Gemma first implementation

Sana/models/sana_multi_scale.py

@@ -301,6 +301,8 @@ class SanaMS(Sana):
         """
         bs = x.shape[0]
         y = context
+        if len(y.shape) == 3:
+            y = y.unsqueeze(1)
         self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
         if self.use_pe:
             x = self.x_embedder(x)

text_encoders/pixart/tenc.py

@@ -1,10 +1,12 @@
+import os
+import torch
+
 from comfy import sd1_clip
 import comfy.text_encoders.t5
 import comfy.text_encoders.sd3_clip
 import comfy.model_management
+
 from transformers import T5TokenizerFast
-import torch
-import os
 
 class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
     def __init__(self, **kwargs):

text_encoders/sana/config.json

@@ -0,0 +1,35 @@
+{
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.4",
+  "use_cache": true,
+  "vocab_size": 256000
+}

text_encoders/sana/gemma.py

@@ -0,0 +1,379 @@
+# Copyright 2024 Google Inc. 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.
+import torch
+import torch.nn as nn
+import importlib
+
+def rotate_half(x):
+    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):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+def repeat_kv(hidden_states, n_rep):
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+def sdpa_attention_forward(config, query, key, value, mask=None, **kwargs):
+    key = repeat_kv(key, config["num_key_value_groups"])
+    value = repeat_kv(value, config["num_key_value_groups"])
+
+    causal_mask = mask
+    if mask is not None:
+        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+    # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+    # Reference: https://github.com/pytorch/pytorch/issues/112577.
+    if query.device.type == "cuda" and causal_mask is not None:
+        query = query.contiguous()
+        key = key.contiguous()
+        value = value.contiguous()
+
+    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+    is_causal = True if causal_mask is None and query.shape[1] > 1 else False
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query,
+        key,
+        value,
+        attn_mask=causal_mask,
+        dropout_p=0.0,
+        is_causal=is_causal,
+        scale=config["scaling"],
+    )
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    return attn_output, None
+
+def eager_attention_forward(config, query, key, value, mask, **kwargs):
+    key_states = repeat_kv(key, config["num_key_value_groups"])
+    value_states = repeat_kv(value, config["num_key_value_groups"])
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * config["scaling"]
+
+    if config["attn_logit_softcapping"] is not None:
+        attn_weights = attn_weights / config["attn_logit_softcapping"]
+        attn_weights = torch.tanh(attn_weights)
+        attn_weights = attn_weights * config["attn_logit_softcapping"]
+    if mask is not None:  # no matter the length, we just slice it
+        causal_mask = mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    #attn_weights = nn.functional.dropout(attn_weights, p=0, training=config.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    return attn_output, attn_weights
+
+# torch 2.0 can't pass scale arg to sdpa
+if int((torch.__version__).split(".")[1]) >= 1:
+    attention_forward = sdpa_attention_forward
+else:
+    attention_forward = eager_attention_forward
+
+class Gemma2RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float())
+        # Llama does x.to(float16) * w whilst Gemma2 is (x * w).to(float16)
+        # See https://github.com/huggingface/transformers/pull/29402
+        output = output * (1.0 + self.weight.float())
+        return output.type_as(x)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+class Gemma2MLP(nn.Module):
+    def __init__(self, config, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config["hidden_size"]
+        self.intermediate_size = config["intermediate_size"]
+        self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=False, dtype=dtype, device=device)
+        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=False, dtype=dtype, device=device)
+        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=False, dtype=dtype, device=device)
+        if config["hidden_activation"] != "gelu_pytorch_tanh":
+            raise NotImplementedError("Unknown act mode")
+        self.act_fn = torch.nn.GELU()
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+class Gemma2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
+        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        self.inv_freq.to(x.device)
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+class Gemma2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config, layer_idx=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.attention_dropout = config["attention_dropout"]
+        self.hidden_size = config["hidden_size"]
+        self.num_heads = config["num_attention_heads"]
+        self.head_dim = config["head_dim"]
+        self.num_key_value_heads = config["num_key_value_heads"]
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config["max_position_embeddings"]
+        self.rope_theta = config["rope_theta"]
+        self.is_causal = True
+        self.scaling = config["query_pre_attn_scalar"]**-0.5
+        self.sliding_window = config["sliding_window"] if not bool(layer_idx % 2) else None
+        self.attn_logit_softcapping = config["attn_logit_softcapping"]
+        if self.hidden_size % self.num_heads != 0:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = operations.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.k_proj = operations.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.v_proj = operations.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.o_proj = operations.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.rotary_emb = Gemma2RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position= None):
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "sliding_window": self.sliding_window,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        config = {
+            "scaling": self.scaling,
+            "num_key_value_groups": self.num_key_value_groups,
+            "max_position_embeddings": self.max_position_embeddings,
+            "attn_logit_softcapping": self.attn_logit_softcapping,
+        }
+        attn_output, attn_weights = attention_forward(config, query_states, key_states, value_states, attention_mask, output_attentions=output_attentions)
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+class Gemma2DecoderLayer(nn.Module):
+    def __init__(self, config, layer_idx, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.hidden_size = config["hidden_size"]
+        self.config = config
+        self.is_sliding = not bool(layer_idx % 2)
+
+        self.self_attn = Gemma2Attention(config=config, layer_idx=layer_idx, dtype=dtype, device=device, operations=operations)
+        self.mlp = Gemma2MLP(config, dtype=dtype, device=device, operations=operations)
+
+        self.input_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+        self.post_attention_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+
+        self.pre_feedforward_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+        self.post_feedforward_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+        self.sliding_window = config["sliding_window"]
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position=None):
+        if self.is_sliding and attention_mask is not None:  # efficient SDPA and no padding
+            # # Flash-attn is a 2D tensor
+            # if self.config["_attn_implementation == "flash_attention_2":
+            #     if past_key_value is not None:  # when decoding
+            #         attention_mask = attention_mask[:, -self.sliding_window :]
+            # else:
+                min_dtype = torch.finfo(hidden_states.dtype).min
+                sliding_window_mask = torch.tril(
+                    torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
+                )
+                attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
+                if attention_mask.shape[-1] <= 1:  # when decoding
+                    attention_mask = attention_mask[:, :, :, -self.sliding_window :]
+
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.pre_feedforward_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+def prepare_causal_mask(input_tensor, attention_mask):
+    dtype, device = input_tensor.dtype, input_tensor.device
+    batch_size=input_tensor.shape[0]
+    sequence_length = input_tensor.shape[1]
+    target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1]
+
+    min_dtype = torch.finfo(dtype).min
+    causal_mask = torch.full(
+        (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+    )
+    if sequence_length != 1:
+        causal_mask = torch.triu(causal_mask, diagonal=1)
+    #causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+    causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+    if attention_mask is not None:
+        causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+        mask_length = attention_mask.shape[-1]
+        padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+        padding_mask = padding_mask == 0
+        causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+            padding_mask, min_dtype
+        )
+
+    return causal_mask
+
+class Gemma2Model(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.padding_idx = 0
+        self.hidden_size = config_dict["hidden_size"]
+        self.embed_tokens = operations.Embedding(config_dict["vocab_size"], self.hidden_size, self.padding_idx, device=device, dtype=dtype)
+        self.num_layers = config_dict["num_hidden_layers"]
+        self.layers = nn.ModuleList(
+            [Gemma2DecoderLayer(config_dict, layer_idx, dtype=dtype, device=device, operations=operations) for layer_idx in range(config_dict["num_hidden_layers"])]
+        )
+        self.norm = Gemma2RMSNorm(self.hidden_size, eps=config_dict["rms_norm_eps"])
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+    
+    def forward(self, input_ids=None, attention_mask=None, position_ids=None, intermediate_output=None, final_layer_norm_intermediate=False, *args, **kwargs):
+        inputs_embeds = self.embed_tokens(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+        hidden_states = inputs_embeds
+        intermediate = None
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+
+        # normalized
+        # Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
+        # See https://github.com/huggingface/transformers/pull/29402
+        normalizer = torch.tensor(self.hidden_size**0.5, dtype=hidden_states.dtype)
+        hidden_states = hidden_states * normalizer
+
+        causal_mask = prepare_causal_mask(inputs_embeds, attention_mask)
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.layers) + intermediate_output
+
+        for i, decoder_layer in enumerate(self.layers):
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=None,
+                output_attentions=False,
+                use_cache=False,
+            )
+            if i == intermediate_output:
+                intermediate = hidden_states.clone()
+            hidden_states = layer_outputs[0]
+
+        hidden_states = self.norm(hidden_states)
+        if intermediate is not None and final_layer_norm_intermediate:
+            intermediate = self.norm(intermediate)
+
+        return hidden_states, intermediate

text_encoders/sana/tenc.py

@@ -0,0 +1,46 @@
+import os
+import torch
+
+from comfy import sd1_clip
+import comfy.model_management
+from .gemma import Gemma2Model
+
+from transformers import GemmaTokenizer as TFGemmaTokenizer
+
+class GemmaClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.json")
+        special_tokens = {"start": 2, "end": 1, "pad": 0}
+        super().__init__(device=device, layer="last", layer_idx=None, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, model_class=Gemma2Model, enable_attention_masks=True, return_attention_masks=False, model_options=model_options)
+
+class SanaClipModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="gemma", clip_model=GemmaClipModel, model_options=model_options)
+
+class GemmaTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
+            "tokenizers", "gemma_tokenizer",
+        )
+        # TODO: reenable proper logic here - needs comfy version 44db978 or higher
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=2304, embedding_key='gemma', tokenizer_class=TFGemmaTokenizer, has_start_token=False, pad_to_max_length=True, max_length=300, min_length=1)
+        self.start_token = 2
+        self.end_token = 1
+        self.pad_token = 0
+
+    def tokenize_with_weights(self, text, return_word_ids=False):
+        # TODO: see above, this is still just a wrapper for now
+        tokens = self.tokenizer(
+            text,
+            max_length=300,
+            padding="max_length", 
+            truncation=True,
+            return_tensors="pt"
+        )
+        batched_tokens = [(x.item(), 1.0) for x in tokens.input_ids[0]]
+        return [batched_tokens]
+
+class SanaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="gemma", tokenizer=GemmaTokenizer)

text_encoders/tenc.py

@@ -4,8 +4,10 @@ from enum import Enum
 import comfy.sd
 import comfy.utils
 import comfy.text_encoders
+import comfy.model_management
 
 from .pixart.tenc import pixart_te, PixArtTokenizer
+from .sana.tenc import SanaClipModel, SanaTokenizer
 
 class TencType(Enum):
     # offset in case we ever integrate w/ original
@@ -44,6 +46,8 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
     for i in range(len(clip_data)):
         if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
             clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+        elif "model.layers.25.post_feedforward_layernorm.weight" in clip_data[i]:
+            clip_data[i] = {k[len("model."):]:v for k,v in clip_data[i].items()}
         else:
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
@@ -54,6 +58,11 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
     if clip_type == TencType.PixArt:
         clip_target.clip = pixart_te(**comfy.sd.t5xxl_detect(clip_data))
         clip_target.tokenizer = PixArtTokenizer
+    elif clip_type == TencType.Sana:
+        clip_target.clip = SanaClipModel
+        clip_target.tokenizer = SanaTokenizer
+    else:
+        raise NotImplementedError(f"Unknown tenc: {clip_type}")
 
     parameters = 0
     tokenizer_data = {}

text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json

@@ -0,0 +1,34 @@
+{
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}