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Clone_Small_Alpha64/PETER - PARAMETERS.txt

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+The parameters used to train this model, are encoded as follows:
+
+G-B1,DR0.2,ACC1,L2.5e-05,R32,A64,E20,TDFT,BF16T,GCLT,MG0.8,GCHF,D1.4,CE0.04,W0.03,cosine,R1 - THE KING
+
+
+
+Here's the actual python command to train it, we used this in Google Colab Notebook. 
+
+<CODE>
+# Begin the fine-tuning proces
+
+%cd /content/drive/MyDrive/VibeVoice-finetuning/
+
+# Define your parameters as Python variables
+batch_size = 1
+drop_rate = 0.2
+grad_accum = 1
+lr = 2.5e-5
+lora_r = 32
+lora_alpha = 64
+epochs = 20
+train_diff = True
+bf16 = True
+grad_clip = True
+max_grad = 0.8
+grad_checkpoint = False
+diff_weight = 1.4
+ce_weight = 0.04
+warmup = 0.03
+scheduler = "cosine"
+run_num = 2
+
+# Build the output directory dynamically
+output_dir =  (
+
+    f"Precise/G-B{batch_size},DR{drop_rate},ACC{grad_accum},"
+    f"L{lr},R{lora_r},A{lora_alpha},E{epochs},"
+    f"TDF{'T' if train_diff else 'F'},BF16{'T' if bf16 else 'F'},"
+    f"GCL{'T' if grad_clip else 'F'},MG{max_grad},"
+    f"GCH{'T' if grad_checkpoint else 'F'},D{diff_weight},"
+    f"CE{ce_weight},W{warmup},{scheduler},R{run_num}"
+
+              )
+
+# Now use the variables in your command
+!python -m src.finetune_vibevoice_lora \
+    --model_name_or_path vibevoice/VibeVoice-7B \
+    --processor_name_or_path src/vibevoice/processor \
+    --text_column_name text \
+    --audio_column_name audio \
+    --output_dir {output_dir} \
+    \
+    --train_jsonl GOLD_cortana_train_data.jsonl \
+    --per_device_train_batch_size {batch_size} \
+    --voice_prompt_drop_rate {drop_rate} \
+    --gradient_accumulation_steps {grad_accum} \
+    --learning_rate {lr} \
+    --lora_r {lora_r} \
+    --lora_alpha {lora_alpha} \
+    --num_train_epochs {epochs} \
+    --train_diffusion_head {train_diff} \
+    --bf16 {bf16} \
+    --gradient_clipping \
+    --max_grad_norm {max_grad} \
+    --gradient_checkpointing {grad_checkpoint} \
+    --diffusion_loss_weight {diff_weight} \
+    --ce_loss_weight {ce_weight} \
+    --warmup_ratio {warmup} \
+    --lr_scheduler_type {scheduler} \
+    \
+    --logging_steps 10 \
+    --save_steps 1528 \
+    \
+    --report_to wandb \
+    --remove_unused_columns False \
+    --do_train \
+    --ddpm_batch_mul 4 \
+    --lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
+
+</CODE>

Clone_Small_Alpha64/README.md

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+---
+base_model: ''
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- 'base_model:adapter:'
+- lora
+- transformers
+---
+
+# 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 0.17.1

Clone_Small_Alpha64/acoustic_connector/pytorch_model.bin

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Clone_Small_Alpha64/adapter_config.json

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+{
+  "alpha_pattern": {},
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+  "bias": "none",
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+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
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+  "target_modules": [
+    "o_proj",
+    "up_proj",
+    "v_proj",
+    "gate_proj",
+    "down_proj",
+    "q_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

Clone_Small_Alpha64/adapter_model.safetensors

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Clone_Small_Alpha64/diffusion_head/config.json

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+{
+  "architectures": [
+    "VibeVoiceDiffusionHead"
+  ],
+  "ddpm_batch_mul": 4,
+  "ddpm_beta_schedule": "cosine",
+  "ddpm_num_inference_steps": 20,
+  "ddpm_num_steps": 1000,
+  "diffusion_type": "ddpm",
+  "head_ffn_ratio": 3.0,
+  "head_layers": 4,
+  "hidden_size": 3584,
+  "latent_size": 64,
+  "model_type": "vibevoice_diffusion_head",
+  "prediction_type": "v_prediction",
+  "rms_norm_eps": 1e-05,
+  "speech_vae_dim": 64,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3"
+}

Clone_Small_Alpha64/diffusion_head/diffusion_head_full.bin

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Clone_Small_Alpha64/diffusion_head/model.safetensors

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Clone_Small_Alpha64/diffusion_head_full.bin

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Clone_Small_Alpha64/semantic_connector/pytorch_model.bin

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