
@@ -0,0 +1,899 @@
+# Copyright 2025 The LLAMA4 and HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Callable, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+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 BaseModelOutputWithPast, ModelOutput
+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, can_return_tuple, logging
+
+from .configuration_step3p5 import Step3p5Config
+
+logger = logging.get_logger(__name__)
+
+__all__ = ["Step3p5Model", "Step3p5ForCausalLM"]
+
+class Step3p5RotaryEmbedding(nn.Module):
+
+ def __init__(self, config: Step3p5Config, device=None, layer_idx=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ self.layer_idx = layer_idx
+ if config.rope_parameters is not None:
+ self.rope_type = config.rope_parameters.get(
+ "rope_type", config.rope_parameters.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
+
+ partial_rotary_factors = getattr(config, "partial_rotary_factors",
+ None)
+ if partial_rotary_factors is not None:
+ config.partial_rotary_factor = partial_rotary_factors[
+ self.layer_idx]
+ else:
+ config.partial_rotary_factor = 1.0
+
+ self.rope_theta = config.rope_theta
+ if isinstance(config.rope_theta, list):
+ self.rope_theta = config.rope_theta.copy()
+ config.rope_theta = self.rope_theta[self.layer_idx]
+
+ 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
+ config.rope_theta = self.rope_theta
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ 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().to(x.device)
+
+ 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): # Force float32
+ 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)
+
+
+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.
+ 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.
+ """
+ rotary_dim = cos.shape[-1]
+ q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+ k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+ # Apply rotary embeddings on the first half or full tensor
+ q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
+ k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
+
+ # Concatenate back to full shape
+ q_embed = torch.cat([q_embed, q_pass], dim=-1)
+ k_embed = torch.cat([k_embed, k_pass], dim=-1)
+ return q_embed, k_embed
+
+
+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)
+
+
+# Adapted from transformers.models.llama.modeling_llama.eager_attention_forward -> llama4 doesn't cast attn weights to fp32
+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,
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+ # breakpoint()
+ 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)
+ 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
+
+@dataclass
+class Step3p5CausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ logits: torch.FloatTensor = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+class Step3p5MLP(nn.Module):
+
+ def __init__(self, config, intermediate_size=None, swiglu_limit=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["silu"]
+ self.limit = swiglu_limit
+
+ def forward(self, x):
+ up = self.up_proj(x)
+ gate = self.act_fn(self.gate_proj(x))
+ if self.limit is not None:
+ gate = gate.clamp(min=None, max=self.limit)
+ up = up.clamp(min=-self.limit, max=self.limit)
+
+ return self.down_proj(gate * up)
+
+
+def sigmoid_routing_function(gating_output: torch.Tensor, topk: int,
+ renormalize: bool):
+ gating_output = gating_output.float()
+ gate_prob = torch.sigmoid(gating_output)
+ gate_prob = gate_prob / gate_prob.sum(dim=-1, keepdim=True)
+ topk_prob, indices = torch.topk(gate_prob, k=topk, dim=1)
+ expert_topk_weight = topk_prob
+ if renormalize:
+ expert_topk_weight = expert_topk_weight / torch.sum(
+ expert_topk_weight, dim=-1, keepdim=True)
+ return expert_topk_weight, indices
+
+
+def softmax_routing_function(gating_output: torch.Tensor, top_k: int,
+ renormalize: bool):
+ gating_output = gating_output.float()
+ gate_prob = torch.softmax(gating_output, dim=-1)
+ gate_prob = gate_prob / gate_prob.sum(dim=-1, keepdim=True)
+ topk_prob, indices = torch.topk(gate_prob, k=top_k, dim=1)
+ expert_topk_weight = topk_prob
+ if renormalize:
+ expert_topk_weight = expert_topk_weight / torch.sum(
+ expert_topk_weight, dim=-1, keepdim=True)
+ return expert_topk_weight, indices.to(torch.int32)
+
+
+class MoELinear(nn.Module):
+
+ def __init__(self, num_experts, in_features, out_features):
+ super().__init__()
+ self.num_experts = num_experts
+ self.in_features = in_features
+ self.out_features = out_features
+ self.weight = nn.Parameter(
+ torch.empty(num_experts, out_features, in_features))
+
+ def forward(self, x, expert_id):
+ x = F.linear(x.float(), self.weight[expert_id].float())
+ return x
+
+
+class Step3p5MoEMLP(nn.Module):
+
+ def __init__(self, config, swiglu_limit=None):
+ super().__init__()
+ self.num_experts = config.moe_num_experts
+ self.top_k = config.moe_top_k
+ self.hidden_size = config.hidden_size
+ self.moe_intermediate_size = config.moe_intermediate_size
+
+ self.use_moe_router_bias = config.use_moe_router_bias
+ if self.use_moe_router_bias:
+ self.router_bias = nn.Parameter(torch.zeros(config.moe_num_experts,
+ dtype=torch.float32),
+ requires_grad=False)
+ self.custom_routing_function = self.router_bias_func
+ elif config.moe_router_activation == "sigmoid":
+ self.custom_routing_function = sigmoid_routing_function
+ else:
+ self.custom_routing_function = None
+ self.need_fp32_gate = config.need_fp32_gate
+ self.routed_scaling_factor = getattr(config,
+ "moe_router_scaling_factor", 1.0)
+
+ # gating
+ self.gate = nn.Linear(self.hidden_size, self.num_experts, bias=False)
+
+ self.act_fn = ACT2FN["silu"]
+ self.limit = swiglu_limit
+
+ self.up_proj = MoELinear(self.num_experts, self.hidden_size,
+ self.moe_intermediate_size)
+ self.gate_proj = MoELinear(self.num_experts, self.hidden_size,
+ self.moe_intermediate_size)
+ self.down_proj = MoELinear(self.num_experts,
+ self.moe_intermediate_size,
+ self.hidden_size)
+
+ def router_bias_func(self, gating_output: torch.Tensor, topk: int,
+ renormalize: bool):
+ gate_prob = torch.sigmoid(gating_output.float())
+ gate_prob_with_bias = gate_prob + self.router_bias.unsqueeze(0)
+ _, indices = torch.topk(gate_prob_with_bias, k=topk, dim=1)
+ topk_prob = torch.gather(gate_prob, 1, indices)
+ expert_topk_weight = topk_prob
+ if renormalize:
+ expert_topk_weight = expert_topk_weight / (
+ torch.sum(expert_topk_weight, dim=-1, keepdim=True) + 1e-20)
+ return expert_topk_weight, indices
+
+ def get_expert_output(self, inputs: torch.Tensor, expert_id):
+ #if self.limit is None:
+ up = self.up_proj(inputs, expert_id)
+ gate = self.act_fn(self.gate_proj(inputs, expert_id))
+ if self.limit is not None:
+ gate = gate.clamp(min=None, max=self.limit)
+ up = up.clamp(min=-self.limit, max=self.limit)
+
+ return self.down_proj(gate * up, expert_id)
+
+ def forward(self, hidden_states):
+ """ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ if self.need_fp32_gate:
+ router_logits = torch.matmul(hidden_states.to(torch.float32), self.gate.weight.t().to(torch.float32))
+ else:
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.gate(hidden_states)
+
+ if self.custom_routing_function:
+ routing_weights, selected_experts = self.custom_routing_function(
+ router_logits, self.top_k, renormalize=True)
+ else:
+ routing_weights = F.softmax(router_logits,
+ dim=1,
+ dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights,
+ self.top_k,
+ dim=-1)
+
+ routing_weights = routing_weights * self.routed_scaling_factor
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim),
+ dtype=hidden_states.dtype,
+ device=hidden_states.device)
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be sollicitated
+ expert_mask = torch.nn.functional.one_hot(
+ selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ # Loop over all available experts in the model and perform the computation on each expert
+ for expert_idx in range(self.num_experts):
+ idx, top_x = torch.where(expert_mask[expert_idx])
+
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+ current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+ current_hidden_states = (
+ self.get_expert_output(current_state, expert_idx) *
+ routing_weights[top_x, idx, None])
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(
+ 0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(
+ batch_size, sequence_length, hidden_dim)
+ return final_hidden_states
+
+
+class Step3p5RMSNorm(nn.Module):
+
+ def __init__(
+ self,
+ hidden_size: int,
+ eps: float = 1e-5,
+ ) -> None:
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ dtype = x.dtype
+ x = x.float()
+ variance = x.pow(2).mean(dim=-1, keepdim=True)
+ normed = x * torch.rsqrt(variance + self.variance_epsilon)
+ normed = normed * (self.weight.float() + 1)
+ return normed.to(dtype)
+class Step3p5Attention(nn.Module):
+
+ def __init__(self, config: Step3p5Config, layer_idx):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.num_attention_heads = config.num_attention_heads
+ self.num_key_value_heads = config.num_attention_groups
+
+ layer_types = getattr(config, "layer_types", [])
+ if layer_types:
+ enable_sliding_window = layer_types[
+ self.layer_idx] == "sliding_attention"
+ else:
+ enable_sliding_window = self.layer_idx % 2 == 0
+
+ if hasattr(config, "yarn_only_types") and layer_types[
+ self.layer_idx] not in config.yarn_only_types:
+ config.rope_parameters = None
+ else:
+ config.rope_parameters = getattr(config, "rope_scaling", None)
+
+ self.sliding_window = config.sliding_window
+ if enable_sliding_window:
+ self.num_attention_heads = config.attention_other_setting[
+ "num_attention_heads"]
+ self.num_key_value_heads = config.attention_other_setting[
+ "num_attention_groups"]
+
+ if self.sliding_window is not None and enable_sliding_window:
+ self.sliding_window = (self.sliding_window)
+ else:
+ self.sliding_window = None
+ self.head_dim = getattr(config, "head_dim",
+ config.hidden_size // self.num_attention_heads)
+ self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
+
+ self.rotary_emb = Step3p5RotaryEmbedding(config, layer_idx=layer_idx)
+
+ self.q_size = self.num_attention_heads * self.head_dim
+ self.kv_size = self.num_key_value_heads * self.head_dim
+ self.scaling = self.head_dim**-0.5
+
+ self.q_proj = nn.Linear(config.hidden_size, self.q_size, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, self.kv_size, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, self.kv_size, bias=False)
+ self.o_proj = nn.Linear(self.q_size, config.hidden_size, bias=False)
+ self.q_norm = Step3p5RMSNorm(self.head_dim,
+ eps=config.rms_norm_eps)
+ self.k_norm = Step3p5RMSNorm(self.head_dim,
+ eps=config.rms_norm_eps)
+
+ self.use_head_wise_attn_gate = config.use_head_wise_attn_gate
+ if self.use_head_wise_attn_gate:
+ self.g_proj = nn.Linear(config.hidden_size,
+ self.num_attention_heads,
+ bias=False)
+
+ self.use_rope = True
+ use_rope_layers = getattr(config, "use_rope_layers", None)
+ if use_rope_layers:
+ self.use_rope = use_rope_layers[self.layer_idx]
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ past_key_value: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+ Optional[Tuple[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)
+ if self.use_head_wise_attn_gate:
+ gate_states = self.g_proj(hidden_states)
+ cos, sin = self.rotary_emb(hidden_states, position_ids)
+
+ # cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(
+ query_states, key_states, cos, sin)
+
+ # query_states, key_states = apply_rotary_pos_emb(query_norm_states, key_norm_states, cos, sin)
+ if past_key_value is not None:
+ # sin and cos are specific to RoPE models; position_ids needed for the static cache
+ cache_kwargs = {
+ "sin": sin,
+ "cos": cos,
+ "cache_position": cache_position
+ }
+ key_states, value_states = past_key_value.update(
+ key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ # TODO: considering FP8ļ¼
+ # RuntimeError: Expected attn_mask dtype to be bool or float or to match query dtype,
+ # but got attn_mask.dtype: long int and query.dtype: c10::BFloat16 instead.
+ 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, # main diff with Llama
+ **kwargs,
+ )
+ attn_output = attn_output.reshape(*input_shape, -1)
+ if self.use_head_wise_attn_gate:
+ output = attn_output.view(
+ *attn_output.shape[:-1], self.num_attention_heads,
+ self.head_dim) * gate_states.unsqueeze(-1).sigmoid()
+ attn_output = output.view(*attn_output.shape)
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class Step3p5DecoderLayer(GradientCheckpointingLayer):
+
+ def __init__(self, config, layer_idx):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.layer_idx = layer_idx
+ self.self_attn = Step3p5Attention(config, layer_idx)
+ self.attention_type = config.layer_types[layer_idx]
+
+ moe_layers_enum = getattr(config, "moe_layers_enum", None)
+ if moe_layers_enum is not None:
+ moe_layers_idx = [
+ int(i) for i in moe_layers_enum.strip().split(',')
+ ]
+ else:
+ moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+ self.is_moe_layer = layer_idx in moe_layers_idx
+ self.use_moe = False
+
+ if config.swiglu_limits_shared and config.swiglu_limits_shared[
+ layer_idx] is not None and config.swiglu_limits_shared[
+ layer_idx] != 0:
+ swiglu_limit_shared = config.swiglu_limits_shared[layer_idx]
+ else:
+ swiglu_limit_shared = None
+ if config.swiglu_limits and config.swiglu_limits[
+ layer_idx] is not None and config.swiglu_limits[layer_idx] != 0:
+ swiglu_limit = config.swiglu_limits[layer_idx]
+ else:
+ swiglu_limit = None
+ if self.is_moe_layer:
+ self.moe = Step3p5MoEMLP(config, swiglu_limit=swiglu_limit) #
+ self.share_expert = Step3p5MLP(
+ config,
+ intermediate_size=config.share_expert_dim,
+ swiglu_limit=swiglu_limit_shared)
+ self.use_moe = True
+ else:
+ self.mlp = Step3p5MLP(config,
+ intermediate_size=config.intermediate_size,
+ swiglu_limit=swiglu_limit_shared)
+
+ self.input_layernorm = Step3p5RMSNorm(
+ config.hidden_size,
+ eps=config.rms_norm_eps)
+ self.post_attention_layernorm = Step3p5RMSNorm(
+ config.hidden_size,
+ eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[tuple[torch.Tensor]] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> torch.FloatTensor:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ if self.use_moe:
+ share_output = self.share_expert(hidden_states)
+ moe_output = self.moe(hidden_states)
+ ffn_output = moe_output + share_output
+ else:
+ ffn_output = self.mlp(hidden_states)
+ if isinstance(ffn_output, tuple):
+ hidden_states, _ = ffn_output
+ else:
+ hidden_states = ffn_output
+
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+class Step3p5PreTrainedModel(PreTrainedModel):
+ # Link this model family to its configuration class so PreTrainedModel.from_pretrained
+ # can load the config instead of failing with a NoneType error.
+ config_class = Step3p5Config
+ supports_gradient_checkpointing = True
+ _skip_keys_device_placement = ["past_key_values"]
+ _keys_to_ignore_on_load_unexpected = [
+ r"model\.layers\.45\.*",
+ r"model\.layers\.46\.*",
+ r"model\.layers\.47\.*"
+ ]
+ _supports_flash_attn = False
+ _supports_sdpa = True
+ _supports_flex_attn = True
+ _supports_static_cache = True
+ _supports_attention_backend = True
+
+
+class Step3p5Model(Step3p5PreTrainedModel, GenerationMixin):
+ _no_split_modules = ["Step3p5DecoderLayer"]
+ base_model_prefix = "model"
+ _tied_weights_keys = ["lm_head.weight"]
+ config: Step3p5Config
+ def __init__(self, config: Step3p5Config):
+ 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([
+ Step3p5DecoderLayer(config, layer_idx)
+ for layer_idx in range(config.num_hidden_layers)
+ ])
+ self.norm = Step3p5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.gradient_checkpointing = False
+ self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self, input_ids):
+ return self.embed_tokens(input_ids)
+
+ @can_return_tuple
+ def forward(
+ self,
+ input_ids: 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,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (output_hidden_states
+ if output_hidden_states is not None else
+ self.config.output_hidden_states)
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError(
+ "You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(
+ input_ids.to(self.embed_tokens.weight.device))
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ 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)
+
+ hidden_states = inputs_embeds
+
+ # It may already have been prepared by e.g. `generate`
+ if not isinstance(causal_mask_mapping := attention_mask, dict):
+ # Prepare mask arguments
+ mask_kwargs = {
+ "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,
+ }
+ # Create the masks
+ causal_mask_mapping = {
+ "full_attention": create_causal_mask(**mask_kwargs),
+ }
+
+ # The sliding window alternating layers are not always activated depending on the config
+ if self.has_sliding_layers:
+ causal_mask_mapping[
+ "sliding_attention"] = create_sliding_window_causal_mask(
+ **mask_kwargs)
+
+ # # create position embeddings to be shared across the decoder layers
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ for decoder_layer in self.layers[:self.config.num_hidden_layers]:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states, )
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask_mapping[
+ decoder_layer.attention_type],
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs
+
+ hidden_states = self.norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class Step3p5ForCausalLM(Step3p5PreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ config: Step3p5Config
+
+ def __init__(self, config: Step3p5Config):
+ super().__init__(config)
+ self.model = Step3p5Model(config)
+ self.lm_head = nn.Linear(config.hidden_size,
+ config.vocab_size,
+ bias=False)
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self):
+ return self.model.get_output_embeddings()
+
+ def set_output_embeddings(self, new_embeddings):
+ self.model.set_output_embeddings(new_embeddings)
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ num_patches=None,
+ patch_pixel_values=None,
+ patch_newline_mask=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_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, Step3p5CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ Example:
+ ```python
+ >>> from transformers import AutoTokenizer, Llama4ForCausalLM
+ >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (output_hidden_states
+ if output_hidden_states is not None else
+ self.config.output_hidden_states)
+ # breakpoint()
+ outputs = self.model(
+ input_ids=input_ids,
+ num_patches=num_patches,
+ patch_pixel_values=patch_pixel_values,
+ patch_newline_mask=patch_newline_mask,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = outputs.last_hidden_state
+ logits = self.lm_head(hidden_states)
+
+ return Step3p5CausalLMOutputWithPast(logits=logits, )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+ def _fix_state_dict_key_on_load(self, key: str) -> tuple[str, bool]:
+ if key.startswith("language_model."):
+ return key[len("language_model."):], True
+
+ return key, False
\ No newline at end of file
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..71e14b354c9d1319df9ac55d385c33e74c0113bd
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+ "bos_token": {
+ "content": "<ļ½begināofāsentenceļ½>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "<|im_end|>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "<ļ½endāofāsentenceļ½>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..a57a955520240603eca1ffedcf3f8d27a1d678ec
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b564c620eb77fa11d0926011c2202347d6cfc358d79724ee04ae7007e13636f0
+size 9976972
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c29f8000f644d30a535f738cb1efba4e9afff26c
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,6562 @@
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+ "add_eos_token": false,
+ "add_prefix_space": null,
+ "added_tokens_decoder": {
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+ "content": "<ļ½begināofāsentenceļ½>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
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+ "single_word": false,
+ "special": true
+ },
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+ },
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+ "128814": {
+ "content": "<ļ½toolāsepļ½>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ }
+ },
+ "bos_token": "<ļ½begināofāsentenceļ½>",
+ "clean_up_tokenization_spaces": false,
+ "eos_token": "<|im_end|>",
+ "extra_special_tokens": {},
+ "legacy": true,
+ "model_max_length": 131072,
+ "pad_token": "<ļ½endāofāsentenceļ½>",
+ "sp_model_kwargs": {},
+ "tokenizer_class": "LlamaTokenizerFast",
+ "unk_token": null,
+ "use_default_system_prompt": false
+}