Upload model

config.json

@@ -1,13 +1,18 @@
 {
+  "architectures": [
+    "SpeechLLMModel"
+  ],
   "audio_enc_dim": 1024,
   "audio_encoder_name": "microsoft/wavlm-large",
   "audio_processor_name": "microsoft/wavlm-base",
   "auto_map": {
-    "AutoConfig": "config.SpeechLLMModelConfig"
+    "AutoConfig": "config.SpeechLLMModelConfig",
+    "AutoModel": "model.SpeechLLMModel"
   },
   "llm_dim": 2048,
   "llm_model_checkpoint": "hf_repo/llm_model_checkpoint",
   "llm_model_name": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
   "model_type": "custom_model",
+  "torch_dtype": "float32",
   "transformers_version": "4.41.2"
 }

model-00001-of-00002.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ca3b8867386b18c6aadb0499a682799dcbd76a573769d4bd28e3b3db0637d34c
-size 4972078744
+oid sha256:33955a9cf46521f3f86e5e7d3b9614e0383d84348cc639e3573576ce14f43dbf
+size 4993668624

model-00002-of-00002.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c276e96e5b2bf5bc6824b157179f3cb4304de0e674ffbb793cc2cb62b8dd1cd6
-size 836841304
+oid sha256:d7228df2837eb08cd7644f41c2fe4e3badf45a67ba3bd88e79616983c45337f4
+size 840535192

model.py

@@ -45,20 +45,20 @@ class SpeechLLMModel(PreTrainedModel):
         self.audio_encoder = TransformerAudioEnoder(config.audio_encoder_name)
         self.connector = CNNConnector(config.audio_enc_dim, config.llm_dim)
 
-        self.llm_model = AutoModelForCausalLM.from_pretrained(config.llm_model_checkpoint)
+        # self.llm_model = AutoModelForCausalLM.from_pretrained(config.llm_model_checkpoint)
+        # self.llm_tokenizer = AutoTokenizer.from_pretrained(config.llm_model_name)
+        
+        self.llm_model = AutoModelForCausalLM.from_pretrained(config.llm_model_name)
         self.llm_tokenizer = AutoTokenizer.from_pretrained(config.llm_model_name)
 
-        # self.llm_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-        # self.llm_tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-
-        # peft_config = LoraConfig(
-        #     r=8,
-        #     lora_alpha=16,
-        #     target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj'],
-        #     lora_dropout=0.05,
-        #     task_type="CAUSAL_LM",
-        # )
-        # self.llm_model = get_peft_model(self.llm_model, peft_config)
+        peft_config = LoraConfig(
+            r=8,
+            lora_alpha=16,
+            target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj'],
+            lora_dropout=0.05,
+            task_type="CAUSAL_LM",
+        )
+        self.llm_model = get_peft_model(self.llm_model, peft_config)
 
     def encode(self, mel, pre_tokenized_ids, post_tokenized_ids, output_tokenized_ids):
         batch_size = mel.shape[0]
@@ -67,7 +67,7 @@ class SpeechLLMModel(PreTrainedModel):
             speech_embeds = self.audio_encoder(mel)
             speech_embeds = self.connector(speech_embeds)
             
-        embedder = self.llm_model.model.embed_tokens
+        embedder = self.llm_model.model.model.embed_tokens
         pre_prompt_embeds = embedder(pre_tokenized_ids)
         post_prompt_embeds = embedder(post_tokenized_ids)
         output_prompt_embeds = embedder(output_tokenized_ids)

model.safetensors.index.json

@@ -1,6 +1,6 @@
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   "metadata": {
-    "total_size": 5808827136
+    "total_size": 5834058496
   },
   "weight_map": {
     "audio_encoder.encoder.encoder.layer_norm.bias": "model-00001-of-00002.safetensors",
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