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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "SpeechUnitModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_speechunit.SpeechUnitConfig",
+    "AutoModelForCausalLM": "modeling_speechunit.SpeechUnitModel"
+  },
+  "base_model_id": "meta-llama/Llama-3.2-1B",
+  "initializer_range": 0.02,
+  "model_type": "speechunit",
+  "num_heads": 8,
+  "num_hidden_layers": 3,
+  "output_dim": 2048,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1"
+}

configuration_speechunit.py

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+from transformers import PretrainedConfig
+
+
+class SpeechUnitConfig(PretrainedConfig):
+    model_type = "speechunit"
+
+    def __init__(
+        self,
+        base_model_id: str = "meta-llama/Llama-3.2-1B",
+        num_hidden_layers: int = 3,
+        output_dim: int = 2048,
+        num_heads: int = 8,
+        initializer_range: float = 0.02,
+        **kwargs,
+    ):
+        self.base_model_id = base_model_id
+        self.num_hidden_layers = num_hidden_layers
+        self.output_dim = output_dim
+        self.num_heads = num_heads
+        self.initializer_range = initializer_range
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2adb59175cf4905c66e840ef50f60fca817e03f4e94ca7efde6f35afc164fc03
+size 2049176536

modeling_speechunit.py

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+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from transformers import LlamaConfig, LlamaModel, PreTrainedModel
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.llama.modeling_llama import KwargsForCausalLM
+from transformers.processing_utils import Unpack
+
+from configuration_speechunit import SpeechUnitConfig
+
+
+# Copied from transformer.models.llama.modeling_llama.LlamaPreTrainedModel class
+class SpeechUnitPreTrainedModel(PreTrainedModel):
+    config_class = SpeechUnitConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = 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_()
+        elif isinstance(module, SpeechUnitModel):
+            src_model = LlamaModel.from_pretrained(self.config.base_model_id)
+            with torch.no_grad():
+                for name, param in module.llama_model.named_parameters():
+                    param.copy_(src_model.state_dict()[name])
+
+class SpeechUnitModel(SpeechUnitPreTrainedModel):
+    def __init__(self, config: SpeechUnitConfig):
+        super(SpeechUnitModel, self).__init__(config)
+ 
+        # Initialize LLaMA model and load weights
+        llama_config = LlamaConfig.from_pretrained(config.base_model_id)
+        llama_config.num_hidden_layers = config.num_hidden_layers
+        self.llama_model = LlamaModel._from_config(llama_config)
+ 
+        # Embedding layers
+        original_vocab_size, embed_dim = self.llama_model.embed_tokens.weight.shape
+ 
+        # Audio embeddings (16400 = codebook size + 2 for BOS and EOS tokens)
+        self.audio_embed = nn.Embedding(16400, embed_dim)
+        nn.init.xavier_uniform_(self.audio_embed.weight.data)
+ 
+        # Learnable weights for token integration
+        self.token_weights = nn.Parameter(torch.ones(config.num_heads))
+ 
+        # Prediction heads
+        self.heads = nn.ModuleList([nn.Linear(embed_dim, config.output_dim) for _ in range(config.num_heads)])
+        
+        self.post_init()
+ 
+    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,
+        num_logits_to_keep: int = 0,
+        **kwargs: Unpack[KwargsForCausalLM],
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        # 參考 https://github.com/huggingface/transformers/blob/b05df6611e6e3e6834acca2b50baeb7cdd5fbe3c/src/transformers/models/llama/modeling_llama.py#L784
+        pass