second time

.gitattributes

@@ -0,0 +1,7 @@
+examples/1.wav filter=lfs diff=lfs merge=lfs -text
+model/adapter_model.safetensors filter=lfs diff=lfs merge=lfs -text
+model/optimizer.pt filter=lfs diff=lfs merge=lfs -text
+model/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+model2/adapter_model.safetensors filter=lfs diff=lfs merge=lfs -text
+model2/optimizer.pt filter=lfs diff=lfs merge=lfs -text
+model2/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: AudSemThinker
+emoji: 🐨
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 5.20.1
+app_file: app.py
+pinned: false
+---

app.py

@@ -0,0 +1,254 @@
+import spaces
+import gradio as gr
+import torch
+import subprocess
+import sys
+from transformers import AutoProcessor, Qwen2_5OmniThinkerForConditionalGeneration, TextIteratorStreamer
+import torchaudio
+from threading import Thread
+from qwen_omni_utils import process_mm_info
+from transformers import StoppingCriteria, StoppingCriteriaList
+
+print(f"PyTorch version: {torch.__version__}")
+print(f"CUDA available: {torch.cuda.is_available()}")
+print(f"CUDA version: {torch.version.cuda}")
+
+# Check environment
+result = subprocess.run([sys.executable, '-c', 
+    'import torch; print(f"PyTorch: {torch.__version__}"); print(f"CUDA: {torch.version.cuda}")'], 
+    capture_output=True, text=True)
+print(result.stdout)
+
+# Model paths and configuration
+model_path_1 = "./model"
+model_path_2 = "./model2"
+base_model_id = "Qwen/Qwen2.5-Omni-7B"
+
+# Dictionary to store loaded models and processors
+loaded_models = {}
+
+# Load the model and processor
+def load_model(model_path):
+    # Check if model is already loaded
+    if model_path in loaded_models:
+        return loaded_models[model_path]
+    
+    # Load the processor from the base model
+    processor = AutoProcessor.from_pretrained(
+        base_model_id,
+        trust_remote_code=True,
+    )
+    
+    # Load the model
+    model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        attn_implementation="flash_attention_2",
+        trust_remote_code=True,
+        device_map="auto",
+    )
+    
+    model.eval()
+    
+    # Store in cache
+    loaded_models[model_path] = (model, processor)
+    
+    return model, processor
+
+# Initialize first model and processor
+model, processor = load_model(model_path_1)
+
+
+def process_output(output):
+    if "<think>" in output:
+        rest = output.split("<think>")[1]
+        output = "<think>\n" + rest
+    elif "<semantic_elements>" in output:
+        rest = output.split("<semantic_elements>")[1]
+        output = "<semantic_elements>\n" + rest
+    elif "<answer>" in output:
+        rest = output.split("<answer>")[1]
+        output = "<answer>\n" + rest
+    elif "</think>" in output:
+        rest = output.split("</think>")[0]
+        output = rest + "\n</think>\n\n"
+    elif "</semantic_elements>" in output:
+        rest = output.split("</semantic_elements>")[0]
+        output = rest + "\n</semantic_elements>\n\n"
+    elif "</answer>" in output:
+        rest = output.split("</answer>")[0]
+        output = rest + "\n</answer>\n"
+    output = output.replace("\\n", "\n")
+    output = output.replace("\\", "\n")
+    output = output.replace("\n-", "-")
+    return output
+
+# Custom Stopping Criteria
+class StopOnSpecificToken(StoppingCriteria):
+    def __init__(self, stop_token_sequences: list[list[int]], device: str = "cuda"):
+        super().__init__()
+        self.stop_sequence_tensors = []
+        for seq in stop_token_sequences:
+            if seq:  # Only process non-empty sequences
+                self.stop_sequence_tensors.append(torch.tensor(seq, dtype=torch.long, device=device))
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        for stop_seq_tensor in self.stop_sequence_tensors:
+            current_sequence_length = input_ids.shape[-1]
+            stop_sequence_length = stop_seq_tensor.shape[-1]
+
+            # stop_sequence_length should be > 0 due to the check in __init__
+            if stop_sequence_length == 0: # Should ideally not be reached if seq was non-empty
+                continue
+
+            if current_sequence_length >= stop_sequence_length:
+                # Check the last tokens of the last sequence in the batch
+                last_tokens = input_ids[0, -stop_sequence_length:]
+                if torch.equal(last_tokens, stop_seq_tensor):
+                    return True
+        return False
+
+# Keep only the process_audio_streaming function that's actually used in the Gradio interface
+@spaces.GPU
+def process_audio_streaming(audio_file, model_choice, question="Describe this audio in detail"):
+    # Load the selected model
+    model_path = model_path_2 if model_choice == "Think" else model_path_1
+    model, processor = load_model(model_path)
+    
+    # Load and process the audio with torchaudio
+    waveform, sr = torchaudio.load(audio_file)
+    
+    # Resample to 16kHz if needed
+    if sr != processor.feature_extractor.sampling_rate:
+        waveform = torchaudio.functional.resample(waveform, sr, processor.feature_extractor.sampling_rate)
+        sr = processor.feature_extractor.sampling_rate
+    
+    # Convert to mono if stereo
+    if waveform.shape[0] > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    
+    # Get the audio data as numpy array
+    y = waveform.squeeze().numpy()
+            
+    # Set sampling rate for the processor
+    sampling_rate = processor.feature_extractor.sampling_rate
+    
+    # Define prompts based on model choice
+    prompt_think_semantics = f"You are given a question and an audio clip. Your task is to answer the question based on the audio clip. First, think about the question and the audio clip and put your thoughts in <think> and </think> tags. Then reason about the semantic elements involved in the audio clip and put your reasoning in <semantic_elements> and </semantic_elements> tags. Then answer the question based on the audio clip, put your answer in <answer> and </answer> tags. {question}"
+
+    instruction_text = ""
+    if model_choice == "Think + Semantics":
+        instruction_text = prompt_think_semantics
+    else: # Default to the question if no specific model processing is chosen.
+        instruction_text = question
+    
+    # Create conversation format
+    conversation = [
+        {"role": "system", "content": [
+            {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}]},
+        {"role": "user", "content": [
+            {"type": "audio", "audio": y},
+            {"type": "text", "text": instruction_text}
+        ]}
+    ]
+    
+    # Format the chat
+    chat_text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+    
+    # Process multimedia info using qwen_omni_utils
+    audios, images, videos = process_mm_info(conversation, use_audio_in_video=False)
+    
+    # Process the inputs
+    inputs = processor(
+        text=chat_text,
+        audio=audios,
+        images=images, 
+        videos=videos,
+        return_tensors="pt",
+        sampling_rate=sampling_rate,
+    ).to(model.device)
+    
+    # Create a standard streamer instance
+    streamer = TextIteratorStreamer(
+        processor.tokenizer, 
+        timeout=10., 
+        skip_prompt=True, 
+        skip_special_tokens=True
+    )
+    
+    # Initialize variables for buffering
+    accumulated_output = ""
+    buffer = ""
+    stop_sequence = "</answer>"
+    stop_found = False
+
+    answer_token_ids = processor.tokenizer.encode("</answer>", add_special_tokens=False)
+    processed_stop_token_ids = [answer_token_ids]
+
+    # Get the device of the model to ensure tensors are on the same device
+    model_device = next(model.parameters()).device
+    custom_stopping_criteria = StopOnSpecificToken(stop_token_sequences=processed_stop_token_ids, device=model_device.type)
+    stopping_criteria = StoppingCriteriaList([custom_stopping_criteria])
+
+    # Generate the output with streaming
+    with torch.no_grad():
+        generate_kwargs = dict(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=768,
+            do_sample=False,
+            stopping_criteria=stopping_criteria
+        )
+        t = Thread(target=model.generate, kwargs=generate_kwargs)
+        t.start()
+
+        # Process the stream with buffering
+        for output in streamer:
+            if stop_found:
+                break
+                
+            output = process_output(output)
+            buffer += output
+            
+            # Check if stop sequence is in the buffer
+            if stop_sequence in buffer:
+                # Output everything up to and including the stop sequence
+                before_stop = buffer.split(stop_sequence)[0]
+                accumulated_output += before_stop + stop_sequence
+                yield accumulated_output
+                stop_found = True
+                break
+            else:
+                # Check if we can safely output part of the buffer
+                # Keep the last N characters where N is the length of the stop sequence
+                if len(buffer) > len(stop_sequence):
+                    # Output all but the last len(stop_sequence) characters
+                    safe_output = buffer[:-len(stop_sequence)]
+                    buffer = buffer[-len(stop_sequence):]
+                    accumulated_output += safe_output
+                    yield accumulated_output
+        
+        # Output any remaining buffer if no stop sequence was found
+        if not stop_found and buffer:
+            accumulated_output += buffer
+            yield accumulated_output
+
+# Create Gradio interface for audio processing
+audio_demo = gr.Interface(
+    fn=process_audio_streaming,
+    inputs=[
+        gr.Audio(type="filepath", label="Upload Audio"),
+        gr.Radio(["Think", "Think + Semantics"], label="Select Model", value="Think + Semantics"),
+        gr.Textbox(label="Question", value="Describe this audio in detail")
+    ],
+    outputs=gr.Textbox(label="Generated Output", lines=30),
+    title="AudSemThinker",
+    description="Upload an audio file and the model will provide detailed analysis and description. Choose between different model versions.",
+    examples=[["examples/1.wav", "Think + Semantics", "Describe this audio in detail"]],
+    cache_examples=False,
+    live=True
+)
+
+# Launch the apps
+if __name__ == "__main__":
+    audio_demo.launch()

examples/1.wav

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+oid sha256:9cdddcf346c154468e0f846f48bd5ab493c75ffaed90990f909b80dfd4bde801
+size 430974

model/README.md

@@ -0,0 +1,201 @@
+---
+library_name: peft
+---
+
+# 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. -->
+
+
+
+- **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]
+### Framework versions
+
+- PEFT 0.15.0

model/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "Qwen2_5OmniThinkerForConditionalGeneration",
+    "parent_library": "transformers.models.qwen2_5_omni.modeling_qwen2_5_omni"
+  },
+  "base_model_name_or_path": "Qwen/Qwen2.5-Omni-7B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

model/adapter_model.safetensors

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model/added_tokens.json

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+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|AUDIO|>": 151646,
+  "<|IMAGE|>": 151655,
+  "<|VIDEO|>": 151656,
+  "<|audio_bos|>": 151647,
+  "<|audio_eos|>": 151648,
+  "<|box_end|>": 151649,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|vision_bos|>": 151652,
+  "<|vision_eos|>": 151653,
+  "<|vision_pad|>": 151654
+}

model/chat_template.jinja

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+{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_bos|><|IMAGE|><|vision_eos|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<|audio_bos|><|AUDIO|><|audio_eos|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_bos|><|VIDEO|><|vision_eos|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

model/optimizer.pt

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model/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|AUDIO|>",
+    "<|audio_bos|>",
+    "<|audio_eos|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_bos|>",
+    "<|vision_eos|>",
+    "<|vision_pad|>",
+    "<|IMAGE|>",
+    "<|VIDEO|>"
+  ],
+  "audio_bos_token": "<|audio_bos|>",
+  "audio_eos_token": "<|audio_eos|>",
+  "audio_token": "<|AUDIO|>",
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<|IMAGE|>",
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "video_token": "<|VIDEO|>",
+  "vision_bos_token": "<|vision_bos|>",
+  "vision_eos_token": "<|vision_eos|>"
+}

model/tokenizer.json

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+size 11421870

model/tokenizer_config.json

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+  "add_prefix_space": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|audio_bos|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
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+  },
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+}

model2/README.md

@@ -0,0 +1,201 @@
+---
+library_name: peft
+---
+
+# 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. -->
+
+
+
+- **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]
+### Framework versions
+
+- PEFT 0.15.0

model2/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "Qwen2_5OmniThinkerForConditionalGeneration",
+    "parent_library": "transformers.models.qwen2_5_omni.modeling_qwen2_5_omni"
+  },
+  "base_model_name_or_path": "Qwen/Qwen2.5-Omni-7B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

model2/adapter_model.safetensors

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+size 15367872

model2/added_tokens.json

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+}

model2/chat_template.jinja

@@ -0,0 +1,7 @@
+{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_bos|><|IMAGE|><|vision_eos|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<|audio_bos|><|AUDIO|><|audio_eos|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_bos|><|VIDEO|><|vision_eos|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

model2/optimizer.pt

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+size 30872250

model2/special_tokens_map.json

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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "video_token": "<|VIDEO|>",
+  "vision_bos_token": "<|vision_bos|>",
+  "vision_eos_token": "<|vision_eos|>"
+}

model2/tokenizer.json

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+oid sha256:8441917e39ae0244e06d704b95b3124795cec478e297f9afac39ba670d7e9d99
+size 11421870

model2/tokenizer_config.json

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+    "<|IMAGE|>",
+    "<|VIDEO|>"
+  ],
+  "audio_bos_token": "<|audio_bos|>",
+  "audio_eos_token": "<|audio_eos|>",
+  "audio_token": "<|AUDIO|>",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {
+    "audio_bos_token": "<|audio_bos|>",
+    "audio_eos_token": "<|audio_eos|>",
+    "audio_token": "<|AUDIO|>",
+    "image_token": "<|IMAGE|>",
+    "video_token": "<|VIDEO|>",
+    "vision_bos_token": "<|vision_bos|>",
+    "vision_eos_token": "<|vision_eos|>"
+  },
+  "image_token": "<|IMAGE|>",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen2_5OmniProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|VIDEO|>",
+  "vision_bos_token": "<|vision_bos|>",
+  "vision_eos_token": "<|vision_eos|>"
+}

requirements.txt

@@ -0,0 +1,15 @@
+gradio
+pydantic==2.10.6
+torch==2.6.0 --extra-index-url https://download.pytorch.org/whl/cu126
+torchvision==0.21.0 --extra-index-url https://download.pytorch.org/whl/cu126
+torchaudio==2.6.0 --extra-index-url https://download.pytorch.org/whl/cu126
+transformers
+peft
+numpy
+matplotlib
+soundfile
+librosa
+torchaudio
+qwen_omni_utils
+torchvision
+https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl