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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,89 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ - es
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+ - fr
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+ - de
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+ - it
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+ - pt
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+ - ru
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+ - ar
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+ - hi
13
+ - ko
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+ - zh
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+ library_name: transformers
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+ ---
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+ <div align="center">
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+ <picture>
19
+ <img
20
+ src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
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+ alt="Arcee Trinity Nano"
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+ style="max-width: 100%; height: auto;"
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+ >
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+ </picture>
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+ </div>
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+
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+ # Trinity Nano Base Pre Anneal
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+
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+ Trinity-Nano-Base-Pre-Anneal is an Arcee AI 6B MoE model with 1B active parameters. It is the small-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
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+
31
+ This base model is a pre-anneal posttraining checkpoint captured at Adam LR: 0.002, Muon LR: 0.001 before starting learning rate decay on a high-quality data mix.
32
+ While this checkpoint was not exposed to the anneal phase mix containing high proportions of math and code content, it has been trained on significant amounts of such data.
33
+ This checkpoint is not suitable for chatting or general use without further finetuning and should be trained for your specific domain before use.
34
+
35
+ ***
36
+
37
+ Trinity-Nano-Base-Pre-Anneal is trained on 8.8T tokens gathered and curated through a key partnership with [Datology](https://www.datologyai.com/), building upon the excellent dataset we used on [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) with additional math and code.
38
+
39
+ Training was performed on a cluster of 512 H200 GPUs powered by [Prime Intellect](https://www.primeintellect.ai/) using HSDP parallelism.
40
+
41
+ More details, including key architecture decisions, can be found on our blog [here](https://www.arcee.ai/blog/the-trinity-manifesto)
42
+
43
+ ***
44
+
45
+ ## Model Details
46
+
47
+ * **Model Architecture:** AfmoeForCausalLM
48
+ * **Parameters:** 6B, 1B active
49
+ * **Experts:** 128 total, 8 active, 1 shared
50
+ * **Context length:** 4K
51
+ * **Learning rate during pretraining**:
52
+ * `adam_lr = 0.0002`
53
+ * `muon_lr = 0.001`
54
+ * **Training Tokens:** 8.8T
55
+ * **License:** [Apache 2.0](https://huggingface.co/arcee-ai/Trinity-Mini#license)
56
+
57
+ ***
58
+
59
+ <div align="center">
60
+ <picture>
61
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
62
+ </picture>
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+ </div>
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+
65
+ ## Try out our reasoning tune of our medium-sized Trinity Mini model
66
+
67
+ Trinity Mini is available today on openrouter:
68
+
69
+ https://openrouter.ai/arcee-ai/trinity-mini
70
+
71
+ ```
72
+ curl -X POST "https://openrouter.ai/v1/chat/completions" \
73
+ -H "Authorization: Bearer $OPENROUTER_API_KEY" \
74
+ -H "Content-Type: application/json" \
75
+ -d '{
76
+ "model": "arcee-ai/trinity-mini",
77
+ "messages": [
78
+ {
79
+ "role": "user",
80
+ "content": "What are some fun things to do in New York?"
81
+ }
82
+ ]
83
+ }'
84
+ ```
85
+
86
+
87
+ ## License
88
+
89
+ Trinity-Nano-Base-Pre-Anneal is released under the Apache-2.0 license.
config.json ADDED
@@ -0,0 +1,104 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
+ "architectures": [
3
+ "AfmoeForCausalLM"
4
+ ],
5
+ "attention_dropout": 0.0,
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_afmoe.AfmoeConfig",
8
+ "AutoModel": "modeling_afmoe.AfmoeModel",
9
+ "AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM"
10
+ },
11
+ "dtype": "bfloat16",
12
+ "global_attn_every_n_layers": 4,
13
+ "head_dim": 128,
14
+ "hidden_act": "silu",
15
+ "hidden_size": 1024,
16
+ "initializer_range": 0.02,
17
+ "intermediate_size": 3072,
18
+ "layer_types": [
19
+ "sliding_attention",
20
+ "sliding_attention",
21
+ "sliding_attention",
22
+ "full_attention",
23
+ "sliding_attention",
24
+ "sliding_attention",
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+ "sliding_attention",
26
+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "full_attention"
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+ ],
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+ "load_balance_coeff": 0.001,
77
+ "max_position_embeddings": 4096,
78
+ "model_type": "afmoe",
79
+ "moe_intermediate_size": 256,
80
+ "mup_enabled": true,
81
+ "n_group": 1,
82
+ "num_attention_heads": 8,
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+ "num_dense_layers": 2,
84
+ "num_expert_groups": 1,
85
+ "num_experts": 128,
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+ "num_experts_per_tok": 8,
87
+ "num_hidden_layers": 56,
88
+ "num_key_value_heads": 2,
89
+ "num_limited_groups": 1,
90
+ "num_shared_experts": 1,
91
+ "rms_norm_eps": 1e-05,
92
+ "rope_scaling": null,
93
+ "rope_theta": 10000,
94
+ "route_norm": true,
95
+ "route_scale": 2.826,
96
+ "score_func": "sigmoid",
97
+ "sliding_window": 2048,
98
+ "tie_word_embeddings": false,
99
+ "topk_group": 1,
100
+ "transformers_version": "4.57.3",
101
+ "use_cache": true,
102
+ "use_grouped_mm": true,
103
+ "vocab_size": 200192
104
+ }
configuration_afmoe.py ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ from transformers.configuration_utils import PretrainedConfig
16
+ from transformers.modeling_rope_utils import rope_config_validation
17
+ from transformers.configuration_utils import layer_type_validation
18
+ from transformers.utils import logging
19
+
20
+ logger = logging.get_logger(__name__)
21
+
22
+ class AfmoeConfig(PretrainedConfig):
23
+ """
24
+ n_group (`int`, *optional*, defaults to 1):
25
+ Number of groups for routed experts.
26
+ topk_group (`int`, *optional*, defaults to 1):
27
+ Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
28
+ """
29
+ model_type = "afmoe"
30
+ base_model_pp_plan = {
31
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
32
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
33
+ "norm": (["hidden_states"], ["hidden_states"]),
34
+ }
35
+
36
+ def __init__(
37
+ self,
38
+ num_hidden_layers: int = 32,
39
+ vocab_size: int = 200192,
40
+ hidden_size: int = 2048,
41
+ intermediate_size: int = 6144,
42
+ moe_intermediate_size=1408,
43
+ num_dense_layers=1,
44
+ num_attention_heads=16,
45
+ num_key_value_heads=None,
46
+ head_dim=128,
47
+ hidden_act="silu",
48
+ max_position_embeddings=16384,
49
+ initializer_range=0.02,
50
+ rms_norm_eps=1e-5,
51
+ use_cache=True,
52
+ tie_word_embeddings=False,
53
+ rope_theta=10000.0,
54
+ rope_scaling=None,
55
+ num_experts=64,
56
+ num_experts_per_tok=6,
57
+ num_shared_experts=2,
58
+ num_expert_groups=1,
59
+ num_limited_groups=1,
60
+ score_func="sigmoid",
61
+ route_norm=True,
62
+ route_scale=1.0,
63
+ global_attn_every_n_layers=4,
64
+ sliding_window=1024,
65
+ mup_enabled=False,
66
+ layer_types=None,
67
+ attention_dropout: float = 0.0,
68
+ n_group: int = 1,
69
+ topk_group: int = 1,
70
+ **kwargs,
71
+ ):
72
+ self.vocab_size = vocab_size
73
+ self.max_position_embeddings = max_position_embeddings
74
+ self.hidden_size = hidden_size
75
+ self.intermediate_size = intermediate_size
76
+ self.num_hidden_layers = num_hidden_layers
77
+ self.num_dense_layers = num_dense_layers
78
+ self.num_attention_heads = num_attention_heads
79
+ self.head_dim = head_dim
80
+ self.hidden_act = hidden_act
81
+ self.initializer_range = initializer_range
82
+ self.rms_norm_eps = rms_norm_eps
83
+ self.use_cache = use_cache
84
+ self.rope_theta = rope_theta
85
+ self.rope_scaling = rope_scaling
86
+
87
+
88
+ # MoE specific
89
+ self.moe_intermediate_size = moe_intermediate_size
90
+ self.num_experts_per_tok = num_experts_per_tok
91
+ self.n_group = n_group
92
+ self.topk_group = topk_group
93
+ self.num_experts = num_experts
94
+ self.num_shared_experts = num_shared_experts
95
+ self.num_expert_groups = num_expert_groups
96
+ self.num_limited_groups = num_limited_groups
97
+ self.score_func = score_func
98
+ self.route_norm = route_norm
99
+ self.route_scale = route_scale
100
+
101
+
102
+ # Attention specific
103
+ self.attention_dropout = attention_dropout
104
+ self.global_attn_every_n_layers = global_attn_every_n_layers
105
+ self.sliding_window = sliding_window
106
+ self.layer_types = layer_types
107
+ if self.layer_types is None:
108
+ self.layer_types = [
109
+ "sliding_attention" if bool((i + 1) % global_attn_every_n_layers) else "full_attention" for i in range(self.num_hidden_layers)
110
+ ]
111
+ layer_type_validation(self.layer_types)
112
+
113
+ # muP specific
114
+ self.mup_enabled = mup_enabled
115
+
116
+ if num_key_value_heads is None:
117
+ num_key_value_heads = num_attention_heads
118
+
119
+ self.num_key_value_heads = num_key_value_heads
120
+
121
+
122
+ # Validate rope configs
123
+ if self.rope_scaling is not None and "type" in self.rope_scaling:
124
+ self.rope_scaling["rope_type"] = self.rope_scaling["type"]
125
+ rope_config_validation(self)
126
+
127
+ super().__init__(
128
+ tie_word_embeddings=tie_word_embeddings,
129
+ **kwargs,
130
+ )
131
+
132
+
133
+ __all__ = ["AfmoeConfig"]
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