Step-Audio (models: Step-Audio-Chat)

.gitattributes

@@ -52,3 +52,6 @@ models/Step-Audio-Tokenizer/dengcunqin/speech_paraformer-large_asr_nat-zh-canton
 models/Step-Audio-TTS-3B/lib/liboptimus_ths-torch2.2-cu121.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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models/Step-Audio-Chat/README.md

@@ -0,0 +1,180 @@
+---
+license: apache-2.0
+pipeline_tag: audio-text-to-text
+---
+
+# 1. Step-Audio-Chat
+
+This repository contains the Multimodal Large Language Model (LLM) component of Step-Audio. It is a 130 billion parameter multimodal LLM that is responsible for understanding and generating human speech. The model is specifically designed to seamlessly integrate functions such as speech recognition, semantic understanding, dialogue management, voice cloning, and speech generation.
+
+## 2. Evaluation
+### 2.1 LLM judge metrics(GPT-4o) on [**StepEval-Audio-360**](https://huggingface.co/datasets/stepfun-ai/StepEval-Audio-360)
+<table>
+    <caption>Comparison of fundamental capabilities of voice chat on the StepEval-Audio-360.</caption>
+    <thead>
+        <tr>
+            <th>Model</th>
+            <th style="text-align:center">Factuality (% &uarr;)</th>
+            <th style="text-align:center">Relevance (% &uarr;)</th>
+            <th style="text-align:center">Chat Score &uarr;</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>GLM4-Voice</td>
+            <td style="text-align:center">54.7</td>
+            <td style="text-align:center">66.4</td>
+            <td style="text-align:center">3.49</td>
+        </tr>
+        <tr>
+            <td>Qwen2-Audio</td>
+            <td style="text-align:center">22.6</td>
+            <td style="text-align:center">26.3</td>
+            <td style="text-align:center">2.27</td>
+        </tr>
+        <tr>
+            <td>Moshi<sup>*</sup></td>
+            <td style="text-align:center">1.0</td>
+            <td style="text-align:center">0</td>
+            <td style="text-align:center">1.49</td>
+        </tr>
+        <tr>
+            <td><strong>Step-Audio-Chat</strong></td>
+            <td style="text-align:center"><strong>66.4</strong></td>
+            <td style="text-align:center"><strong>75.2</strong></td>
+            <td style="text-align:center"><strong>4.11</strong></td>
+        </tr>
+    </tbody>
+</table>
+
+*Note: Moshi are marked with "\*" and should be considered for reference only.
+
+
+### 2.2 Public Test Set
+
+<table>
+    <thead>
+        <tr>
+            <th>Model</th>
+            <th style="text-align:center">Llama Question</th>
+            <th style="text-align:center">Web Questions</th>
+            <th style="text-align:center">TriviaQA*</th>
+            <th style="text-align:center">ComplexBench</th>
+            <th style="text-align:center">HSK-6</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>GLM4-Voice</td>
+            <td style="text-align:center">64.7</td>
+            <td style="text-align:center">32.2</td>
+            <td style="text-align:center">39.1</td>
+            <td style="text-align:center">66.0</td>
+            <td style="text-align:center">74.0</td>
+        </tr>
+        <tr>
+            <td>Moshi</td>
+            <td style="text-align:center">62.3</td>
+            <td style="text-align:center">26.6</td>
+            <td style="text-align:center">22.8</td>
+            <td style="text-align:center">-</td>
+            <td style="text-align:center">-</td>
+        </tr>
+        <tr>
+            <td>Freeze-Omni</td>
+            <td style="text-align:center">72.0</td>
+            <td style="text-align:center">44.7</td>
+            <td style="text-align:center">53.9</td>
+            <td style="text-align:center">-</td>
+            <td style="text-align:center">-</td>
+        </tr>
+        <tr>
+            <td>LUCY</td>
+            <td style="text-align:center">59.7</td>
+            <td style="text-align:center">29.3</td>
+            <td style="text-align:center">27.0</td>
+            <td style="text-align:center">-</td>
+            <td style="text-align:center">-</td>
+        </tr>
+        <tr>
+            <td>MinMo</td>
+            <td style="text-align:center">78.9</td>
+            <td style="text-align:center">55.0</td>
+            <td style="text-align:center">48.3</td>
+            <td style="text-align:center">-</td>
+            <td style="text-align:center">-</td>
+        </tr>
+        <tr>
+            <td>Qwen2-Audio</td>
+            <td style="text-align:center">52.0</td>
+            <td style="text-align:center">27.0</td>
+            <td style="text-align:center">37.3</td>
+            <td style="text-align:center">54.0</td>
+            <td style="text-align:center">-</td>
+        </tr>
+        <tr>
+            <td><strong>Step-Audio-Chat</strong></td>
+            <td style="text-align:center"><strong><i>81.0</i></strong></td>
+            <td style="text-align:center"><strong>75.1</strong></td>
+            <td style="text-align:center"><strong>58.0</strong></td>
+            <td style="text-align:center"><strong>74.0</strong></td>
+            <td style="text-align:center"><strong>86.0</strong></td>
+        </tr>
+    </tbody>
+</table>
+
+*Note: Results marked with "\*" on TriviaQA dataset are considered for reference only.*
+
+*TriviaQA dataset marked with "\*" indicates results are for reference only.*
+
+### 2.3 Audio instruction following
+<table>
+    <thead>
+        <tr>
+            <th rowspan="2">Category</th>
+            <th colspan="2" style="text-align:center">Instruction Following</th>
+            <th colspan="2" style="text-align:center">Audio Quality</th>
+        </tr>
+        <tr>
+            <th style="text-align:center">GLM-4-Voice</th>
+            <th style="text-align:center">Step-Audio</th>
+            <th style="text-align:center">GLM-4-Voice</th>
+            <th style="text-align:center">Step-Audio</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>Languages</td>
+            <td style="text-align:center">1.9</td>
+            <td style="text-align:center">3.8</td>
+            <td style="text-align:center">2.9</td>
+            <td style="text-align:center">3.3</td>
+        </tr>
+        <tr>
+            <td>Role-playing</td>
+            <td style="text-align:center">3.8</td>
+            <td style="text-align:center">4.2</td>
+            <td style="text-align:center">3.2</td>
+            <td style="text-align:center">3.6</td>
+        </tr>
+        <tr>
+            <td>Singing / RAP</td>
+            <td style="text-align:center">2.1</td>
+            <td style="text-align:center">2.4</td>
+            <td style="text-align:center">2.4</td>
+            <td style="text-align:center">4</td>
+        </tr>
+        <tr>
+            <td>Voice Control</td>
+            <td style="text-align:center">3.6</td>
+            <td style="text-align:center">4.4</td>
+            <td style="text-align:center">3.3</td>
+            <td style="text-align:center">4.1</td>
+        </tr>
+    </tbody>
+</table>
+
+
+## 3. More information
+
+For more information, please refer to our repository: [Step-Audio](https://github.com/stepfun-ai/Step-Audio).

models/Step-Audio-Chat/config.json

@@ -0,0 +1,22 @@
+{
+  "architectures": [
+    "Step1ForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_step1.Step1Config",
+    "AutoModelForCausalLM": "modeling_step1.Step1ForCausalLM"
+  },
+  "model_type": "step1",
+  "bos_token_id": 1,
+  "pad_token_id": 0,
+  "eos_token_id": 3,
+  "hidden_size": 12288,
+  "intermediate_size": 31232,
+  "num_attention_heads": 96,
+  "num_attention_groups": 8,
+  "num_hidden_layers": 88,
+  "max_seq_len": 32768,
+  "vocab_size": 74752,
+  "rms_norm_eps": 1e-05,
+  "torch_dtype": "bfloat16"
+}

models/Step-Audio-Chat/configuration_step1.py

@@ -0,0 +1,41 @@
+from typing import Optional, List, Any, Dict
+from transformers.configuration_utils import PretrainedConfig
+
+
+
+class Step1Config(PretrainedConfig):
+    model_type = "step1"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    
+    def __init__(
+        self,
+        hidden_size: int = 5120,
+        intermediate_size: int = 13312,
+        num_attention_heads: int = 40,
+        num_attention_groups: int = 8,
+        num_hidden_layers: int = 48,
+        max_seq_len: int = 4096,
+        vocab_size: int = 65536,
+        rms_norm_eps: float = 1e-5,
+        bos_token_id: int = 1,
+        eos_token_id: int = 3,
+        pad_token_id: int = 0,
+        **kwargs,
+    ) -> None:
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_attention_groups = num_attention_groups
+        self.num_hidden_layers = num_hidden_layers
+        self.max_seq_len = max_seq_len
+        self.vocab_size = vocab_size
+        self.rms_norm_eps = rms_norm_eps
+        super().__init__(
+            bos_token_id=bos_token_id,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs
+        )
+
+
+__all__ = ["Step1Config"]

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models/Step-Audio-Chat/modeling_step1.py

@@ -0,0 +1,414 @@
+import math
+from typing import Optional, Tuple, Union, List
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from transformers.generation import GenerationMixin
+
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from .configuration_step1 import Step1Config
+from transformers.cache_utils import Cache, DynamicCache
+from einops import rearrange
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+
+logger = logging.get_logger(__name__)
+
+
+def build_alibi_cache(block_size, n_heads, dtype, device):
+    # get slopes
+    n = 2 ** math.floor(math.log2(n_heads))  # nearest 2**n to n_heads
+    m0 = 2.0 ** (-8.0 / n)
+    # 2^(-8/n), 2^(-8*2/n), 2^(-8*3/n), ...
+    slopes = torch.pow(m0, torch.arange(1, n + 1))
+    if n < n_heads:
+        m1 = 2.0 ** (-4.0 / n)
+        # 2^(-8/(2n)), 2^(-8*3/(2n)), 2^(-8*5/(2n)), ...
+        mm = torch.pow(m1, torch.arange(1, 1 + 2 * (n_heads - n), 2))
+        slopes = torch.cat([slopes, mm])
+    slopes = slopes.to(device)
+
+    tril = torch.tril(torch.ones(1, 1, block_size, block_size, device=device))
+
+    bias_rows = torch.arange(block_size, device=device).view(1, -1)
+    bias_cols = torch.arange(block_size, device=device).view(-1, 1)
+    bias = -torch.sqrt(bias_cols - bias_rows)
+    bias = bias.view(1, block_size, block_size) * slopes.view(-1, 1, 1)
+    bias = bias.masked_fill(tril == 0, float("-inf"))
+
+    return bias.type(dtype)
+
+
+class StepRMSNorm(torch.nn.Module):
+    def __init__(self, hidden_size, eps=1e-5):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor):
+        var = x.float().pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(var + self.eps).to(x.dtype)
+        x = x * self.weight
+        return x
+
+
+class StepAttention(torch.nn.Module):
+    def __init__(self, hidden_size, num_heads, num_groups, layer_idx: int):
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.num_groups = num_groups
+        self.hidden_size = hidden_size
+        self.head_dim = hidden_size // num_heads
+
+        self.q_proj = torch.nn.Linear(hidden_size, hidden_size, bias=False)
+        self.k_proj = torch.nn.Linear(
+            hidden_size, num_groups * self.head_dim, bias=False
+        )
+        self.v_proj = torch.nn.Linear(
+            hidden_size, num_groups * self.head_dim, bias=False
+        )
+        self.o_proj = torch.nn.Linear(hidden_size, hidden_size, bias=False)
+
+        self.layer_idx = layer_idx
+
+    def flash_attn_func(self, q, k, v, dropout_p=0.0, softmax_scale=None, causal=True,
+                    return_attn_probs=False, tp_group_rank=0, tp_group_size=1):
+        softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale
+        return torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0]
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+
+        q: torch.Tensor = self.q_proj(x)
+        k: torch.Tensor = self.k_proj(x)
+        v: torch.Tensor = self.v_proj(x)
+        if past_key_value is not None:
+            cache_kwargs = {"cache_position": cache_position}
+            k, v = past_key_value.update(k, v, self.layer_idx, cache_kwargs)
+
+        q = rearrange(q, "b s (h d) -> b s h d", h=self.num_heads)
+        k = rearrange(k, "b s (g d) -> b s g d", g=self.num_groups)
+        v = rearrange(v, "b s (g d) -> b s g d", g=self.num_groups)
+
+        try:
+            if self.head_dim not in (64, 128):
+                raise ValueError("head_dim must be 64 or 128")
+            attn_output = self.flash_attn_func(q, k, v)
+            attn_output = attn_output.flatten(-2, -1)
+        except:
+            k = k.repeat_interleave(self.num_heads // self.num_groups, dim=-2)
+            v = v.repeat_interleave(self.num_heads // self.num_groups, dim=-2)
+
+            attention_mask = build_alibi_cache(
+                k.size(1), self.num_heads, dtype=q.dtype, device=q.device
+            )[:, :, -q.size(1) :, :].contiguous()
+
+            q = q.transpose(1, 2)
+            k = k.transpose(1, 2)
+            v = v.transpose(1, 2)
+
+            attn_output: torch.Tensor = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=attention_mask
+            )
+
+            attn_output = attn_output.transpose(1, 2).flatten(-2, -1)
+
+        out = self.o_proj(attn_output)
+        return out, None  # attn weights are not returned
+
+
+class StepMLP(torch.nn.Module):
+    def __init__(self, hidden_size, intermediate_size):
+        super().__init__()
+        self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.up_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
+
+    def forward(self, x):
+        gate = self.gate_proj(x)
+        up = self.up_proj(x)
+        x = torch.nn.functional.silu(gate) * up
+        x = self.down_proj(x)
+        return x
+
+
+class StepLayer(torch.nn.Module):
+    def __init__(self, config: Step1Config, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.self_attn = StepAttention(
+            hidden_size=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_groups=config.num_attention_groups,
+            layer_idx=layer_idx,
+        )
+        self.mlp = StepMLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+        )
+        self.input_layernorm = StepRMSNorm(
+            hidden_size=config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = StepRMSNorm(
+            hidden_size=config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, self_attn_weights = self.self_attn(hidden_states, past_key_value, attention_mask, cache_position)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states, )
+        if output_attentions:
+            outputs += (self_attn_weights,)
+        return outputs
+
+
+class StepPreTrainedModel(PreTrainedModel):
+    config_class = Step1Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["StepLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_cache_class = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class Step1Model(StepPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: Step1Config
+    """
+
+    def __init__(self, config: Step1Config):
+        super().__init__(config)
+        self.config = config
+        self.embed_tokens = torch.nn.Embedding(config.vocab_size, config.hidden_size)
+
+        self.layers = torch.nn.Sequential(
+            *[
+                StepLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+        self.norm = StepRMSNorm(
+            hidden_size=config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        causal_mask = attention_mask
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                past_key_value=past_key_values,
+                cache_position=cache_position,
+                output_attentions=output_attentions,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        output = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=None,
+        )
+        return output if return_dict else output.to_tuple()
+
+
+class Step1ForCausalLM(StepPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Step1Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

models/Step-Audio-Chat/source.txt

@@ -0,0 +1 @@
+https://huggingface.co/stepfun-ai/Step-Audio-Chat

models/Step-Audio-Chat/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25e122d9205d035033a9994c4d46a6a1b467a938654e4178fc0e5f4f5d610674
+size 1264044

models/Step-Audio-Chat/tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "add_bos_token": true,
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 65536,
+  "pad_token": "<unk>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|BOT|>system\nYou are a helpful assistant.<|EOT|>' }}{% endif %}{{'<|BOT|>' + (message['role'] if message['role'] != 'user' else 'human') + '\n' + message['content'] + '<|EOT|>'}}{% endfor %}{% if add_generation_prompt %}{{ '<|BOT|>assistant\n' }}{% endif %}"
+}
+