Tokenizer update
Browse files- config.json +6 -1
- model.py +1 -0
config.json
CHANGED
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@@ -1,13 +1,18 @@
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{
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"audio_enc_dim": 1024,
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"audio_encoder_name": "microsoft/wavlm-large",
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"audio_processor_name": "microsoft/wavlm-base",
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"auto_map": {
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"AutoConfig": "config.SpeechLLMModelConfig"
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},
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"llm_dim": 2048,
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"llm_model_checkpoint": "hf_repo/llm_model_checkpoint",
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"llm_model_name": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"model_type": "custom_model",
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"transformers_version": "4.41.2"
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}
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{
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"architectures": [
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"SpeechLLMModel"
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],
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"audio_enc_dim": 1024,
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"audio_encoder_name": "microsoft/wavlm-large",
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"audio_processor_name": "microsoft/wavlm-base",
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"auto_map": {
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"AutoConfig": "config.SpeechLLMModelConfig",
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"AutoModel": "model.SpeechLLMModel"
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},
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"llm_dim": 2048,
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"llm_model_checkpoint": "hf_repo/llm_model_checkpoint",
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"llm_model_name": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"model_type": "custom_model",
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"torch_dtype": "float32",
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"transformers_version": "4.41.2"
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}
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model.py
CHANGED
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@@ -49,6 +49,7 @@ class SpeechLLMModel(PreTrainedModel):
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llm_config = AutoConfig.from_pretrained(config.llm_model_name)
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self.llm_model = AutoModelForCausalLM.from_config(llm_config)
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self.llm_tokenizer = AutoTokenizer.from_pretrained(config.llm_model_name)
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peft_config = LoraConfig(
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r=8,
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llm_config = AutoConfig.from_pretrained(config.llm_model_name)
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self.llm_model = AutoModelForCausalLM.from_config(llm_config)
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self.llm_tokenizer = AutoTokenizer.from_pretrained(config.llm_model_name)
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self.llm_tokenizer.pad_token = self.llm_tokenizer.eos_token
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peft_config = LoraConfig(
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r=8,
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