Duplicate from arcee-ai/Trinity-Large-Preview

Co-authored-by: Varun Singh <sapiosaturn@users.noreply.huggingface.co>

.eval_results/mmlu_pro.yaml

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+- dataset:
+    id: TIGER-Lab/MMLU-Pro
+    task_id: mmlu_pro
+  value: 75.2
+  date: '2026-02-03'
+  source:
+    url: https://huggingface.co/arcee-ai/Trinity-Large-Preview
+    name: Model Card

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

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+OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
+
+By exercising rights granted to you under this agreement, you accept and agree
+to its terms.
+
+As used in this agreement, "Model Materials" means the materials provided to
+you under this agreement, consisting of: (1) one or more machine learning
+models (including architecture and parameters); and (2) all related artifacts
+(including associated data, documentation and software) that are provided to
+you hereunder.
+
+Subject to your compliance with this agreement, permission is hereby granted,
+free of charge, to deal in the Model Materials without restriction, including
+under all copyright, patent, database, and trade secret rights included or
+embodied therein.
+
+If you distribute any portion of the Model Materials, you shall retain in your
+distribution (1) a copy of this agreement, and (2) all copyright notices and
+other notices of origin included in the Model Materials that are applicable to
+your distribution.
+
+If you file, maintain, or voluntarily participate in a lawsuit against any
+person or entity asserting that the Model Materials directly or indirectly
+infringe any patent or copyright, then all rights and grants made to you
+hereunder are terminated, unless that lawsuit was in response to a
+corresponding lawsuit first brought against you.
+
+This agreement does not impose any restrictions or obligations with respect to
+any use, modification, or sharing of any outputs generated by using the Model
+Materials.
+
+THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE
+ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL
+TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW.
+
+YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY
+APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION
+ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL
+MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS
+REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE
+DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR
+ANYTHING INCORPORATED OR EMBODIED THEREIN.
+
+IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE
+USE THEREOF OR OTHER DEALINGS THEREIN.

README.md

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+---
+license: other
+language:
+- en
+- es
+- fr
+- de
+- it
+- pt
+- ru
+- ar
+- hi
+- ko
+- zh
+library_name: transformers
+base_model:
+- arcee-ai/Trinity-Large-Base
+arxiv:
+- 2602.17004
+license_link: LICENSE
+license_name: openmdw-1.1
+---
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <picture>
+    <img
+      src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
+      alt="Arcee Trinity Large"
+      style="max-width: 100%; height: auto;"
+    >
+  </picture>
+</div>
+<hr>
+
+# Trinity-Large-Preview
+
+## Introduction
+
+Trinity-Large-Preview is a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. It is the largest model in Arcee AI's Trinity family, trained on more than 17 trillion tokens and delivering frontier-level performance with strong long-context comprehension.
+Trinity-Large-Preview is a lightly post-trained model based on Trinity-Large-Base.
+
+Try it at [chat.arcee.ai](http://chat.arcee.ai/)
+
+More details on the training of Trinity Large are available in the [technical report](https://github.com/arcee-ai/trinity-large-tech-report/).
+
+
+## Model Variants
+
+The Trinity Large family consists of three checkpoints from the same training run:
+
+- **Trinity-Large-Preview** (this release): Lightly post-trained, chat-ready model undergoing active RL
+- **[Trinity-Large-Thinking](https://huggingface.co/arcee-ai/Trinity-Large-Thinking)**: Reasoning-optimized, agentic post-training with extended chain-of-thought
+- **[Trinity-Large-TrueBase](https://huggingface.co/arcee-ai/Trinity-Large-TrueBase)**: 10T-token pre-anneal pretraining checkpoint
+- **[Trinity-Large-Base](https://huggingface.co/arcee-ai/Trinity-Large-Base)**: Full 17T-token pretrained foundation model with mid-training anneals
+
+## Architecture
+
+Trinity-Large-Preview uses a sparse MoE configuration designed to maximize efficiency while maintaining large-scale capacity.
+
+| Hyperparameter | Value |
+|:---|:---:|
+| Total parameters | ~398B |
+| Active parameters per token | ~13B |
+| Experts | 256 (1 shared) |
+| Active experts | 4 |
+| Routing strategy | 4-of-256 (1.56% sparsity) |
+| Dense layers | 6 |
+| Pretraining context length | 8,192 |
+| Context length after extension | 512k |
+| Architecture | Sparse MoE (AfmoeForCausalLM) |
+
+## Benchmarks
+
+| Benchmark | Llama 4 Maverick | Trinity-Large Preview |
+|-----------|------------------|----------------------|
+| MMLU | 85.5 | 87.2 |
+| MMLU-Pro | 80.5 | 75.2 |
+| GPQA-Diamond | 69.8 | 63.3 |
+| AIME 2025 | 19.3 | 24.0 |
+
+## Training Configuration
+
+### Pretraining
+
+- Training tokens: 17 trillion
+- Data partner: [Datology](https://www.datologyai.com/)
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
+  </picture>
+</div>
+
+## Posttraining
+- This checkpoint was instruction tuned on 20B tokens.
+
+### Infrastructure
+
+- Hardware: 2,048 NVIDIA B300 GPUs
+- Parallelism: HSDP + Expert Parallelism
+- Compute partner: [Prime Intellect](https://www.primeintellect.ai/)
+
+
+<div align="center">
+  <picture>
+      <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61e020e4a343274bb132e138/H2mcdPRWtl4iKLd-OYYBc.jpeg" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Prime Intellect">
+  </picture>
+</div>
+
+## Usage
+
+### Running our model
+
+- [Transformers](https://huggingface.co/arcee-ai/Trinity-Large-Preview#transformers)
+- [VLLM](https://huggingface.co/arcee-ai/Trinity-Large-Preview#vllm)
+- [llama.cpp](https://huggingface.co/arcee-ai/Trinity-Large-Preview#llamacpp)
+- [LM Studio](https://huggingface.co/arcee-ai/Trinity-Large-Preview#lm-studio)
+- [API](https://huggingface.co/arcee-ai/Trinity-Large-Preview#api)
+
+
+### Transformers
+
+Use the `main` transformers branch or pass `trust_remote_code=True` with a released version.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "arcee-ai/Trinity-Large-Preview"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.8,
+    top_k=50,
+    top_p=0.8
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### VLLM
+
+Supported in VLLM release 0.11.1+
+
+```bash
+vllm serve arcee-ai/Trinity-Large-Preview \
+  --dtype bfloat16 \
+  --enable-auto-tool-choice \
+  --tool-call-parser hermes
+```
+
+### llama.cpp
+
+Supported in llama.cpp release b7061+
+
+```bash
+llama-server -hf arcee-ai/Trinity-Large-Preview-GGUF:q4_k_m
+```
+
+### LM Studio
+
+Supported in the latest LM Studio runtime. Search for `arcee-ai/Trinity-Large-Preview-GGUF` in Model Search.
+
+### API
+
+Available on OpenRouter:
+
+```bash
+curl -X POST "https://openrouter.ai/v1/chat/completions" \
+  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "arcee-ai/trinity-large-preview",
+    "messages": [
+      {
+        "role": "user",
+        "content": "What are some fun things to do in New York?"
+      }
+    ]
+  }'
+```
+
+
+## License
+
+Trinity-Large-Preview is released under the OpenMDW License, version 1.1 (OpenMDW-1.1).
+
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{singh2026arceetrinity,
+  title        = {Arcee Trinity Large Technical Report},
+  author       = {Varun Singh and Lucas Krauss and Sami Jaghouar and Matej Sirovatka and Charles Goddard and Fares Obied and Jack Min Ong and Jannik Straube and Fern and Aria Harley and Conner Stewart and Colin Kealty and Maziyar Panahi and Simon Kirsten and Anushka Deshpande and Anneketh Vij and Arthur Bresnu and Pranav Veldurthi and Raghav Ravishankar and Hardik Bishnoi and DatologyAI Team and Arcee AI Team and Prime Intellect Team and Mark McQuade and Johannes Hagemann and Lucas Atkins},
+  year         = {2026},
+  eprint       = {2602.17004},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.LG},
+  doi          = {10.48550/arXiv.2602.17004},
+  url          = {https://arxiv.org/abs/2602.17004}
+}

chat_template.jinja

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+{{ bos_token }}{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {% generation %}
+        {{- content}}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>' }}
+        {% endgeneration%}
+        {{- '\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "AfmoeForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_afmoe.AfmoeConfig",
+    "AutoModel": "modeling_afmoe.AfmoeModel",
+    "AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM"
+  },
+  "dtype": "bfloat16",
+  "global_attn_every_n_layers": 4,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "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",
+    "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"
+  ],
+  "load_balance_coeff": 0.00005,
+  "max_position_embeddings": 262144,
+  "model_type": "afmoe",
+  "moe_intermediate_size": 3072,
+  "mup_enabled": true,
+  "n_group": 1,
+  "num_attention_heads": 48,
+  "num_dense_layers": 6,
+  "num_expert_groups": 1,
+  "num_experts": 256,
+  "num_experts_per_tok": 4,
+  "num_hidden_layers": 60,
+  "num_key_value_heads": 8,
+  "num_limited_groups": 1,
+  "num_shared_experts": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "route_norm": true,
+  "route_scale": 2.448,
+  "score_func": "sigmoid",
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "topk_group": 1,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_grouped_mm": true,
+  "vocab_size": 200192
+}

configuration_afmoe.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.configuration_utils import layer_type_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+class AfmoeConfig(PretrainedConfig):
+    """
+    n_group (`int`, *optional*, defaults to 1):
+            Number of groups for routed experts.
+    topk_group (`int`, *optional*, defaults to 1):
+        Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+    """
+    model_type = "afmoe"
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        num_hidden_layers: int = 32,
+        vocab_size: int = 200192,
+        hidden_size: int = 2048,
+        intermediate_size: int = 6144,
+        moe_intermediate_size=1408,
+        num_dense_layers=1,
+        num_attention_heads=16,
+        num_key_value_heads=None,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=16384,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        num_experts=64,
+        num_experts_per_tok=6,
+        num_shared_experts=2,
+        num_expert_groups=1,
+        num_limited_groups=1,
+        score_func="sigmoid",
+        route_norm=True,
+        route_scale=1.0,
+        global_attn_every_n_layers=4,
+        sliding_window=1024,
+        mup_enabled=False,
+        layer_types=None,
+        attention_dropout: float = 0.0,
+        n_group: int = 1,
+        topk_group: int = 1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_dense_layers = num_dense_layers
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+
+
+        # MoE specific
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts = num_experts
+        self.num_shared_experts = num_shared_experts
+        self.num_expert_groups = num_expert_groups
+        self.num_limited_groups = num_limited_groups
+        self.score_func = score_func
+        self.route_norm = route_norm
+        self.route_scale = route_scale
+
+
+        # Attention specific
+        self.attention_dropout = attention_dropout
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.sliding_window = sliding_window
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention" if bool((i + 1) % global_attn_every_n_layers) else "full_attention" for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types)
+
+        # muP specific
+        self.mup_enabled = mup_enabled
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+
+
+        # Validate rope configs
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["AfmoeConfig"]

generation_config.json

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+  "pad_token_id": 12,
+  "transformers_version": "4.57.3",
+  "temperature": 0.8,
+  "top_p": 0.8
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special_tokens_map.json

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+  "pad_token": "<|pad|>",
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+  "chat_template": "{{ bos_token }}{%- 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' }}\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' }}\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 %}"
+}