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Co-authored-by: mlabonne <mlabonne@users.noreply.huggingface.co>
Co-authored-by: fernandofernandes <fernandofernandes@users.noreply.huggingface.co>

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+ LFM Open License v1.0
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README.md ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ tags:
5
+ - liquid
6
+ - lfm2
7
+ - lfm2.5
8
+ - bidirectional
9
+ - masked-lm
10
+ - encoder
11
+ - diffusion-language-model
12
+ - masked-diffusion
13
+ - mdlm
14
+ - instruction-tuned
15
+ library_name: transformers
16
+ license: other
17
+ license_name: lfm1.0
18
+ license_link: LICENSE
19
+ pipeline_tag: text-generation
20
+ base_model:
21
+ - LiquidAI/LFM2.5-Encoder-350M
22
+ ---
23
+
24
+ <div align="center">
25
+ <img
26
+ src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
27
+ alt="Liquid AI"
28
+ style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
29
+ />
30
+ <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
31
+ <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
32
+ <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
33
+ <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
34
+ <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
35
+ </div>
36
+ </div>
37
+
38
+ # LFM2.5-Encoder-350M-Diffusion
39
+
40
+ A full fine-tune of [LFM2.5-Encoder-350M](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) as a masked-diffusion instruction model that generates text by iteratively unmasking tokens instead of decoding left to right.
41
+
42
+ The model was SFT-trained on [`mlabonne/open-perfectblend`](https://huggingface.co/datasets/mlabonne/open-perfectblend), a dataset of roughly 1.39M conversations, for 3 epochs.
43
+
44
+ Masked diffusion is a natural extension of masked-language modeling: the model starts from masked answer tokens, repeatedly predicts all masked positions, fills the most confident tokens, and continues until the answer is complete.
45
+
46
+ Find more details about our encoders in our [blog post](https://www.liquid.ai/blog/lfm2-5-encoders).
47
+
48
+ > [!NOTE]
49
+ > 💻 **Demos**: Try this fine-tuned model running in a CPU-only Hugging Face space:
50
+ > **[Masked-diffusion text generation](https://huggingface.co/spaces/LiquidAI/masked-diffusion)** — run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.
51
+
52
+ ## Usage
53
+
54
+ Install the required packages:
55
+
56
+ ```bash
57
+ pip install torch transformers
58
+ ```
59
+
60
+ Run masked-diffusion text generation:
61
+
62
+ ```python
63
+ import torch
64
+ from transformers import AutoModelForMaskedLM, AutoTokenizer
65
+
66
+ model_id = "LiquidAI/LFM2.5-Encoder-350M-Diffusion"
67
+
68
+ tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
69
+ model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True).eval()
70
+
71
+ messages = [{"role": "user", "content": "Give one short tip for writing clearer code."}]
72
+ prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
73
+ inputs = tokenizer(prompt, return_tensors="pt")
74
+
75
+ num_new_tokens = 12
76
+ mask_id = tokenizer.mask_token_id
77
+ input_ids = torch.cat(
78
+ [inputs.input_ids, torch.full((1, num_new_tokens), mask_id, dtype=torch.long)],
79
+ dim=1,
80
+ )
81
+ attention_mask = torch.ones_like(input_ids)
82
+
83
+ with torch.no_grad():
84
+ for _ in range(num_new_tokens):
85
+ mask_positions = (input_ids[0] == mask_id).nonzero(as_tuple=True)[0]
86
+ if len(mask_positions) == 0:
87
+ break
88
+
89
+ logits = model(input_ids=input_ids, attention_mask=attention_mask).logits[0, mask_positions]
90
+ logits[:, len(tokenizer):] = -torch.inf
91
+ for token_id in tokenizer.all_special_ids:
92
+ if token_id != tokenizer.eos_token_id:
93
+ logits[:, token_id] = -torch.inf
94
+
95
+ probs = logits.softmax(dim=-1)
96
+ confidence, token_ids = probs.max(dim=-1)
97
+ best = confidence.argmax()
98
+ input_ids[0, mask_positions[best]] = token_ids[best]
99
+
100
+ generated = input_ids[0, inputs.input_ids.shape[1]:]
101
+ text = tokenizer.decode(generated, skip_special_tokens=True).split("[/Answer]")[0]
102
+ print(text.strip())
103
+ ```
104
+
105
+ ## 📬 Contact
106
+
107
+ - Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
108
+ - If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
109
+
110
+ ## Citation
111
+
112
+ ```bibtex
113
+ @article{liquidAI2026Encoders,
114
+ author = {Liquid AI},
115
+ title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
116
+ journal = {Liquid AI Blog},
117
+ year = {2026},
118
+ note = {www.liquid.ai/blog/lfm2-5-encoders},
119
+ }
120
+ ```
chat_template.jinja ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {% if messages[0]['role'] == 'system' %}
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+ [SYS]
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+ {{ messages[0]['content'] | trim }}
4
+ [/SYS]
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+
6
+ {% set loop_messages = messages[1:] %}
7
+ {% else %}
8
+ {% set loop_messages = messages %}
9
+ {% endif -%}
10
+ {%- for message in loop_messages %}
11
+ {% if message['role'] == 'user' %}
12
+ [Question]
13
+ {{ message['content'] | trim }}
14
+ [/Question]
15
+
16
+ {% elif message['role'] == 'assistant' %}
17
+ [Answer]
18
+ {{ message['content'] | trim }}
19
+ [/Answer]
20
+
21
+ {% endif %}
22
+ {% endfor -%}
23
+ {%- if add_generation_prompt and (loop_messages | length == 0 or loop_messages[-1]['role'] != 'assistant') %}
24
+ [Answer]
25
+ {% endif %}
config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
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+ "Lfm2BidirectionalForMaskedLM"
4
+ ],
5
+ "auto_map": {
6
+ "AutoModel": "modeling_lfm2_bidirectional.Lfm2BidirectionalModel",
7
+ "AutoModelForMaskedLM": "modeling_lfm2_bidirectional.Lfm2BidirectionalForMaskedLM"
8
+ },
9
+ "block_auto_adjust_ff_dim": true,
10
+ "block_dim": 1024,
11
+ "block_ffn_dim_multiplier": 1.0,
12
+ "block_mlp_init_scale": 1.0,
13
+ "block_multiple_of": 256,
14
+ "block_norm_eps": 1e-05,
15
+ "block_out_init_scale": 1.0,
16
+ "block_use_swiglu": true,
17
+ "block_use_xavier_init": true,
18
+ "bos_token_id": 1,
19
+ "conv_L_cache": 3,
20
+ "conv_bias": false,
21
+ "conv_dim": 1024,
22
+ "conv_dim_out": 1024,
23
+ "conv_use_xavier_init": true,
24
+ "dtype": "float32",
25
+ "eos_token_id": 7,
26
+ "hidden_size": 1024,
27
+ "initializer_range": 0.02,
28
+ "intermediate_size": 6656,
29
+ "layer_types": [
30
+ "conv",
31
+ "conv",
32
+ "full_attention",
33
+ "conv",
34
+ "conv",
35
+ "full_attention",
36
+ "conv",
37
+ "conv",
38
+ "full_attention",
39
+ "conv",
40
+ "full_attention",
41
+ "conv",
42
+ "full_attention",
43
+ "conv",
44
+ "full_attention",
45
+ "conv"
46
+ ],
47
+ "max_position_embeddings": 128000,
48
+ "model_type": "lfm2",
49
+ "norm_eps": 1e-05,
50
+ "num_attention_heads": 16,
51
+ "num_heads": 16,
52
+ "num_hidden_layers": 16,
53
+ "num_key_value_heads": 8,
54
+ "pad_token_id": 0,
55
+ "rope_parameters": {
56
+ "rope_theta": 1000000.0,
57
+ "rope_type": "default"
58
+ },
59
+ "rope_theta": 1000000.0,
60
+ "transformers_version": "4.57.0",
61
+ "use_cache": false,
62
+ "use_pos_enc": true,
63
+ "vocab_size": 65536
64
+ }
model.safetensors ADDED
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+ size 1417952208
modeling_lfm2_bidirectional.py ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """LFM2 backbone with bidirectional attention + non-causal short-conv.
2
+
3
+ Wired into the HF repo via `auto_map` in config.json so that
4
+
5
+ AutoModel.from_pretrained(repo, trust_remote_code=True)
6
+ AutoModelForMaskedLM.from_pretrained(repo, trust_remote_code=True)
7
+
8
+ both return a model with the encoder-style patches already applied.
9
+
10
+ Supports `attn_implementation` in {"eager", "sdpa", "flash_attention_2"}:
11
+
12
+ eager/sdpa consume a 4D additive pad-only mask and reproduce the exact
13
+ training-time behavior; flash_attention_2 receives the 2D padding mask (or
14
+ None) and runs the kernel non-causally via `Lfm2Attention.is_causal = False`,
15
+ yielding outputs equivalent to the unpadded forward.
16
+ """
17
+
18
+ from typing import Optional
19
+
20
+ import torch
21
+ import torch.nn as nn
22
+ import torch.nn.functional as F
23
+ from transformers.configuration_utils import PretrainedConfig
24
+ from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
25
+ from transformers.modeling_utils import PreTrainedModel
26
+ from transformers.models.lfm2 import modeling_lfm2 as _lfm2_mod
27
+ from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
28
+ from transformers.models.lfm2.modeling_lfm2 import (
29
+ Lfm2Attention,
30
+ Lfm2Model,
31
+ Lfm2PreTrainedModel,
32
+ Lfm2ShortConv,
33
+ apply_mask_to_padding_states,
34
+ )
35
+
36
+
37
+ def _bidirectional_mask(
38
+ config,
39
+ input_embeds: torch.Tensor = None,
40
+ attention_mask: Optional[torch.Tensor] = None,
41
+ cache_position: Optional[torch.LongTensor] = None,
42
+ past_key_values=None,
43
+ position_ids: Optional[torch.LongTensor] = None,
44
+ **kwargs,
45
+ ) -> Optional[torch.Tensor]:
46
+ # transformers has renamed the embeds kwarg across versions
47
+ # (input_embeds <-> inputs_embeds); accept either to stay forward-compatible.
48
+ if input_embeds is None:
49
+ input_embeds = kwargs.get("inputs_embeds")
50
+
51
+ if config._attn_implementation == "flash_attention_2":
52
+ # FA2 only uses the 2D padding mask to unpad sequences; causality is
53
+ # controlled by `Lfm2Attention.is_causal` (set to False below).
54
+ if attention_mask is not None and not attention_mask.all():
55
+ return attention_mask
56
+ return None
57
+
58
+ device = input_embeds.device
59
+ dtype = input_embeds.dtype
60
+ bsz, q_len = input_embeds.shape[:2]
61
+ past = past_key_values.get_seq_length() if past_key_values is not None else 0
62
+ kv_len = past + q_len
63
+
64
+ mask = torch.zeros((bsz, 1, q_len, kv_len), device=device, dtype=dtype)
65
+ if attention_mask is not None:
66
+ cur_len = attention_mask.size(-1)
67
+ key_pad_flags = (attention_mask == 0).to(device=device, dtype=torch.float32)
68
+ pad_vec = torch.zeros((bsz, kv_len), device=device, dtype=torch.float32)
69
+ if cur_len > 0:
70
+ pad_vec[:, past:past + cur_len] = key_pad_flags * -1e9
71
+ mask = mask + pad_vec.to(dtype)[:, None, None, :]
72
+ return mask
73
+
74
+
75
+ def _noncausal_shortconv_forward(
76
+ self,
77
+ hidden_states: torch.Tensor,
78
+ past_key_values=None,
79
+ cache_position=None,
80
+ attention_mask: Optional[torch.Tensor] = None,
81
+ **kwargs,
82
+ ) -> torch.Tensor:
83
+ x = apply_mask_to_padding_states(hidden_states, attention_mask)
84
+
85
+ BCx = self.in_proj(x).transpose(-1, -2)
86
+ B, C, x = BCx.chunk(3, dim=-2)
87
+ Bx = B * x
88
+
89
+ k = self.conv.weight.shape[-1]
90
+ pad = k // 2
91
+ conv_out = F.conv1d(
92
+ Bx, weight=self.conv.weight, bias=self.conv.bias,
93
+ stride=1, padding=pad, dilation=1, groups=Bx.shape[1],
94
+ )
95
+ if conv_out.shape[-1] > Bx.shape[-1]:
96
+ conv_out = conv_out[..., :Bx.shape[-1]]
97
+ elif conv_out.shape[-1] < Bx.shape[-1]:
98
+ conv_out = F.pad(conv_out, (0, Bx.shape[-1] - conv_out.shape[-1]))
99
+
100
+ y = C * conv_out
101
+ y = y.transpose(-1, -2).contiguous()
102
+ return self.out_proj(y)
103
+
104
+
105
+ def _shortconv_forward(self, *args, **kwargs):
106
+ return self.slow_forward(*args, **kwargs)
107
+
108
+
109
+ _PATCHED = False
110
+
111
+
112
+ def _install_patches() -> None:
113
+ global _PATCHED
114
+ if _PATCHED:
115
+ return
116
+ _lfm2_mod.create_causal_mask = _bidirectional_mask
117
+ Lfm2ShortConv.slow_forward = _noncausal_shortconv_forward
118
+ Lfm2ShortConv.forward = _shortconv_forward
119
+ _PATCHED = True
120
+
121
+
122
+ _install_patches()
123
+
124
+
125
+ def _set_attention_noncausal(model) -> None:
126
+ for module in model.modules():
127
+ if isinstance(module, Lfm2Attention):
128
+ module.is_causal = False
129
+
130
+
131
+ class Lfm2BidirectionalModel(Lfm2Model):
132
+ """LFM2 patched for encoder-style use:
133
+ full bidirectional attention + non-causal short-conv."""
134
+
135
+ def __init__(self, config):
136
+ _install_patches()
137
+ super().__init__(config)
138
+ _set_attention_noncausal(self)
139
+
140
+
141
+ class Lfm2BidirectionalForMaskedLM(Lfm2PreTrainedModel):
142
+ """LFM2 bidirectional encoder with a tied masked-LM head."""
143
+
144
+ config_class = Lfm2Config
145
+ base_model_prefix = "lfm2"
146
+ _tied_weights_keys = {"lm_head.weight": "lfm2.embed_tokens.weight"}
147
+
148
+ def __init__(self, config: Lfm2Config):
149
+ _install_patches()
150
+ config = type(config).from_dict({**config.to_dict(), "use_cache": False})
151
+ super().__init__(config)
152
+ self.lfm2 = Lfm2BidirectionalModel(config)
153
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
154
+ self.post_init()
155
+ self.lm_head.weight = self.lfm2.embed_tokens.weight
156
+
157
+ def get_input_embeddings(self):
158
+ return self.lfm2.embed_tokens
159
+
160
+ def set_input_embeddings(self, value):
161
+ self.lfm2.embed_tokens = value
162
+
163
+ def get_output_embeddings(self):
164
+ return self.lm_head
165
+
166
+ def set_output_embeddings(self, new_embeddings):
167
+ self.lm_head = new_embeddings
168
+
169
+ def forward(
170
+ self,
171
+ input_ids: Optional[torch.LongTensor] = None,
172
+ attention_mask: Optional[torch.Tensor] = None,
173
+ position_ids: Optional[torch.LongTensor] = None,
174
+ inputs_embeds: Optional[torch.FloatTensor] = None,
175
+ labels: Optional[torch.LongTensor] = None,
176
+ output_hidden_states: Optional[bool] = None,
177
+ output_attentions: Optional[bool] = None,
178
+ return_dict: Optional[bool] = None,
179
+ **kwargs,
180
+ ) -> MaskedLMOutput:
181
+ return_dict = True if return_dict is None else return_dict
182
+ outputs = self.lfm2(
183
+ input_ids=input_ids,
184
+ attention_mask=attention_mask,
185
+ position_ids=position_ids,
186
+ inputs_embeds=inputs_embeds,
187
+ use_cache=False,
188
+ output_attentions=output_attentions,
189
+ output_hidden_states=output_hidden_states,
190
+ return_dict=True,
191
+ )
192
+ hidden = outputs.last_hidden_state
193
+ logits = self.lm_head(hidden)
194
+
195
+ loss = None
196
+ if labels is not None:
197
+ loss = F.cross_entropy(
198
+ logits.view(-1, self.config.vocab_size),
199
+ labels.view(-1),
200
+ ignore_index=-100,
201
+ )
202
+
203
+ if not return_dict:
204
+ out = (logits,) + outputs[1:]
205
+ return ((loss,) + out) if loss is not None else out
206
+ return MaskedLMOutput(
207
+ loss=loss,
208
+ logits=logits,
209
+ hidden_states=outputs.hidden_states,
210
+ attentions=outputs.attentions,
211
+ )
special_tokens_map.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|im_end|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "mask_token": {
17
+ "content": "<|mask|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "pad_token": {
24
+ "content": "<|pad|>",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ }
30
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,4085 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<|pad|>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<|startoftext|>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "<|endoftext|>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<|fim_pre|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "<|fim_mid|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "5": {
44
+ "content": "<|fim_suf|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "6": {
52
+ "content": "<|im_start|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "7": {
60
+ "content": "<|im_end|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "8": {
68
+ "content": "<|tool_list_start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ },
75
+ "9": {
76
+ "content": "<|tool_list_end|>",
77
+ "lstrip": false,
78
+ "normalized": false,
79
+ "rstrip": false,
80
+ "single_word": false,
81
+ "special": true
82
+ },
83
+ "10": {
84
+ "content": "<|tool_call_start|>",
85
+ "lstrip": false,
86
+ "normalized": false,
87
+ "rstrip": false,
88
+ "single_word": false,
89
+ "special": true
90
+ },
91
+ "11": {
92
+ "content": "<|tool_call_end|>",
93
+ "lstrip": false,
94
+ "normalized": false,
95
+ "rstrip": false,
96
+ "single_word": false,
97
+ "special": true
98
+ },
99
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4048
+ "single_word": false,
4049
+ "special": true
4050
+ },
4051
+ "64399": {
4052
+ "content": "<|file_end|>",
4053
+ "lstrip": false,
4054
+ "normalized": false,
4055
+ "rstrip": false,
4056
+ "single_word": false,
4057
+ "special": true
4058
+ },
4059
+ "64400": {
4060
+ "content": "<think>",
4061
+ "lstrip": false,
4062
+ "normalized": false,
4063
+ "rstrip": false,
4064
+ "single_word": false,
4065
+ "special": true
4066
+ },
4067
+ "64401": {
4068
+ "content": "</think>",
4069
+ "lstrip": false,
4070
+ "normalized": false,
4071
+ "rstrip": false,
4072
+ "single_word": false,
4073
+ "special": true
4074
+ }
4075
+ },
4076
+ "bos_token": "<|startoftext|>",
4077
+ "clean_up_tokenization_spaces": false,
4078
+ "eos_token": "<|im_end|>",
4079
+ "extra_special_tokens": {},
4080
+ "mask_token": "<|mask|>",
4081
+ "model_max_length": 1000000000000000019884624838656,
4082
+ "pad_token": "<|pad|>",
4083
+ "padding_side": "right",
4084
+ "tokenizer_class": "PreTrainedTokenizerFast"
4085
+ }