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Model: homebrewltd/mini-Ichigo-llama3.2-3B-s-instruct Source: Original Platform
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3B_full.yaml
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# Config for multi-device full finetuning in full_finetune_distributed.py
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# using a Llama3 8B Instruct model
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#
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# This config assumes that you've run the following command before launching
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# this run:
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# tune download meta-llama/Meta-Llama-3-8B-Instruct --output-dir /tmp/Meta-Llama-3-8B-Instruct --hf-token <HF_TOKEN>
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#
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# To launch on 4 devices, run the following command from root:
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# tune run --nproc_per_node 4 full_finetune_distributed --config llama3/8B_full
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#
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# You can add specific overrides through the command line. For example
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# to override the checkpointer directory while launching training
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# you can run:
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# tune run --nproc_per_node 4 full_finetune_distributed --config llama3/8B_full checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
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#
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# This config works best when the model is being fine-tuned on 2+ GPUs.
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# Single device full finetuning requires more memory optimizations. It's
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# best to use 8B_full_single_device.yaml for those cases
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# Tokenizer
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tokenizer:
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_component_: torchtune.models.llama3.llama3_s_tokenizer
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path: ../model_zoo_llama3.2/tokenizer.model
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max_seq_len: 4096
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# Dataset
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dataset:
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_component_: torchtune.datasets.chat_dataset
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source: homebrewltd/mixed-instruction-speech-whispervq-v3-full-phase2-3
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conversation_column: conversations
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conversation_style: openai
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split: train
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train_on_input: True
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seed: 42
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shuffle: False
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# Model Arguments
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model:
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_component_: torchtune.models.llama3_2.llama3_2_s_3b
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# path: model_zoo/Llama3.1_s_8b_init
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checkpointer:
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_component_: torchtune.training.FullModelHFCheckpointerSaveSteps
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checkpoint_dir: ../model_zoo_llama3.2/llama3.2-s-3b-base
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checkpoint_files: [
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model-00001-of-00002.safetensors,
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model-00002-of-00002.safetensors,
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]
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recipe_checkpoint: null
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output_dir: ../model_zoo_llama3.2/llama3.2-3B-s-instruct
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model_type: LLAMA3_2
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resume_from_checkpoint: False
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save_every_n_steps: 1000
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max_checkpoints: 3
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# Fine-tuning arguments
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batch_size: 3
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epochs: 1
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max_steps_per_epoch: null
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gradient_accumulation_steps: 12
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compile: False
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# Optimizer and Scheduler
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optimizer:
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_component_: torch.optim.AdamW #change this to use adam_mini: torchtune.modules.optimizer.Adam_mini
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weight_decay: 0.005
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lr: 7e-5
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fused: True
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lr_scheduler:
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_component_: torchtune.modules.get_cosine_schedule_with_warmup
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num_warmup_steps: 62
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loss:
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_component_: torch.nn.CrossEntropyLoss
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fsdp:
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cpu_offload: False
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# Training env
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device: cuda
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dtype: bf16
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# Memory management
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enable_activation_checkpointing: True
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memory_efficient_fsdp_wrap: True
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ac_mode: 'selective'
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# Logging
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metric_logger:
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_component_: torchtune.training.metric_logging.DiskLogger
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log_dir: ${output_dir}
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output_dir: ../model_zoo_llama3.2/llama3.2-3B-s-instruct-log/
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log_every_n_steps: 1
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log_peak_memory_stats: False
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README.md
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---
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datasets:
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- homebrewltd/instruction-speech-whispervq-v2
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language:
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- en
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license: apache-2.0
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tags:
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- sound language model
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pipeline_tag: audio-text-to-text
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---
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[](https://github.com/homebrewltd/ichigo/stargazers)
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## Model Details
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We have developed and released the family [Ichigo-llama3s](https://huggingface.co/collections/homebrew-research/llama3-s-669df2139f0576abc6eb7405). This family is natively understanding audio and text input.
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We expand the Semantic tokens experiment with WhisperVQ as a tokenizer for audio files from [homebrewltd/mini-Ichigo-llama3.2-3B-s-base](https://huggingface.co/homebrewltd/mini-Ichigo-llama3.2-3B-s-base) with nearly 1B tokens from [Instruction Speech WhisperVQ v3](homebrewltd/mixed-instruction-speech-whispervq-v3-full) dataset.
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**Model developers** Homebrew Research.
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**Input** Text and sound.
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**Output** Text.
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**Model Architecture** Llama-3.
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**Language(s):** English.
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## Intended Use
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**Intended Use Cases** This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities.
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**Out-of-scope** The use of llama3-s in any manner that violates applicable laws or regulations is strictly prohibited.
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## How to Get Started with the Model
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Try this model using [Google Colab Notebook](https://colab.research.google.com/drive/18IiwN0AzBZaox5o0iidXqWD1xKq11XbZ?usp=sharing).
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First, we need to convert the audio file to sound tokens
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```python
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if not os.path.exists("whisper-vq-stoks-medium-en+pl-fixed.model"):
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hf_hub_download(
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repo_id="jan-hq/WhisperVQ",
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filename="whisper-vq-stoks-medium-en+pl-fixed.model",
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local_dir=".",
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)
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vq_model = RQBottleneckTransformer.load_model(
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"whisper-vq-stoks-medium-en+pl-fixed.model"
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).to(device)
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vq_model.ensure_whisper(device)
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def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device=device):
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wav, sr = torchaudio.load(audio_path)
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if sr != 16000:
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wav = torchaudio.functional.resample(wav, sr, 16000)
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with torch.no_grad():
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codes = vq_model.encode_audio(wav.to(device))
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codes = codes[0].cpu().tolist()
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result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
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return f'<|sound_start|>{result}<|sound_end|>'
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```
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Then, we can inference the model the same as any other LLM.
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```python
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def setup_pipeline(model_path, use_4bit=False, use_8bit=False):
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model_kwargs = {"device_map": "auto"}
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if use_4bit:
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model_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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elif use_8bit:
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model_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_8bit=True,
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bnb_8bit_compute_dtype=torch.bfloat16,
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bnb_8bit_use_double_quant=True,
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)
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else:
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model_kwargs["torch_dtype"] = torch.bfloat16
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model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
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return pipeline("text-generation", model=model, tokenizer=tokenizer)
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def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False):
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generation_args = {
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"max_new_tokens": max_new_tokens,
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"return_full_text": False,
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"temperature": temperature,
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"do_sample": do_sample,
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}
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output = pipe(messages, **generation_args)
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return output[0]['generated_text']
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# Usage
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llm_path = "homebrewltd/llama3.1-s-instruct-v0.2"
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pipe = setup_pipeline(llm_path, use_8bit=True)
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```
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## Training process
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**Training Metrics Image**: Below is a snapshot of the training loss curve visualized.
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**[MMLU](https://huggingface.co/datasets/cais/mmlu)**:
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| Model | MMLU Score |
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| --- | --- |
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| llama3.1-instruct-8b | 69.40 |
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| ichigo-llama3.1-s-v0.3: phase 3 | 63.79 |
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| ichigo-llama3.1-s-v0.3: phase 2 | 63.08 |
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| ichigo-llama3.1-s-base-v0.3 | 42.11 |
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| mini-ichigo-llama3.2-3B-s-instruct | **58.60** |
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| mini-ichigo-llama3.2-3B-s-base | 59.61 |
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| llama3.1-s-instruct-v0.2 | 50.27 |
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**[AudioBench](https://arxiv.org/abs/2406.16020) Eval**:
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| Model Bench | [Open-hermes Instruction Audio](https://huggingface.co/datasets/AudioLLMs/openhermes_instruction_test) (GPT-4-O judge 0:5) | [Alpaca Instruction Audio](https://huggingface.co/datasets/AudioLLMs/alpaca_audio_test) (GPT-4-O judge 0:5) |
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| --- | --- | --- |
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| [Llama3.1-s-v2](https://huggingface.co/homebrewltd/llama3-s-instruct-v0.2) | 3.45 | 3.53 |
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| [Ichigo-llama3.1-s v0.3-phase2 -cp7000](https://huggingface.co/homebrewltd/Ichigo-llama3.1-s-instruct-v0.3-phase-2) | 3.42 | 3.62 |
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| [Ichigo-llama3.1-s v0.3-phase2-cplast](https://huggingface.co/jan-hq/llama3-s-instruct-v0.3-checkpoint-last) | 3.31 | 3.6 |
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| [Ichigo-llama3.1-s v0.3-phase3](https://huggingface.co/homebrewltd/Ichigo-llama3.1-s-instruct-v0.3-phase-3) | 3.64 | 3.68 |
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| [mini-Ichigo-llama3.2-3B-s-instruct](https://huggingface.co/homebrewltd/mini-Ichigo-llama3.2-3B-s-instruct) | **2.58** | **2.07** |
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| [Qwen2-audio-7B](https://huggingface.co/Qwen/Qwen2-Audio-7B) | 2.63 | 2.24 |
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### Hardware
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**GPU Configuration**: Cluster of 10x NVIDIA A6000-48GB.
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**GPU Usage**:
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- **Fine-tuning**: 12 hours.
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### Training Arguments
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We utilize [torchtune](https://github.com/pytorch/torchtune) library for the latest FSDP2 training code implementation.
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| Parameter | Instruction Fine-tuning |
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|----------------------------|-------------------------|
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| **Epoch** | 1 |
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| **Global batch size** | 360 |
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| **Learning Rate** | 7e-5 |
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| **Learning Scheduler** | LambdaLR with warmup |
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| **Optimizer** | Adam torch fused |
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| **Warmup Ratio** | 0.01 |
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| **Weight Decay** | 0.005 |
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| **Max Sequence Length** | 4096 |
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## Examples
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1. Good example:
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<details>
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<summary>Click to toggle Example 1</summary>
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```
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```
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</details>
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<details>
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<summary>Click to toggle Example 2</summary>
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```
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```
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</details>
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2. Misunderstanding example:
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||||||
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<details>
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<summary>Click to toggle Example 3</summary>
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```
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```
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</details>
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3. Off-tracked example:
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||||||
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||||||
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<details>
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||||||
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<summary>Click to toggle Example 4</summary>
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```
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||||||
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```
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</details>
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## Citation Information
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||||||
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||||||
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**BibTeX:**
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||||||
|
|
||||||
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```
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@article{Llama3-S: Sound Instruction Language Model 2024,
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title={Llama3-S},
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|
author={Homebrew Research},
|
||||||
|
year=2024,
|
||||||
|
month=August},
|
||||||
|
url={https://huggingface.co/homebrewltd/llama3.1-s-2024-08-20}
|
||||||
|
```
|
||||||
|
|
||||||
|
## Acknowledgement
|
||||||
|
|
||||||
|
- **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)**
|
||||||
|
|
||||||
|
- **[Meta-Llama-3.1-8B-Instruct ](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**
|
||||||
40
config.json
Normal file
40
config.json
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
{
|
||||||
|
"_name_or_path": "Ichigo-llama3.2-3B-s-instruct/",
|
||||||
|
"architectures": [
|
||||||
|
"LlamaForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 128000,
|
||||||
|
"eos_token_id": [
|
||||||
|
128001,
|
||||||
|
128008,
|
||||||
|
128009
|
||||||
|
],
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 3072,
|
||||||
|
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|
||||||
|
"intermediate_size": 8192,
|
||||||
|
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|
||||||
|
"mlp_bias": false,
|
||||||
|
"model_type": "llama",
|
||||||
|
"num_attention_heads": 24,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pretraining_tp": 1,
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_scaling": {
|
||||||
|
"factor": 32.0,
|
||||||
|
"high_freq_factor": 4.0,
|
||||||
|
"low_freq_factor": 1.0,
|
||||||
|
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|
||||||
|
"rope_type": "llama3"
|
||||||
|
},
|
||||||
|
"rope_theta": 500000.0,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.45.0",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 128771
|
||||||
|
}
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "audio-text-to-text", "allow_remote": true}
|
||||||
10
generation_config.json
Normal file
10
generation_config.json
Normal file
@@ -0,0 +1,10 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 128000,
|
||||||
|
"eos_token_id": [
|
||||||
|
128001,
|
||||||
|
128008,
|
||||||
|
128009
|
||||||
|
],
|
||||||
|
"transformers_version": "4.45.0"
|
||||||
|
}
|
||||||
5509
loss_log_intruct.txt
Normal file
5509
loss_log_intruct.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
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3
model-00001-of-00002.safetensors
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|
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|
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261
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261
model.safetensors.index.json
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|
||||||
|
}
|
||||||
|
}
|
||||||
20
special_tokens_map.json
Normal file
20
special_tokens_map.json
Normal file
@@ -0,0 +1,20 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|sound_start|>",
|
||||||
|
"<|sound_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|begin_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|eot_id|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:d1cd254b76ef1f79a37b7c5d40fa0818cf6ae047e3b990877724b37c378ddaff
|
||||||
|
size 17308285
|
||||||
6186
tokenizer_config.json
Normal file
6186
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user